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4. ULTRASTRUCTURAL FEATURES OF THE DIGESTIVE SYSTEM OF THE ADULT HELMINTH Heterakis gallinarum SCHRANK, 1788 (NEMATODA: HETERAKIDAE).

Author

Rzayev, F. H., Nasirov, A. M., Isayev, O. R., and Gasimov, E. K.

Abstract

Species of the Heterakidae family are similar in appearance. These signs cause difficulties in determining the systematic position of the species. For this purpose, the digestive organs of adult H. gallinarum nematode were studied at the ultrastructural levels by light and electron microscopic methods. The obtained results were compared with the structures of other species of the family. The digestive organs of adult H. gallinarum nematode are divided into 3 parts - stomodeum, intestine and proctodeum. The esophagus in turn is divided into procorpus, metacorpus and bulb. Its wall consists of basement membrane, muscle cells and cuticle. In the bulb, the muscle cells are large and relatively large in number. The intestine is divided into ventricular, middle and prerectal areas. It consists of a thick basement membrane, a single-layered epithelium with microvillas. Epithelial cells are divided into 5 areas that differ from each other according to their structure. The smooth membranes of the epithelial cells create a barrier connection with each other. Sphincter cells are noted at the end of the intestine. After that part, the rectum was found in females and the cloaca in males nematodes. The lumen of both is covered with cuticle. The ultrastructural features of the digestive organs of the nematode H. gallinarum are consistent with the main systematic features of the family. [ABSTRACT FROM AUTHOR]

Published
2024
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9. Roadmap on Machine learning in electronic structure

Author

Kulik, H J, primary, Hammerschmidt, T, additional, Schmidt, J, additional, Botti, S, additional, Marques, M A L, additional, Boley, M, additional, Scheffler, M, additional, Todorović, M, additional, Rinke, P, additional, Oses, C, additional, Smolyanyuk, A, additional, Curtarolo, S, additional, Tkatchenko, A, additional, Bartók, A P, additional, Manzhos, S, additional, Ihara, M, additional, Carrington, T, additional, Behler, J, additional, Isayev, O, additional, Veit, M, additional, Grisafi, A, additional, Nigam, J, additional, Ceriotti, M, additional, Schütt, K T, additional, Westermayr, J, additional, Gastegger, M, additional, Maurer, R J, additional, Kalita, B, additional, Burke, K, additional, Nagai, R, additional, Akashi, R, additional, Sugino, O, additional, Hermann, J, additional, Noé, F, additional, Pilati, S, additional, Draxl, C, additional, Kuban, M, additional, Rigamonti, S, additional, Scheidgen, M, additional, Esters, M, additional, Hicks, D, additional, Toher, C, additional, Balachandran, P V, additional, Tamblyn, I, additional, Whitelam, S, additional, Bellinger, C, additional, and Ghiringhelli, L M, additional

Published
2022
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10. Roadmap on Machine learning in electronic structure

Author

Kulik, H J, Hammerschmidt, T, Schmidt, J, Botti, S, Marques, M A L, Boley, M, Scheffler, M, Todorović, M, Rinke, P, Oses, C, Smolyanyuk, A, Curtarolo, S, Tkatchenko, A, Bartók, A P, Manzhos, S, Ihara, M, Carrington, T, Behler, J, Isayev, O, Veit, M, Grisafi, A, Nigam, J, Ceriotti, M, Schütt, K T, Westermayr, J, Gastegger, M, Maurer, R J, Kalita, B, Burke, K, Nagai, R, Akashi, R, Sugino, O, Hermann, J, Noé, F, Pilati, S, Draxl, C, Kuban, Martin, Rigamonti, Santiago, Scheidgen, Markus, Esters, M, Hicks, D, Toher, C, Balachandran, P V, Tamblyn, I, Whitelam, S, Bellinger, C, Ghiringhelli, Luca M., Kulik, H J, Hammerschmidt, T, Schmidt, J, Botti, S, Marques, M A L, Boley, M, Scheffler, M, Todorović, M, Rinke, P, Oses, C, Smolyanyuk, A, Curtarolo, S, Tkatchenko, A, Bartók, A P, Manzhos, S, Ihara, M, Carrington, T, Behler, J, Isayev, O, Veit, M, Grisafi, A, Nigam, J, Ceriotti, M, Schütt, K T, Westermayr, J, Gastegger, M, Maurer, R J, Kalita, B, Burke, K, Nagai, R, Akashi, R, Sugino, O, Hermann, J, Noé, F, Pilati, S, Draxl, C, Kuban, Martin, Rigamonti, Santiago, Scheidgen, Markus, Esters, M, Hicks, D, Toher, C, Balachandran, P V, Tamblyn, I, Whitelam, S, Bellinger, C, and Ghiringhelli, Luca M.

Abstract

In recent years, we have been witnessing a paradigm shift in computational materials science. In fact, traditional methods, mostly developed in the second half of the XXth century, are being complemented, extended, and sometimes even completely replaced by faster, simpler, and often more accurate approaches. The new approaches, that we collectively label by machine learning, have their origins in the fields of informatics and artificial intelligence, but are making rapid inroads in all other branches of science. With this in mind, this Roadmap article, consisting of multiple contributions from experts across the field, discusses the use of machine learning in materials science, and share perspectives on current and future challenges in problems as diverse as the prediction of materials properties, the construction of force-fields, the development of exchange correlation functionals for density-functional theory, the solution of the many-body problem, and more. In spite of the already numerous and exciting success stories, we are just at the beginning of a long path that will reshape materials science for the many challenges of the XXIth century., Peer Reviewed

Published
2022

11. Community assessment to advance computational prediction of cancer drug combinations in a pharmacogenomic screen

Author

Menden M, Wang D, Mason M, Szalai B, Bulusu K, Guan Y, Yu T, Kang J, Jeon M, Wolfinger R, Nguyen T, Zaslavskiy M, Jang I, Ghazoui Z, Ahsen M, Vogel R, Neto E, Norman T, Tang E, Garnett M, Di Veroli G, Fawell S, Stolovitzky G, Guinney J, Dry J, Saez-Rodriguez J, Abante J, Abecassis B, Aben N, Aghamirzaie D, Aittokallio T, Akhtari F, Al-lazikani B, Alam T, Allam A, Allen C, de Almeida M, Altarawy D, Alves V, Amadoz A, Anchang B, Antolin A, Ash J, Aznar V, Ba-alawi W, Bagheri M, Bajic V, Ball G, Ballester P, Baptista D, Bare C, Bateson M, Bender A, Bertrand D, Wijayawardena B, Boroevich K, Bosdriesz E, Bougouffa S, Bounova G, Brouwer T, Bryant B, Calaza M, Calderone A, Calza S, Capuzzi S, Carbonell-Caballero J, Carlin D, Carter H, Castagnoli L, Celebi R, Cesareni G, Chang H, Chen G, Chen H, Cheng L, Chernomoretz A, Chicco D, Cho K, Cho S, Choi D, Choi J, Choi K, Choi M, De Cock M, Coker E, Cortes-Ciriano I, Cserzo M, Cubuk C, Curtis C, Van Daele D, Dang C, Dijkstra T, Dopazo J, Draghici S, Drosou A, Dumontier M, Ehrhart F, Eid F, ElHefnawi M, Elmarakeby H, van Engelen B, Engin H, de Esch I, Evelo C, Falcao A, Farag S, Fernandez-Lozano C, Fisch K, Flobak A, Fornari C, Foroushani A, Fotso D, Fourches D, Friend S, Frigessi A, Gao F, Gao X, Gerold J, Gestraud P, Ghosh S, Gillberg J, Godoy-Lorite A, Godynyuk L, Godzik A, Goldenberg A, Gomez-Cabrero D, Gonen M, de Graaf C, Gray H, Grechkin M, Guimera R, Guney E, Haibe-Kains B, Han Y, Hase T, He D, He L, Heath L, Hellton K, Helmer-Citterich M, Hidalgo M, Hidru D, Hill S, Hochreiter S, Hong S, Hovig E, Hsueh Y, Hu Z, Huang J, Huang R, Hunyady L, Hwang J, Hwang T, Hwang W, Hwang Y, Isayev O, Walk O, Jack J, Jahandideh S, Ji J, Jo Y, Kamola P, Kanev G, Karacosta L, Karimi M, Kaski S, Kazanov M, Khamis A, Khan S, Kiani N, Kim A, Kim J, Kim K, Kim S, Kim Y, Kirk P, Kitano H, Klambauer G, Knowles D, Ko M, Kohn-Luque A, Kooistra A, Kuenemann M, Kuiper M, Kurz C, Kwon M, van Laarhoven T, Laegreid A, Lederer S, Lee H, Lee J, Lee Y, Leppaho E, Lewis R, Li J, Li L, Liley J, Lim W, Lin C, Liu Y, Lopez Y, Low J, Lysenko A, Machado D, Madhukar N, De Maeyer D, Malpartida A, Mamitsuka H, Marabita F, Marchal K, Marttinen P, Mason D, Mazaheri A, Mehmood A, Mehreen A, Michaut M, Miller R, Mitsopoulos C, Modos D, Van Moerbeke M, Moo K, Motsinger-Reif A, Movva R, Muraru S, Muratov E, Mushthofa M, Nagarajan N, Nakken S, Nath A, Neuvial P, Newton R, Ning Z, De Niz C, Oliva B, Olsen C, Palmeri A, Panesar B, Papadopoulos S, Park J, Park S, Pawitan Y, Peluso D, Pendyala S, Peng J, Perfetto L, Pirro S, Plevritis S, Politi R, Poon H, Porta E, Prellner I, Preuer K, Pujana M, Ramnarine R, Reid J, Reyal F, Richardson S, Ricketts C, Rieswijk L, Rocha M, Rodriguez-Gonzalvez C, Roell K, Rotroff D, de Ruiter J, Rukawa P, Sadacca B, Safikhani Z, Safitri F, Sales-Pardo M, Sauer S, Schlichting M, Seoane J, Serra J, Shang M, Sharma A, Sharma H, Shen Y, Shiga M, Shin M, Shkedy Z, Shopsowitz K, Sinai S, Skola D, Smirnov P, Soerensen I, Soerensen P, Song J, Song S, Soufan O, Spitzmueller A, Steipe B, Suphavilai C, Tamayo S, Tamborero D, Tang J, Tanoli Z, Tarres-Deulofeu M, Tegner J, Thommesen L, Tonekaboni S, Tran H, De Troyer E, Truong A, Tsunoda T, Turu G, Tzeng G, Verbeke L, Videla S, Vis D, Voronkov A, Votis K, Wang A, Wang H, Wang P, Wang S, Wang W, Wang X, Wennerberg K, Wernisch L, Wessels L, van Westen G, Westerman B, White S, Willighagen E, Wurdinger T, Xie L, Xie S, Xu H, Yadav B, Yau C, Yeerna H, Yin J, Yu M, Yun S, Zakharov A, Zamichos A, Zanin M, Zeng L, Zenil H, Zhang F, Zhang P, Zhang W, Zhao H, Zhao L, Zheng W, Zoufir A, Zucknick M, AstraZeneca-Sanger Drug Combinatio, Ege Üniversitesi, Gönen, Mehmet (ORCID 0000-0002-2483-075X & YÖK ID 237468), Menden, Michael P., Wang, Dennis, Mason, Mike J., Szalai, Bence, Bulusu, Krishna C., Guan, Yuanfang, Yu, Thomas, Kang, Jaewoo, Jeon, Minji, Wolfinger, Russ, Nguyen, Tin, Zaslavskiy, Mikhail, Jang, In Sock, Ghazoui, Zara, Ahsen, Mehmet Eren, Vogel, Robert, Neto, Elias Chaibub, Norman, Thea, Tang, Eric K. Y., Garnett, Mathew J., Di Veroli, Giovanni Y., Fawell, Stephen, Stolovitzky, Gustavo, Guinney, Justin, Dry, Jonathan R., Saez-Rodriguez, Julio, Abante, Jordi, Abecassis, Barbara Schmitz, Aben, Nanne, Aghamirzaie, Delasa, Aittokallio, Tero, Akhtari, Farida S., Al-lazikani, Bissan, Alam, Tanvir, Allam, Amin, Allen, Chad, de Almeida, Mariana Pelicano, Altarawy, Doaa, Alves, Vinicius, Amadoz, Alicia, Anchang, Benedict, Antolin, Albert A., Ash, Jeremy R., Romeo Aznar, Victoria, Ba-alawi, Wail, Bagheri, Moeen, Bajic, Vladimir, Ball, Gordon, Ballester, Pedro J., Baptista, Delora, Bare, Christopher, Bateson, Mathilde, Bender, Andreas, Bertrand, Denis, Wijayawardena, Bhagya, Boroevich, Keith A., Bosdriesz, Evert, Bougouffa, Salim, Bounova, Gergana, Brouwer, Thomas, Bryant, Barbara, Calaza, Manuel, Calderone, Alberto, Calza, Stefano, Capuzzi, Stephen, Carbonell-Caballero, Jose, Carlin, Daniel, Carter, Hannah, Castagnoli, Luisa, Celebi, Remzi, Cesareni, Gianni, Chang, Hyeokyoon, Chen, Guocai, Chen, Haoran, Chen, Huiyuan, Cheng, Lijun, Chernomoretz, Ariel, Chicco, Davide, Cho, Kwang-Hyun, Cho, Sunghwan, Choi, Daeseon, Choi, Jaejoon, Choi, Kwanghun, Choi, Minsoo, De Cock, Martine, Coker, Elizabeth, Cortes-Ciriano, Isidro, Cserzo, Miklos, Cubuk, Cankut, Curtis, Christina, Van Daele, Dries, Dang, Cuong C., Dijkstra, Tjeerd, Dopazo, Joaquin, Draghici, Sorin, Drosou, Anastasios, Dumontier, Michel, Ehrhart, Friederike, Eid, Fatma-Elzahraa, ElHefnawi, Mahmoud, Elmarakeby, Haitham, van Engelen, Bo, Engin, Hatice Billur, de Esch, Iwan, Evelo, Chris, Falcao, Andre O., Farag, Sherif, Fernandez-Lozano, Carlos, Fisch, Kathleen, Flobak, Asmund, Fornari, Chiara, Foroushani, Amir B. K., Fotso, Donatien Chedom, Fourches, Denis, Friend, Stephen, Frigessi, Arnoldo, Gao, Feng, Gao, Xiaoting, Gerold, Jeffrey M., Gestraud, Pierre, Ghosh, Samik, Gillberg, Jussi, Godoy-Lorite, Antonia, Godynyuk, Lizzy, Godzik, Adam, Goldenberg, Anna, Gomez-Cabrero, David, de Graaf, Chris, Gray, Harry, Grechkin, Maxim, Guimera, Roger, Guney, Emre, Haibe-Kains, Benjamin, Han, Younghyun, Hase, Takeshi, He, Di, He, Liye, Heath, Lenwood S., Hellton, Kristoffer H., Helmer-Citterich, Manuela, Hidalgo, Marta R., Hidru, Daniel, Hill, Steven M., Hochreiter, Sepp, Hong, Seungpyo, Hovig, Eivind, Hsueh, Ya-Chih, Hu, Zhiyuan, Huang, Justin K., Huang, R. Stephanie, Hunyady, Laszlo, Hwang, Jinseub, Hwang, Tae Hyun, Hwang, Woochang, Hwang, Yongdeuk, Isayev, Olexandr, Walk, Oliver Bear Don't, Jack, John, Jahandideh, Samad, Ji, Jiadong, Jo, Yousang, Kamola, Piotr J., Kanev, Georgi K., Karacosta, Loukia, Karimi, Mostafa, Kaski, Samuel, Kazanov, Marat, Khamis, Abdullah M., Khan, Suleiman Ali, Kiani, Narsis A., Kim, Allen, Kim, Jinhan, Kim, Juntae, Kim, Kiseong, Kim, Kyung, Kim, Sunkyu, Kim, Yongsoo, Kim, Yunseong, Kirk, Paul D. W., Kitano, Hiroaki, Klambauer, Gunter, Knowles, David, Ko, Melissa, Kohn-Luque, Alvaro, Kooistra, Albert J., Kuenemann, Melaine A., Kuiper, Martin, Kurz, Christoph, Kwon, Mijin, van Laarhoven, Twan, Laegreid, Astrid, Lederer, Simone, Lee, Heewon, Lee, Jeon, Lee, Yun Woo, Leppaho, Eemeli, Lewis, Richard, Li, Jing, Li, Lang, Liley, James, Lim, Weng Khong, Lin, Chieh, Liu, Yiyi, Lopez, Yosvany, Low, Joshua, Lysenko, Artem, Machado, Daniel, Madhukar, Neel, De Maeyer, Dries, Malpartida, Ana Belen, Mamitsuka, Hiroshi, Marabita, Francesco, Marchal, Kathleen, Marttinen, Pekka, Mason, Daniel, Mazaheri, Alireza, Mehmood, Arfa, Mehreen, Ali, Michaut, Magali, Miller, Ryan A., Mitsopoulos, Costas, Modos, Dezso, Van Moerbeke, Marijke, Moo, Keagan, Motsinger-Reif, Alison, Movva, Rajiv, Muraru, Sebastian, Muratov, Eugene, Mushthofa, Mushthofa, Nagarajan, Niranjan, Nakken, Sigve, Nath, Aritro, Neuvial, Pierre, Newton, Richard, Ning, Zheng, De Niz, Carlos, Oliva, Baldo, Olsen, Catharina, Palmeri, Antonio, Panesar, Bhawan, Papadopoulos, Stavros, Park, Jaesub, Park, Seonyeong, Park, Sungjoon, Pawitan, Yudi, Peluso, Daniele, Pendyala, Sriram, Peng, Jian, Perfetto, Livia, Pirro, Stefano, Plevritis, Sylvia, Politi, Regina, Poon, Hoifung, Porta, Eduard, Prellner, Isak, Preuer, Kristina, Angel Pujana, Miguel, Ramnarine, Ricardo, Reid, John E., Reyal, Fabien, Richardson, Sylvia, Ricketts, Camir, Rieswijk, Linda, Rocha, Miguel, Rodriguez-Gonzalvez, Carmen, Roell, Kyle, Rotroff, Daniel, de Ruiter, Julian R., Rukawa, Ploy, Sadacca, Benjamin, Safikhani, Zhaleh, Safitri, Fita, Sales-Pardo, Marta, Sauer, Sebastian, Schlichting, Moritz, Seoane, Jose A., Serra, Jordi, Shang, Ming-Mei, Sharma, Alok, Sharma, Hari, Shen, Yang, Shiga, Motoki, Shin, Moonshik, Shkedy, Ziv, Shopsowitz, Kevin, Sinai, Sam, Skola, Dylan, Smirnov, Petr, Soerensen, Izel Fourie, Soerensen, Peter, Song, Je-Hoon, Song, Sang Ok, Soufan, Othman, Spitzmueller, Andreas, Steipe, Boris, Suphavilai, Chayaporn, Tamayo, Sergio Pulido, Tamborero, David, Tang, Jing, Tanoli, Zia-ur-Rehman, Tarres-Deulofeu, Marc, Tegner, Jesper, Thommesen, Liv, Tonekaboni, Seyed Ali Madani, Tran, Hong, De Troyer, Ewoud, Truong, Amy, Tsunoda, Tatsuhiko, Turu, Gabor, Tzeng, Guang-Yo, Verbeke, Lieven, Videla, Santiago, Vis, Daniel, Voronkov, Andrey, Votis, Konstantinos, Wang, Ashley, Wang, Hong-Qiang Horace, Wang, Po-Wei, Wang, Sheng, Wang, Wei, Wang, Xiaochen, Wang, Xin, Wennerberg, Krister, Wernisch, Lorenz, Wessels, Lodewyk, van Westen, Gerard J. P., Westerman, Bart A., White, Simon Richard, Willighagen, Egon, Wurdinger, Tom, Xie, Lei, Xie, Shuilian, Xu, Hua, Yadav, Bhagwan, Yau, Christopher, Yeerna, Huwate, Yin, Jia Wei, Yu, Michael, Yu, MinHwan, Yun, So Jeong, Zakharov, Alexey, Zamichos, Alexandros, Zanin, Massimiliano, Zeng, Li, Zenil, Hector, Zhang, Frederick, Zhang, Pengyue, Zhang, Wei, Zhao, Hongyu, Zhao, Lan, Zheng, Wenjin, Zoufir, Azedine, Zucknick, Manuela, College of Engineering, Department of Industrial Engineering, Institute of Data Science, RS: FSE DACS IDS, Bioinformatica, RS: NUTRIM - R1 - Obesity, diabetes and cardiovascular health, RS: FHML MaCSBio, Promovendi NTM, Tero Aittokallio / Principal Investigator, Bioinformatics, Institute for Molecular Medicine Finland, Hu, Z, Fotso, DC, Menden, M, Wang, D, Mason, M, Szalai, B, Bulusu, K, Guan, Y, Yu, T, Kang, J, Jeon, M, Wolfinger, R, Nguyen, T, Zaslavskiy, M, Abante, J, Abecassis, B, Aben, N, Aghamirzaie, D, Aittokallio, T, Akhtari, F, Al-lazikani, B, Alam, T, Allam, A, Allen, C, de Almeida, M, Altarawy, D, Alves, V, Amadoz, A, Anchang, B, Antolin, A, Ash, J, Aznar, V, Ba-alawi, W, Bagheri, M, Bajic, V, Ball, G, Ballester, P, Baptista, D, Bare, C, Bateson, M, Bender, A, Bertrand, D, Wijayawardena, B, Boroevich, K, Bosdriesz, E, Bougouffa, S, Bounova, G, Brouwer, T, Bryant, B, Calaza, M, Calderone, A, Calza, S, Capuzzi, S, Carbonell-Caballero, J, Carlin, D, Carter, H, Castagnoli, L, Celebi, R, Cesareni, G, Chang, H, Chen, G, Chen, H, Cheng, L, Chernomoretz, A, Chicco, D, Cho, K, Cho, S, Choi, D, Choi, J, Choi, K, Choi, M, Cock, M, Coker, E, Cortes-Ciriano, I, Cserzo, M, Cubuk, C, Curtis, C, Daele, D, Dang, C, Dijkstra, T, Dopazo, J, Draghici, S, Drosou, A, Dumontier, M, Ehrhart, F, Eid, F, Elhefnawi, M, Elmarakeby, H, van Engelen, B, Engin, H, de Esch, I, Evelo, C, Falcao, A, Farag, S, Fernandez-Lozano, C, Fisch, K, Flobak, A, Fornari, C, Foroushani, A, Fotso, D, Fourches, D, Friend, S, Frigessi, A, Gao, F, Gao, X, Gerold, J, Gestraud, P, Ghosh, S, Gillberg, J, Godoy-Lorite, A, Godynyuk, L, Godzik, A, Goldenberg, A, Gomez-Cabrero, D, Gonen, M, de Graaf, C, Gray, H, Grechkin, M, Guimera, R, Guney, E, Haibe-Kains, B, Han, Y, Hase, T, He, D, He, L, Heath, L, Hellton, K, Helmer-Citterich, M, Hidalgo, M, Hidru, D, Hill, S, Hochreiter, S, Hong, S, Hovig, E, Hsueh, Y, Huang, J, Huang, R, Hunyady, L, Hwang, J, Hwang, T, Hwang, W, Hwang, Y, Isayev, O, Don't Walk, O, Jack, J, Jahandideh, S, Ji, J, Jo, Y, Kamola, P, Kanev, G, Karacosta, L, Karimi, M, Kaski, S, Kazanov, M, Khamis, A, Khan, S, Kiani, N, Kim, A, Kim, J, Kim, K, Kim, S, Kim, Y, Kirk, P, Kitano, H, Klambauer, G, Knowles, D, Ko, M, Kohn-Luque, A, Kooistra, A, Kuenemann, M, Kuiper, M, Kurz, C, Kwon, M, van Laarhoven, T, Laegreid, A, Lederer, S, Lee, H, Lee, J, Lee, Y, Lepp_aho, E, Lewis, R, Li, J, Li, L, Liley, J, Lim, W, Lin, C, Liu, Y, Lopez, Y, Low, J, Lysenko, A, Machado, D, Madhukar, N, Maeyer, D, Malpartida, A, Mamitsuka, H, Marabita, F, Marchal, K, Marttinen, P, Mason, D, Mazaheri, A, Mehmood, A, Mehreen, A, Michaut, M, Miller, R, Mitsopoulos, C, Modos, D, Moerbeke, M, Moo, K, Motsinger-Reif, A, Movva, R, Muraru, S, Muratov, E, Mushthofa, M, Nagarajan, N, Nakken, S, Nath, A, Neuvial, P, Newton, R, Ning, Z, Niz, C, Oliva, B, Olsen, C, Palmeri, A, Panesar, B, Papadopoulos, S, Park, J, Park, S, Pawitan, Y, Peluso, D, Pendyala, S, Peng, J, Perfetto, L, Pirro, S, Plevritis, S, Politi, R, Poon, H, Porta, E, Prellner, I, Preuer, K, Pujana, M, Ramnarine, R, Reid, J, Reyal, F, Richardson, S, Ricketts, C, Rieswijk, L, Rocha, M, Rodriguez-Gonzalvez, C, Roell, K, Rotroff, D, de Ruiter, J, Rukawa, P, Sadacca, B, Safikhani, Z, Safitri, F, Sales-Pardo, M, Sauer, S, Schlichting, M, Seoane, J, Serra, J, Shang, M, Sharma, A, Sharma, H, Shen, Y, Shiga, M, Shin, M, Shkedy, Z, Shopsowitz, K, Sinai, S, Skola, D, Smirnov, P, Soerensen, I, Soerensen, P, Song, J, Song, S, Soufan, O, Spitzmueller, A, Steipe, B, Suphavilai, C, Tamayo, S, Tamborero, D, Tang, J, Tanoli, Z, Tarres-Deulofeu, M, Tegner, J, Thommesen, L, Tonekaboni, S, Tran, H, Troyer, E, Truong, A, Tsunoda, T, Turu, G, Tzeng, G, Verbeke, L, Videla, S, Vis, D, Voronkov, A, Votis, K, Wang, A, Wang, H, Wang, P, Wang, S, Wang, W, Wang, X, Wennerberg, K, Wernisch, L, Wessels, L, van Westen, G, Westerman, B, White, S, Willighagen, E, Wurdinger, T, Xie, L, Xie, S, Xu, H, Yadav, B, Yau, C, Yeerna, H, Yin, J, Yu, M, Yun, S, Zakharov, A, Zamichos, A, Zanin, M, Zeng, L, Zenil, H, Zhang, F, Zhang, P, Zhang, W, Zhao, H, Zhao, L, Zheng, W, Zoufir, A, Zucknick, M, Jang, I, Ghazoui, Z, Ahsen, M, Vogel, R, Neto, E, Norman, T, Tang, E, Garnett, M, Veroli, G, Fawell, S, Stolovitzky, G, Guinney, J, Dry, J, Saez-Rodriguez, J, Menden, Michael P. [0000-0003-0267-5792], Mason, Mike J. [0000-0002-5652-7739], Yu, Thomas [0000-0002-5841-0198], Kang, Jaewoo [0000-0001-6798-9106], Nguyen, Tin [0000-0001-8001-9470], Ahsen, Mehmet Eren [0000-0002-4907-0427], Stolovitzky, Gustavo [0000-0002-9618-2819], Guinney, Justin [0000-0003-1477-1888], Saez-Rodriguez, Julio [0000-0002-8552-8976], Apollo - University of Cambridge Repository, Menden, Michael P [0000-0003-0267-5792], Mason, Mike J [0000-0002-5652-7739], Pathology, CCA - Cancer biology and immunology, Medical oncology laboratory, Neurosurgery, Chemistry and Pharmaceutical Sciences, AIMMS, Medicinal chemistry, Universidade do Minho, Department of Computer Science, Professorship Marttinen P., Aalto-yliopisto, and Aalto University

Subjects
Drug Resistance, 02 engineering and technology, 13, PATHWAY, Antineoplastic Combined Chemotherapy Protocols, Molecular Targeted Therapy, Càncer, lcsh:Science, media_common, Cancer, Tumor, Settore BIO/18, Settore BIO/11, Drug combinations, High-throughput screening, Drug Synergism, purl.org/becyt/ford/1.2 [https], Genomics, Machine Learning, predictions, 3. Good health, ddc, Technologie de l'environnement, contrôle de la pollution, Benchmarking, 5.1 Pharmaceuticals, Cancer treatment, Farmacogenètica, Science & Technology - Other Topics, Development of treatments and therapeutic interventions, 0210 nano-technology, Human, Drug, media_common.quotation_subject, Science, 49/23, ADAM17 Protein, General Biochemistry, Genetics and Molecular Biology, 03 medical and health sciences, SDG 3 - Good Health and Well-being, RESOURCE, Machine learning, Genetics, Chimie, Humans, BREAST-CANCER, CELL, 49/98, Science & Technology, Antineoplastic Combined Chemotherapy Protocol, 45, MUTATIONS, Computational Biology, Androgen receptor, Breast-cancer, Gene, Cell, Inhibition, Resistance, Pathway, Mutations, Landscape, Resource, 631/114/1305, medicine.disease, Drug synergy, 49, 030104 developmental biology, Pharmacogenetics, Mutation, Ciências Médicas::Biotecnologia Médica, lcsh:Q, 631/154/1435/2163, Biomarkers, RESISTANCE, 0301 basic medicine, ING-INF/06 - BIOINGEGNERIA ELETTRONICA E INFORMATICA, Statistical methods, Computer science, General Physics and Astronomy, Datasets as Topic, Drug resistance, purl.org/becyt/ford/1 [https], Phosphatidylinositol 3-Kinases, Biotecnologia Médica [Ciências Médicas], Neoplasms, Science and technology, Phosphoinositide-3 Kinase Inhibitors, Multidisciplinary, Biomarkers, Tumor, Cell Line, Tumor, Drug Antagonism, Drug Resistance, Neoplasm, Treatment Outcome, Pharmacogenetic, article, ANDROGEN RECEPTOR, 49/39, 631/114/2415, 021001 nanoscience & nanotechnology, 692/4028/67, Multidisciplinary Sciences, 317 Pharmacy, Patient Safety, Systems biology, 3122 Cancers, INHIBITION, Computational biology, Cell Line, medicine, LANDSCAPE, Physique, Human Genome, Data Science, General Chemistry, AstraZeneca-Sanger Drug Combination DREAM Consortium, Astronomie, GENE, Good Health and Well Being, Pharmacogenomics, Genomic, Neoplasm, 631/553, Phosphatidylinositol 3-Kinase
Abstract

PubMed: 31209238, The effectiveness of most cancer targeted therapies is short-lived. Tumors often develop resistance that might be overcome with drug combinations. However, the number of possible combinations is vast, necessitating data-driven approaches to find optimal patient-specific treatments. Here we report AstraZeneca’s large drug combination dataset, consisting of 11,576 experiments from 910 combinations across 85 molecularly characterized cancer cell lines, and results of a DREAM Challenge to evaluate computational strategies for predicting synergistic drug pairs and biomarkers. 160 teams participated to provide a comprehensive methodological development and benchmarking. Winning methods incorporate prior knowledge of drug-target interactions. Synergy is predicted with an accuracy matching biological replicates for >60% of combinations. However, 20% of drug combinations are poorly predicted by all methods. Genomic rationale for synergy predictions are identified, including ADAM17 inhibitor antagonism when combined with PIK3CB/D inhibition contrasting to synergy when combined with other PI3K-pathway inhibitors in PIK3CA mutant cells. © 2019, The Author(s)., National Institute for Health Research, NIHR Wellcome Trust, WT: 102696, 206194 Magyar Tudományos Akadémia, MTA Bayer 668858 PrECISE AstraZeneca, We thank the Genomics of Drug Sensitivity in Cancer and COSMIC teams at the Wellcome Trust Sanger Institute for help with the preparation of the molecular data, Denes Turei for help with Omnipath, and Katjusa Koler for help with matching drug names across combination screens. We thank AstraZeneca for funding and provision of data to the DREAM Consortium to run the challenge, and funding from the European Union Horizon 2020 research (under grant agreement No 668858 PrECISE to J.S.R.), the Joint Research Center for Computational Biomedicine (which is partially funded by Bayer AG) to J.S.R., National Institute for Health Research (NIHR) Sheffield Biomedical Research Center, Premium Postdoctoral Fellowship Program of the Hungarian Academy of Sciences. M.G lab is supported by Wellcome Trust (102696 and 206194)., Competing interests: K.C.B., Z.G., G.Y.D., E.K.Y.T., S.F., and J.R.D. are AstraZeneca employees. K.C.B., Z.G., E.K.Y.T., S.F., and J.R.D. are AstraZeneca shareholders. Y.G. receives personal compensation from Eli Lilly and Company, is a shareholder of Cleerly, Inc., and Ann Arbor Algorithms, Inc. M.G. receives research funding from AstraZeneca and has performed consultancy for Sanofi. The remaining authors declare no competing interests.

Published
2019

12. Community assessment to advance computational prediction of cancer drug combinations in a pharmacogenomic screen

Author

Menden, M, Wang, D, Mason, M, Szalai, B, Bulusu, K, Guan, Y, Yu, T, Kang, J, Jeon, M, Wolfinger, R, Nguyen, T, Zaslavskiy, M, Abante, J, Abecassis, B, Aben, N, Aghamirzaie, D, Aittokallio, T, Akhtari, F, Al-lazikani, B, Alam, T, Allam, A, Allen, C, de Almeida, M, Altarawy, D, Alves, V, Amadoz, A, Anchang, B, Antolin, A, Ash, J, Aznar, V, Ba-alawi, W, Bagheri, M, Bajic, V, Ball, G, Ballester, P, Baptista, D, Bare, C, Bateson, M, Bender, A, Bertrand, D, Wijayawardena, B, Boroevich, K, Bosdriesz, E, Bougouffa, S, Bounova, G, Brouwer, T, Bryant, B, Calaza, M, Calderone, A, Calza, S, Capuzzi, S, Carbonell-Caballero, J, Carlin, D, Carter, H, Castagnoli, L, Celebi, R, Cesareni, G, Chang, H, Chen, G, Chen, H, Cheng, L, Chernomoretz, A, Chicco, D, Cho, K, Cho, S, Choi, D, Choi, J, Choi, K, Choi, M, Cock, M, Coker, E, Cortes-Ciriano, I, Cserzo, M, Cubuk, C, Curtis, C, Daele, D, Dang, C, Dijkstra, T, Dopazo, J, Draghici, S, Drosou, A, Dumontier, M, Ehrhart, F, Eid, F, Elhefnawi, M, Elmarakeby, H, van Engelen, B, Engin, H, de Esch, I, Evelo, C, Falcao, A, Farag, S, Fernandez-Lozano, C, Fisch, K, Flobak, A, Fornari, C, Foroushani, A, Fotso, D, Fourches, D, Friend, S, Frigessi, A, Gao, F, Gao, X, Gerold, J, Gestraud, P, Ghosh, S, Gillberg, J, Godoy-Lorite, A, Godynyuk, L, Godzik, A, Goldenberg, A, Gomez-Cabrero, D, Gonen, M, de Graaf, C, Gray, H, Grechkin, M, Guimera, R, Guney, E, Haibe-Kains, B, Han, Y, Hase, T, He, D, He, L, Heath, L, Hellton, K, Helmer-Citterich, M, Hidalgo, M, Hidru, D, Hill, S, Hochreiter, S, Hong, S, Hovig, E, Hsueh, Y, Hu, Z, Huang, J, Huang, R, Hunyady, L, Hwang, J, Hwang, T, Hwang, W, Hwang, Y, Isayev, O, Don't Walk, O, Jack, J, Jahandideh, S, Ji, J, Jo, Y, Kamola, P, Kanev, G, Karacosta, L, Karimi, M, Kaski, S, Kazanov, M, Khamis, A, Khan, S, Kiani, N, Kim, A, Kim, J, Kim, K, Kim, S, Kim, Y, Kirk, P, Kitano, H, Klambauer, G, Knowles, D, Ko, M, Kohn-Luque, A, Kooistra, A, Kuenemann, M, Kuiper, M, Kurz, C, Kwon, M, van Laarhoven, T, Laegreid, A, Lederer, S, Lee, H, Lee, J, Lee, Y, Lepp_aho, E, Lewis, R, Li, J, Li, L, Liley, J, Lim, W, Lin, C, Liu, Y, Lopez, Y, Low, J, Lysenko, A, Machado, D, Madhukar, N, Maeyer, D, Malpartida, A, Mamitsuka, H, Marabita, F, Marchal, K, Marttinen, P, Mason, D, Mazaheri, A, Mehmood, A, Mehreen, A, Michaut, M, Miller, R, Mitsopoulos, C, Modos, D, Moerbeke, M, Moo, K, Motsinger-Reif, A, Movva, R, Muraru, S, Muratov, E, Mushthofa, M, Nagarajan, N, Nakken, S, Nath, A, Neuvial, P, Newton, R, Ning, Z, Niz, C, Oliva, B, Olsen, C, Palmeri, A, Panesar, B, Papadopoulos, S, Park, J, Park, S, Pawitan, Y, Peluso, D, Pendyala, S, Peng, J, Perfetto, L, Pirro, S, Plevritis, S, Politi, R, Poon, H, Porta, E, Prellner, I, Preuer, K, Pujana, M, Ramnarine, R, Reid, J, Reyal, F, Richardson, S, Ricketts, C, Rieswijk, L, Rocha, M, Rodriguez-Gonzalvez, C, Roell, K, Rotroff, D, de Ruiter, J, Rukawa, P, Sadacca, B, Safikhani, Z, Safitri, F, Sales-Pardo, M, Sauer, S, Schlichting, M, Seoane, J, Serra, J, Shang, M, Sharma, A, Sharma, H, Shen, Y, Shiga, M, Shin, M, Shkedy, Z, Shopsowitz, K, Sinai, S, Skola, D, Smirnov, P, Soerensen, I, Soerensen, P, Song, J, Song, S, Soufan, O, Spitzmueller, A, Steipe, B, Suphavilai, C, Tamayo, S, Tamborero, D, Tang, J, Tanoli, Z, Tarres-Deulofeu, M, Tegner, J, Thommesen, L, Tonekaboni, S, Tran, H, Troyer, E, Truong, A, Tsunoda, T, Turu, G, Tzeng, G, Verbeke, L, Videla, S, Vis, D, Voronkov, A, Votis, K, Wang, A, Wang, H, Wang, P, Wang, S, Wang, W, Wang, X, Wennerberg, K, Wernisch, L, Wessels, L, van Westen, G, Westerman, B, White, S, Willighagen, E, Wurdinger, T, Xie, L, Xie, S, Xu, H, Yadav, B, Yau, C, Yeerna, H, Yin, J, Yu, M, Yun, S, Zakharov, A, Zamichos, A, Zanin, M, Zeng, L, Zenil, H, Zhang, F, Zhang, P, Zhang, W, Zhao, H, Zhao, L, Zheng, W, Zoufir, A, Zucknick, M, Jang, I, Ghazoui, Z, Ahsen, M, Vogel, R, Neto, E, Norman, T, Tang, E, Garnett, M, Veroli, G, Fawell, S, Stolovitzky, G, Guinney, J, Dry, J, Saez-Rodriguez, J, Menden M. P., Wang D., Mason M. J., Szalai B., Bulusu K. C., Guan Y., Yu T., Kang J., Jeon M., Wolfinger R., Nguyen T., Zaslavskiy M., Abante J., Abecassis B. S., Aben N., Aghamirzaie D., Aittokallio T., Akhtari F. S., Al-lazikani B., Alam T., Allam A., Allen C., de Almeida M. P., Altarawy D., Alves V., Amadoz A., Anchang B., Antolin A. A., Ash J. R., Aznar V. R., Ba-alawi W., Bagheri M., Bajic V., Ball G., Ballester P. J., Baptista D., Bare C., Bateson M., Bender A., Bertrand D., Wijayawardena B., Boroevich K. A., Bosdriesz E., Bougouffa S., Bounova G., Brouwer T., Bryant B., Calaza M., Calderone A., Calza S., Capuzzi S., Carbonell-Caballero J., Carlin D., Carter H., Castagnoli L., Celebi R., Cesareni G., Chang H., Chen G., Chen H., Cheng L., Chernomoretz A., Chicco D., Cho K. -H., Cho S., Choi D., Choi J., Choi K., Choi M., Cock M. D., Coker E., Cortes-Ciriano I., Cserzo M., Cubuk C., Curtis C., Daele D. V., Dang C. C., Dijkstra T., Dopazo J., Draghici S., Drosou A., Dumontier M., Ehrhart F., Eid F. -E., ElHefnawi M., Elmarakeby H., van Engelen B., Engin H. B., de Esch I., Evelo C., Falcao A. O., Farag S., Fernandez-Lozano C., Fisch K., Flobak A., Fornari C., Foroushani A. B. K., Fotso D. C., Fourches D., Friend S., Frigessi A., Gao F., Gao X., Gerold J. M., Gestraud P., Ghosh S., Gillberg J., Godoy-Lorite A., Godynyuk L., Godzik A., Goldenberg A., Gomez-Cabrero D., Gonen M., de Graaf C., Gray H., Grechkin M., Guimera R., Guney E., Haibe-Kains B., Han Y., Hase T., He D., He L., Heath L. S., Hellton K. H., Helmer-Citterich M., Hidalgo M. R., Hidru D., Hill S. M., Hochreiter S., Hong S., Hovig E., Hsueh Y. -C., Hu Z., Huang J. K., Huang R. S., Hunyady L., Hwang J., Hwang T. H., Hwang W., Hwang Y., Isayev O., Don't Walk O. B., Jack J., Jahandideh S., Ji J., Jo Y., Kamola P. J., Kanev G. K., Karacosta L., Karimi M., Kaski S., Kazanov M., Khamis A. M., Khan S. A., Kiani N. A., Kim A., Kim J., Kim K., Kim S., Kim Y., Kirk P. D. W., Kitano H., Klambauer G., Knowles D., Ko M., Kohn-Luque A., Kooistra A. J., Kuenemann M. A., Kuiper M., Kurz C., Kwon M., van Laarhoven T., Laegreid A., Lederer S., Lee H., Lee J., Lee Y. W., Lepp_aho E., Lewis R., Li J., Li L., Liley J., Lim W. K., Lin C., Liu Y., Lopez Y., Low J., Lysenko A., Machado D., Madhukar N., Maeyer D. D., Malpartida A. B., Mamitsuka H., Marabita F., Marchal K., Marttinen P., Mason D., Mazaheri A., Mehmood A., Mehreen A., Michaut M., Miller R. A., Mitsopoulos C., Modos D., Moerbeke M. V., Moo K., Motsinger-Reif A., Movva R., Muraru S., Muratov E., Mushthofa M., Nagarajan N., Nakken S., Nath A., Neuvial P., Newton R., Ning Z., Niz C. D., Oliva B., Olsen C., Palmeri A., Panesar B., Papadopoulos S., Park J., Park S., Pawitan Y., Peluso D., Pendyala S., Peng J., Perfetto L., Pirro S., Plevritis S., Politi R., Poon H., Porta E., Prellner I., Preuer K., Pujana M. A., Ramnarine R., Reid J. E., Reyal F., Richardson S., Ricketts C., Rieswijk L., Rocha M., Rodriguez-Gonzalvez C., Roell K., Rotroff D., de Ruiter J. R., Rukawa P., Sadacca B., Safikhani Z., Safitri F., Sales-Pardo M., Sauer S., Schlichting M., Seoane J. A., Serra J., Shang M. -M., Sharma A., Sharma H., Shen Y., Shiga M., Shin M., Shkedy Z., Shopsowitz K., Sinai S., Skola D., Smirnov P., Soerensen I. F., Soerensen P., Song J. -H., Song S. O., Soufan O., Spitzmueller A., Steipe B., Suphavilai C., Tamayo S. P., Tamborero D., Tang J., Tanoli Z. -U. -R., Tarres-Deulofeu M., Tegner J., Thommesen L., Tonekaboni S. A. M., Tran H., Troyer E. D., Truong A., Tsunoda T., Turu G., Tzeng G. -Y., Verbeke L., Videla S., Vis D., Voronkov A., Votis K., Wang A., Wang H. -Q. H., Wang P. -W., Wang S., Wang W., Wang X., Wennerberg K., Wernisch L., Wessels L., van Westen G. J. P., Westerman B. A., White S. R., Willighagen E., Wurdinger T., Xie L., Xie S., Xu H., Yadav B., Yau C., Yeerna H., Yin J. W., Yu M., Yu M. H., Yun S. J., Zakharov A., Zamichos A., Zanin M., Zeng L., Zenil H., Zhang F., Zhang P., Zhang W., Zhao H., Zhao L., Zheng W., Zoufir A., Zucknick M., Jang I. S., Ghazoui Z., Ahsen M. E., Vogel R., Neto E. C., Norman T., Tang E. K. Y., Garnett M. J., Veroli G. Y. D., Fawell S., Stolovitzky G., Guinney J., Dry J. R., Saez-Rodriguez J., Menden, M, Wang, D, Mason, M, Szalai, B, Bulusu, K, Guan, Y, Yu, T, Kang, J, Jeon, M, Wolfinger, R, Nguyen, T, Zaslavskiy, M, Abante, J, Abecassis, B, Aben, N, Aghamirzaie, D, Aittokallio, T, Akhtari, F, Al-lazikani, B, Alam, T, Allam, A, Allen, C, de Almeida, M, Altarawy, D, Alves, V, Amadoz, A, Anchang, B, Antolin, A, Ash, J, Aznar, V, Ba-alawi, W, Bagheri, M, Bajic, V, Ball, G, Ballester, P, Baptista, D, Bare, C, Bateson, M, Bender, A, Bertrand, D, Wijayawardena, B, Boroevich, K, Bosdriesz, E, Bougouffa, S, Bounova, G, Brouwer, T, Bryant, B, Calaza, M, Calderone, A, Calza, S, Capuzzi, S, Carbonell-Caballero, J, Carlin, D, Carter, H, Castagnoli, L, Celebi, R, Cesareni, G, Chang, H, Chen, G, Chen, H, Cheng, L, Chernomoretz, A, Chicco, D, Cho, K, Cho, S, Choi, D, Choi, J, Choi, K, Choi, M, Cock, M, Coker, E, Cortes-Ciriano, I, Cserzo, M, Cubuk, C, Curtis, C, Daele, D, Dang, C, Dijkstra, T, Dopazo, J, Draghici, S, Drosou, A, Dumontier, M, Ehrhart, F, Eid, F, Elhefnawi, M, Elmarakeby, H, van Engelen, B, Engin, H, de Esch, I, Evelo, C, Falcao, A, Farag, S, Fernandez-Lozano, C, Fisch, K, Flobak, A, Fornari, C, Foroushani, A, Fotso, D, Fourches, D, Friend, S, Frigessi, A, Gao, F, Gao, X, Gerold, J, Gestraud, P, Ghosh, S, Gillberg, J, Godoy-Lorite, A, Godynyuk, L, Godzik, A, Goldenberg, A, Gomez-Cabrero, D, Gonen, M, de Graaf, C, Gray, H, Grechkin, M, Guimera, R, Guney, E, Haibe-Kains, B, Han, Y, Hase, T, He, D, He, L, Heath, L, Hellton, K, Helmer-Citterich, M, Hidalgo, M, Hidru, D, Hill, S, Hochreiter, S, Hong, S, Hovig, E, Hsueh, Y, Hu, Z, Huang, J, Huang, R, Hunyady, L, Hwang, J, Hwang, T, Hwang, W, Hwang, Y, Isayev, O, Don't Walk, O, Jack, J, Jahandideh, S, Ji, J, Jo, Y, Kamola, P, Kanev, G, Karacosta, L, Karimi, M, Kaski, S, Kazanov, M, Khamis, A, Khan, S, Kiani, N, Kim, A, Kim, J, Kim, K, Kim, S, Kim, Y, Kirk, P, Kitano, H, Klambauer, G, Knowles, D, Ko, M, Kohn-Luque, A, Kooistra, A, Kuenemann, M, Kuiper, M, Kurz, C, Kwon, M, van Laarhoven, T, Laegreid, A, Lederer, S, Lee, H, Lee, J, Lee, Y, Lepp_aho, E, Lewis, R, Li, J, Li, L, Liley, J, Lim, W, Lin, C, Liu, Y, Lopez, Y, Low, J, Lysenko, A, Machado, D, Madhukar, N, Maeyer, D, Malpartida, A, Mamitsuka, H, Marabita, F, Marchal, K, Marttinen, P, Mason, D, Mazaheri, A, Mehmood, A, Mehreen, A, Michaut, M, Miller, R, Mitsopoulos, C, Modos, D, Moerbeke, M, Moo, K, Motsinger-Reif, A, Movva, R, Muraru, S, Muratov, E, Mushthofa, M, Nagarajan, N, Nakken, S, Nath, A, Neuvial, P, Newton, R, Ning, Z, Niz, C, Oliva, B, Olsen, C, Palmeri, A, Panesar, B, Papadopoulos, S, Park, J, Park, S, Pawitan, Y, Peluso, D, Pendyala, S, Peng, J, Perfetto, L, Pirro, S, Plevritis, S, Politi, R, Poon, H, Porta, E, Prellner, I, Preuer, K, Pujana, M, Ramnarine, R, Reid, J, Reyal, F, Richardson, S, Ricketts, C, Rieswijk, L, Rocha, M, Rodriguez-Gonzalvez, C, Roell, K, Rotroff, D, de Ruiter, J, Rukawa, P, Sadacca, B, Safikhani, Z, Safitri, F, Sales-Pardo, M, Sauer, S, Schlichting, M, Seoane, J, Serra, J, Shang, M, Sharma, A, Sharma, H, Shen, Y, Shiga, M, Shin, M, Shkedy, Z, Shopsowitz, K, Sinai, S, Skola, D, Smirnov, P, Soerensen, I, Soerensen, P, Song, J, Song, S, Soufan, O, Spitzmueller, A, Steipe, B, Suphavilai, C, Tamayo, S, Tamborero, D, Tang, J, Tanoli, Z, Tarres-Deulofeu, M, Tegner, J, Thommesen, L, Tonekaboni, S, Tran, H, Troyer, E, Truong, A, Tsunoda, T, Turu, G, Tzeng, G, Verbeke, L, Videla, S, Vis, D, Voronkov, A, Votis, K, Wang, A, Wang, H, Wang, P, Wang, S, Wang, W, Wang, X, Wennerberg, K, Wernisch, L, Wessels, L, van Westen, G, Westerman, B, White, S, Willighagen, E, Wurdinger, T, Xie, L, Xie, S, Xu, H, Yadav, B, Yau, C, Yeerna, H, Yin, J, Yu, M, Yun, S, Zakharov, A, Zamichos, A, Zanin, M, Zeng, L, Zenil, H, Zhang, F, Zhang, P, Zhang, W, Zhao, H, Zhao, L, Zheng, W, Zoufir, A, Zucknick, M, Jang, I, Ghazoui, Z, Ahsen, M, Vogel, R, Neto, E, Norman, T, Tang, E, Garnett, M, Veroli, G, Fawell, S, Stolovitzky, G, Guinney, J, Dry, J, Saez-Rodriguez, J, Menden M. P., Wang D., Mason M. J., Szalai B., Bulusu K. C., Guan Y., Yu T., Kang J., Jeon M., Wolfinger R., Nguyen T., Zaslavskiy M., Abante J., Abecassis B. S., Aben N., Aghamirzaie D., Aittokallio T., Akhtari F. S., Al-lazikani B., Alam T., Allam A., Allen C., de Almeida M. P., Altarawy D., Alves V., Amadoz A., Anchang B., Antolin A. A., Ash J. R., Aznar V. R., Ba-alawi W., Bagheri M., Bajic V., Ball G., Ballester P. J., Baptista D., Bare C., Bateson M., Bender A., Bertrand D., Wijayawardena B., Boroevich K. A., Bosdriesz E., Bougouffa S., Bounova G., Brouwer T., Bryant B., Calaza M., Calderone A., Calza S., Capuzzi S., Carbonell-Caballero J., Carlin D., Carter H., Castagnoli L., Celebi R., Cesareni G., Chang H., Chen G., Chen H., Cheng L., Chernomoretz A., Chicco D., Cho K. -H., Cho S., Choi D., Choi J., Choi K., Choi M., Cock M. D., Coker E., Cortes-Ciriano I., Cserzo M., Cubuk C., Curtis C., Daele D. V., Dang C. C., Dijkstra T., Dopazo J., Draghici S., Drosou A., Dumontier M., Ehrhart F., Eid F. -E., ElHefnawi M., Elmarakeby H., van Engelen B., Engin H. B., de Esch I., Evelo C., Falcao A. O., Farag S., Fernandez-Lozano C., Fisch K., Flobak A., Fornari C., Foroushani A. B. K., Fotso D. C., Fourches D., Friend S., Frigessi A., Gao F., Gao X., Gerold J. M., Gestraud P., Ghosh S., Gillberg J., Godoy-Lorite A., Godynyuk L., Godzik A., Goldenberg A., Gomez-Cabrero D., Gonen M., de Graaf C., Gray H., Grechkin M., Guimera R., Guney E., Haibe-Kains B., Han Y., Hase T., He D., He L., Heath L. S., Hellton K. H., Helmer-Citterich M., Hidalgo M. R., Hidru D., Hill S. M., Hochreiter S., Hong S., Hovig E., Hsueh Y. -C., Hu Z., Huang J. K., Huang R. S., Hunyady L., Hwang J., Hwang T. H., Hwang W., Hwang Y., Isayev O., Don't Walk O. B., Jack J., Jahandideh S., Ji J., Jo Y., Kamola P. J., Kanev G. K., Karacosta L., Karimi M., Kaski S., Kazanov M., Khamis A. M., Khan S. A., Kiani N. A., Kim A., Kim J., Kim K., Kim S., Kim Y., Kirk P. D. W., Kitano H., Klambauer G., Knowles D., Ko M., Kohn-Luque A., Kooistra A. J., Kuenemann M. A., Kuiper M., Kurz C., Kwon M., van Laarhoven T., Laegreid A., Lederer S., Lee H., Lee J., Lee Y. W., Lepp_aho E., Lewis R., Li J., Li L., Liley J., Lim W. K., Lin C., Liu Y., Lopez Y., Low J., Lysenko A., Machado D., Madhukar N., Maeyer D. D., Malpartida A. B., Mamitsuka H., Marabita F., Marchal K., Marttinen P., Mason D., Mazaheri A., Mehmood A., Mehreen A., Michaut M., Miller R. A., Mitsopoulos C., Modos D., Moerbeke M. V., Moo K., Motsinger-Reif A., Movva R., Muraru S., Muratov E., Mushthofa M., Nagarajan N., Nakken S., Nath A., Neuvial P., Newton R., Ning Z., Niz C. D., Oliva B., Olsen C., Palmeri A., Panesar B., Papadopoulos S., Park J., Park S., Pawitan Y., Peluso D., Pendyala S., Peng J., Perfetto L., Pirro S., Plevritis S., Politi R., Poon H., Porta E., Prellner I., Preuer K., Pujana M. A., Ramnarine R., Reid J. E., Reyal F., Richardson S., Ricketts C., Rieswijk L., Rocha M., Rodriguez-Gonzalvez C., Roell K., Rotroff D., de Ruiter J. R., Rukawa P., Sadacca B., Safikhani Z., Safitri F., Sales-Pardo M., Sauer S., Schlichting M., Seoane J. A., Serra J., Shang M. -M., Sharma A., Sharma H., Shen Y., Shiga M., Shin M., Shkedy Z., Shopsowitz K., Sinai S., Skola D., Smirnov P., Soerensen I. F., Soerensen P., Song J. -H., Song S. O., Soufan O., Spitzmueller A., Steipe B., Suphavilai C., Tamayo S. P., Tamborero D., Tang J., Tanoli Z. -U. -R., Tarres-Deulofeu M., Tegner J., Thommesen L., Tonekaboni S. A. M., Tran H., Troyer E. D., Truong A., Tsunoda T., Turu G., Tzeng G. -Y., Verbeke L., Videla S., Vis D., Voronkov A., Votis K., Wang A., Wang H. -Q. H., Wang P. -W., Wang S., Wang W., Wang X., Wennerberg K., Wernisch L., Wessels L., van Westen G. J. P., Westerman B. A., White S. R., Willighagen E., Wurdinger T., Xie L., Xie S., Xu H., Yadav B., Yau C., Yeerna H., Yin J. W., Yu M., Yu M. H., Yun S. J., Zakharov A., Zamichos A., Zanin M., Zeng L., Zenil H., Zhang F., Zhang P., Zhang W., Zhao H., Zhao L., Zheng W., Zoufir A., Zucknick M., Jang I. S., Ghazoui Z., Ahsen M. E., Vogel R., Neto E. C., Norman T., Tang E. K. Y., Garnett M. J., Veroli G. Y. D., Fawell S., Stolovitzky G., Guinney J., Dry J. R., and Saez-Rodriguez J.

Abstract

The effectiveness of most cancer targeted therapies is short-lived. Tumors often develop resistance that might be overcome with drug combinations. However, the number of possible combinations is vast, necessitating data-driven approaches to find optimal patient-specific treatments. Here we report AstraZeneca’s large drug combination dataset, consisting of 11,576 experiments from 910 combinations across 85 molecularly characterized cancer cell lines, and results of a DREAM Challenge to evaluate computational strategies for predicting synergistic drug pairs and biomarkers. 160 teams participated to provide a comprehensive methodological development and benchmarking. Winning methods incorporate prior knowledge of drug-target interactions. Synergy is predicted with an accuracy matching biological replicates for >60% of combinations. However, 20% of drug combinations are poorly predicted by all methods. Genomic rationale for synergy predictions are identified, including ADAM17 inhibitor antagonism when combined with PIK3CB/D inhibition contrasting to synergy when combined with other PI3K-pathway inhibitors in PIK3CA mutant cells.

Published
2019

13. Crowdsourced mapping of unexplored target space of kinase inhibitors

Author

Cichońska, A., Ravikumar, B., Allaway, R., Wan, F., Park, S., Isayev, O., Li, S., Mason, M., Lamb, A., Tanoli, Z., Jeon, M., Kim, S., Popova, M., Capuzzi, S., Zeng, J., Dang, K., Koytiger, G., Kang, J., Wells, C., Willson, T., Lienhard, M., Prasse, P., Bachmann, I., Ganzlin, J., Barel, G., Herwig, R., Oprea, T., Schlessinger, A., Drewry, D., Stolovitzky, G., Wennerberg, K., Guinney, J., and Aittokallio, T.

Abstract

Despite decades of intensive search for compounds that modulate the activity of particular protein targets, a large proportion of the human kinome remains as yet undrugged. Effective approaches are therefore required to map the massive space of unexplored compound–kinase interactions for novel and potent activities. Here, we carry out a crowdsourced benchmarking of predictive algorithms for kinase inhibitor potencies across multiple kinase families tested on unpublished bioactivity data. We find the top-performing predictions are based on various models, including kernel learning, gradient boosting and deep learning, and their ensemble leads to a predictive accuracy exceeding that of single-dose kinase activity assays. We design experiments based on the model predictions and identify unexpected activities even for under-studied kinases, thereby accelerating experimental mapping efforts. The open-source prediction algorithms together with the bioactivities between 95 compounds and 295 kinases provide a resource for benchmarking prediction algorithms and for extending the druggable kinome.

Published
2021

14. Crowdsourced mapping of unexplored target space of kinase inhibitors

Author

Cichonska, A, Ravikumar, B, Allaway, RJ, Wan, F, Park, S, Isayev, O, Li, S, Mason, M, Lamb, A, Tanoli, Z, Jeon, M, Kim, S, Popova, M, Capuzzi, S, Zeng, J, Dang, K, Koytiger, G, Kang, J, Wells, CI, Willson, TM, Oprea, TI, Schlessinger, A, Drewry, DH, Stolovitzky, G, Wennerberg, K, Guinney, J, Aittokallio, T, Tan, M, Huang, C-H, Shih, ESC, Chen, T-M, Wu, C-H, Fang, W-Q, Chen, J-Y, Hwang, M-J, Wang, X, Ben Guebila, M, Shamsaei, B, Singh, S, Nguyen, Thin, Karimi, M, Wu, D, Wang, Z, Shen, Y, Ozturk, H, Ozkirimli, E, Ozgur, A, Lim, H, Xie, L, Kanev, GK, Kooistra, AJ, Westerman, BA, Terzopoulos, P, Ntagiantas, K, Fotis, C, Alexopoulos, L, Boeckaerts, D, Stock, M, De Baets, B, Briers, Y, Luo, Y, Hu, H, Peng, J, Dogan, T, Rifaioglu, AS, Atas, H, Atalay, RC, Atalay, V, Martin, MJ, Lee, J, Yun, S, Kim, B, Chang, B, Turu, G, Misak, A, Szalai, B, Hunyady, L, Lienhard, M, Prasse, P, Bachmann, I, Ganzlin, J, Barel, G, Herwig, R, Orsolic, D, Lucic, B, Stepanic, V, Smuc, T, Cichonska, A, Ravikumar, B, Allaway, RJ, Wan, F, Park, S, Isayev, O, Li, S, Mason, M, Lamb, A, Tanoli, Z, Jeon, M, Kim, S, Popova, M, Capuzzi, S, Zeng, J, Dang, K, Koytiger, G, Kang, J, Wells, CI, Willson, TM, Oprea, TI, Schlessinger, A, Drewry, DH, Stolovitzky, G, Wennerberg, K, Guinney, J, Aittokallio, T, Tan, M, Huang, C-H, Shih, ESC, Chen, T-M, Wu, C-H, Fang, W-Q, Chen, J-Y, Hwang, M-J, Wang, X, Ben Guebila, M, Shamsaei, B, Singh, S, Nguyen, Thin, Karimi, M, Wu, D, Wang, Z, Shen, Y, Ozturk, H, Ozkirimli, E, Ozgur, A, Lim, H, Xie, L, Kanev, GK, Kooistra, AJ, Westerman, BA, Terzopoulos, P, Ntagiantas, K, Fotis, C, Alexopoulos, L, Boeckaerts, D, Stock, M, De Baets, B, Briers, Y, Luo, Y, Hu, H, Peng, J, Dogan, T, Rifaioglu, AS, Atas, H, Atalay, RC, Atalay, V, Martin, MJ, Lee, J, Yun, S, Kim, B, Chang, B, Turu, G, Misak, A, Szalai, B, Hunyady, L, Lienhard, M, Prasse, P, Bachmann, I, Ganzlin, J, Barel, G, Herwig, R, Orsolic, D, Lucic, B, Stepanic, V, and Smuc, T

Abstract

AbstractDespite decades of intensive search for compounds that modulate the activity of particular protein targets, a large proportion of the human kinome remains as yet undrugged. Effective approaches are therefore required to map the massive space of unexplored compound–kinase interactions for novel and potent activities. Here, we carry out a crowdsourced benchmarking of predictive algorithms for kinase inhibitor potencies across multiple kinase families tested on unpublished bioactivity data. We find the top-performing predictions are based on various models, including kernel learning, gradient boosting and deep learning, and their ensemble leads to a predictive accuracy exceeding that of single-dose kinase activity assays. We design experiments based on the model predictions and identify unexpected activities even for under-studied kinases, thereby accelerating experimental mapping efforts. The open-source prediction algorithms together with the bioactivities between 95 compounds and 295 kinases provide a resource for benchmarking prediction algorithms and for extending the druggable kinome.

Published
2021

17. High Throughput Screening of Millions of van der Waals Heterostructures for Superlubricant Applications

Author

Fronzi, M, Tawfik, SA, Ghazaleh, MA, Isayev, O, Winkler, DA, Shapter, J, and Ford, MJ

Abstract

© 2020 Wiley-VCH GmbH The screening of novel materials is an important topic in the field of materials science. Although traditional computational modeling, especially first-principles approaches, is a very useful and accurate tool to predict the properties of novel materials, it still demands extensive and expensive state-of-the-art computational resources. Additionally, they can often be extremely time consuming. A time and resource efficient machine learning approach to create a dataset of structural properties of 18 million van der Waals layered structures is described. In particular, the authors focus on the interlayer energy and the elastic constant of layered materials composed of two different 2D structures that are important for novel solid lubricant and super-lubricant materials. It is shown that machine learning models can predict results of computationally expansive approaches (i.e., density functional theory) with high accuracy.

Published
2020

18. Effects of different deoxidization methods on high-temperature physical properties of high-strength low-alloy steels

Author

Zhang, K. -L., Wu, K. -M., Isayev, O., Hress, O., Yershov, S., Tsepelev, V., Hu, C. -Y., Zhang, K. -L., Wu, K. -M., Isayev, O., Hress, O., Yershov, S., Tsepelev, V., and Hu, C. -Y.

Abstract

This study was aimed at examining the effects of different deoxidization methods on the physical properties of metallic melts by measuring the changes in the kinematic viscosity, electrical resistivity, surface tension, and density of the metallurgical melts during the heating and cooling processes. Our results indicate that high-temperature physical properties are consistently affected by specific elements and compounds. © 2020 Ke-lin Zhang et al., published by De Gruyter 2020.

Published
2020

19. EVALUATION OF MONITORING THE PROCESS OF DEFORMATION AND SEDIMATION OF A RIGHT LINE VERTICAL ROD

Author

Isayev, O., Gulayev, Yu., Strilets, V., and Chulanov, P.

Subjects
деформация, осадка, напряжения, предел упругости, вертикальный металлический стержень, точность измерений, датчики деформации, геодезический мониторинг, deformation, sediment, stress, elastic limit, vertical metal rod, measurement accuracy, strain gauges, geodetic monitoring
Abstract

Elements of building structures (EBS) undergo various kinds of deformation as a result of force and temperature effects. As long as the deformations are within the design values, they are not terrible for the structures of the structure, however, if they exceed the permissible values, then there may be a danger of their operation. Therefore, many large, complex, critical structures are constantly monitored by engineering geodesy methods and using sensors of deformations, displacements, tilts, etc.On the example of a vertical rod made of low-carbon steel, which is rigidly fixed and deformed as a result of subsidence of the lower base, the calculation of the accuracy of measuring the tensile strain is calculated, provided that it allows you to track the elastic limits. The calculation shows that the accuracy of measuring the tensile deformation of low-carbon steel is very high and can increase even more for metals of increased strength and with a different (less pronounced) tensile diagram. But modern devices can measure deformations with such precision. Such devices in the form of strain gauge networks are included in the general geodetic monitoring system., На примере вертикального стержня из низкоуглеродистой стали, который жестко закреплен и деформируется в результате осадки нижнего основания, сделан расчет точности измерения деформации растяжения при условии, что она позволит отследить предел упругости. Показана возможность измерения деформации с помощью тензометрических датчиков. Рассмотрены вопросы мониторинга такой конструкции.

Published
2019

20. ANI-1: an extensible neural network potential with DFT accuracy at force field computational cost† †Electronic supplementary information (ESI) available. See DOI: 10.1039/c6sc05720a Click here for additional data file

Author

Smith, J. S., Isayev, O., and Roitberg, A. E.

Subjects
Chemistry, Physics::Chemical Physics
Abstract

We demonstrate how a deep neural network (NN) trained on a data set of quantum mechanical (QM) DFT calculated energies can learn an accurate and transferable atomistic potential for organic molecules containing H, C, N, and O atoms., Deep learning is revolutionizing many areas of science and technology, especially image, text, and speech recognition. In this paper, we demonstrate how a deep neural network (NN) trained on quantum mechanical (QM) DFT calculations can learn an accurate and transferable potential for organic molecules. We introduce ANAKIN-ME (Accurate NeurAl networK engINe for Molecular Energies) or ANI for short. ANI is a new method designed with the intent of developing transferable neural network potentials that utilize a highly-modified version of the Behler and Parrinello symmetry functions to build single-atom atomic environment vectors (AEV) as a molecular representation. AEVs provide the ability to train neural networks to data that spans both configurational and conformational space, a feat not previously accomplished on this scale. We utilized ANI to build a potential called ANI-1, which was trained on a subset of the GDB databases with up to 8 heavy atoms in order to predict total energies for organic molecules containing four atom types: H, C, N, and O. To obtain an accelerated but physically relevant sampling of molecular potential surfaces, we also proposed a Normal Mode Sampling (NMS) method for generating molecular conformations. Through a series of case studies, we show that ANI-1 is chemically accurate compared to reference DFT calculations on much larger molecular systems (up to 54 atoms) than those included in the training data set.

Published
2017

21. Inter-Modular Linkers play a crucial role in governing the biosynthesis of non-ribosomal peptides

Author

Shank, E.A., Isayev, O., Farag, S., Tropsha, A., Bleich, R.M., and Bowers, A.A.

Abstract

Motivation: Non-ribosomal peptide synthetases (NRPSs) are modular enzymatic machines that catalyze the ribosome-independent production of structurally complex small peptides, many of which have important clinical applications as antibiotics, antifungals and anti-cancer agents. Several groups have tried to expand natural product diversity by intermixing different NRPS modules to create synthetic peptides. This approach has not been as successful as anticipated, suggesting that these modules are not fully interchangeable. Results: We explored whether Inter-Modular Linkers (IMLs) impact the ability of NRPS modules to communicate during the synthesis of NRPs. We developed a parser to extract 39 804 IMLs from both well annotated and putative NRPS biosynthetic gene clusters from 39 232 bacterial genomes and established the first IMLs database. We analyzed these IMLs and identified a striking relationship between IMLs and the amino acid substrates of their adjacent modules. More than 92% of the identified IMLs connect modules that activate a particular pair of substrates, suggesting that significant specificity is embedded within these sequences. We therefore propose that incorporating the correct IML is critical when attempting combinatorial biosynthesis of novel NRPS. Availability and implementation: The IMLs database as well as the NRPS-Parser have been made available on the web at https://nrps-linker.unc.edu. The entire source code of the project is hosted in GitHub repository (https://github.com/SWFarag/nrps-linker). Supplementary information: Supplementary data are available at Bioinformatics online.

Published
2019
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32. Effects of different deoxidization methods on high-temperature physical properties of high-strength low-alloy steels

Author

Zhang Ke-lin, Wu Kai-ming, Isayev Oleg, Hress Oleksandr, Yershov Serhii, Tsepelev Vladimir, and Hu Cheng-yang

Subjects
liquid steel, kinematic viscosity, surface tension, electrical resistivity, density, Technology, Chemical technology, TP1-1185, Chemicals: Manufacture, use, etc., TP200-248
Abstract

This study was aimed at examining the effects of different deoxidization methods on the physical properties of metallic melts by measuring the changes in the kinematic viscosity, electrical resistivity, surface tension, and density of the metallurgical melts during the heating and cooling processes. Our results indicate that high-temperature physical properties are consistently affected by specific elements and compounds.

Published
2020
Full Text
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33. Effect of Cu addition on microstructure and impact toughness in the simulated coarse-grained heat-affected zone of high-strength low-alloy steels

Author

Huang, G., Wan, X. L., Wu, K. M., Isayev, O., Hress, O., Rodionova, I., Shirzadi, A. A., Huang, G., Wan, X. L., Wu, K. M., Isayev, O., Hress, O., Rodionova, I., and Shirzadi, A. A.

Abstract

The effects of Cu content on microstructure and impact toughness in the simulated coarse-grained heat-affected zone (CGHAZ) of high-strength low-alloy steels were investigated. It has been observed that the microstructure in the simulated CGHAZ of Cu-free steel is dominated by a small proportion of acicular ferrite and predominantly bainite with martensite–austenite constituent. Whereas, in the 0.45 and 1.01% Cu-containing steels, the acicular ferrite increased significantly due to the effective nucleation on intragranular inclusions with outer layer of MnS and CuS. The formation of acicular ferrite is attributed to superior high heat-affected zone impact toughness in the 0.45% Cu-containing steel. Furthermore, the increasing martensite–austenite constituent and ε-Cu precipitates in the simulated CGHAZ of 1.01% Cu-containing steel caused degradation in impact toughness.

34. Effect of Zr-Ti combined deoxidation on the microstructure and mechanical properties of high-strength low-alloy steels

Author

Li, Y., Wan, X. L., Lu, W. Y., Shirzadi, A. A., Isayev, O., Hress, O., Wu, K. M., Li, Y., Wan, X. L., Lu, W. Y., Shirzadi, A. A., Isayev, O., Hress, O., and Wu, K. M.

Abstract

Effect of Zr-Ti combined deoxidation on the microstructures and mechanical properties of high-strength low-alloy steels were investigated by means of microstructure analysis, tensile- and Charpy impact-tests. Experimental results showed the addition of Zr-Ti based deoxidizing agent resulted in the formation of fine Zr-Ti complex oxides. Owing to abundant finely oxides dispersed in the matrix uniformly, the microstructures deoxidized using Zr-Ti was uniform and the strength was enhanced comparing against the steels deoxidized using Al based deoxidizer. The ductility and toughness of the thick steel plates deoxidized using Zr-Ti were also considerably improved, apparently due to the spheroidization and uniform dispersion of MnS.

35. Effect of Zr-Ti combined deoxidation on the microstructure and mechanical properties of high-strength low-alloy steels

Author

Li, Y., Wan, X. L., Lu, W. Y., Shirzadi, A. A., Isayev, O., Hress, O., Wu, K. M., Li, Y., Wan, X. L., Lu, W. Y., Shirzadi, A. A., Isayev, O., Hress, O., and Wu, K. M.

Abstract

Effect of Zr-Ti combined deoxidation on the microstructures and mechanical properties of high-strength low-alloy steels were investigated by means of microstructure analysis, tensile- and Charpy impact-tests. Experimental results showed the addition of Zr-Ti based deoxidizing agent resulted in the formation of fine Zr-Ti complex oxides. Owing to abundant finely oxides dispersed in the matrix uniformly, the microstructures deoxidized using Zr-Ti was uniform and the strength was enhanced comparing against the steels deoxidized using Al based deoxidizer. The ductility and toughness of the thick steel plates deoxidized using Zr-Ti were also considerably improved, apparently due to the spheroidization and uniform dispersion of MnS.

36. Impressive computational acceleration by using machine learning for 2-dimensional super-lubricant materials discovery

Author

Fronzi, M, Ghazaleh, MA, Isayev, O, Winkler, DA, Shapter, J, Ford, MJ, Fronzi, M, Ghazaleh, MA, Isayev, O, Winkler, DA, Shapter, J, and Ford, MJ

Abstract

The screening of novel materials is an important topic in the field of materials science. Although traditional computational modeling, especially first-principles approaches, is a very useful and accurate tool to predict the properties of novel materials, it still demands extensive and expensive state-of-the-art computational resources. Additionally, they can be often extremely time consuming. We describe a time and resource-efficient machine learning approach to create a large dataset of structural properties of van der Waals layered structures. In particular, we focus on the interlayer energy and the elastic constant of layered materials composed of two different 2-dimensional (2D) structures, that are important for novel solid lubricant and super-lubricant materials. We show that machine learning models can recapitulate results of computationally expansive approaches (i.e. density functional theory) with high accuracy.

37. ANI/EFP: Modeling Long-Range Interactions in ANI Neural Network with Effective Fragment Potentials.

Author

Haghiri S, Viquez Rojas C, Bhat S, Isayev O, and Slipchenko L

Abstract

Deep learning Neural Networks (NN) have been developed in the field of molecular modeling for the purpose of circumventing the high computational cost of quantum-mechanical calculations while rivaling their accuracies. Although these networks have found great success, they generally lack the ability to accurately describe long-range interactions, which makes them unusable for extended molecular systems. Herein, we provide a method for partially retraining the deep learning general-use neural network ANI, in which the long-range interactions are represented via atomic electrostatic potentials. The electrostatic potentials, generated with polarizable effective fragment potentials (EFP), are used as an additional input feature for the network. This new ANI/EFP network can predict solute-solvent interaction energies on a trained data set with a kcal/mol accuracy. It also shows promise in predicting the interaction energies of a solute in solvent environments that have not been included in a training data set. The proposed protocol can be taken as an example and further developed, leading to highly accurate and transferable neural network potentials capable of handling long-range interactions and extended molecular systems.

Published
2024
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38. Discovery of Crystallizable Organic Semiconductors with Machine Learning.

Author

Johnson HM, Gusev F, Dull JT, Seo Y, Priestley RD, Isayev O, and Rand BP

Abstract

Crystalline organic semiconductors are known to have improved charge carrier mobility and exciton diffusion length in comparison to their amorphous counterparts. Certain organic molecular thin films can be transitioned from initially prepared amorphous layers to large-scale crystalline films via abrupt thermal annealing. Ideally, these films crystallize as platelets with long-range-ordered domains on the scale of tens to hundreds of microns. However, other organic molecular thin films may instead crystallize as spherulites or resist crystallization entirely. Organic molecules that have the capability of transforming into a platelet morphology feature both high melting point ( T m ) and crystallization driving force ( ΔG c ). In this work, we employed machine learning (ML) to identify candidate organic materials with the potential to crystallize into platelets by estimating the aforementioned thermal properties. Six organic molecules identified by the ML algorithm were experimentally evaluated; three crystallized as platelets, one crystallized as a spherulite, and two resisted thin film crystallization. These results demonstrate a successful application of ML in the scope of predicting thermal properties of organic molecules and reinforce the principles of T m and ΔG c as metrics that aid in predicting the crystallization behavior of organic thin films.

Published
2024
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39. The seventh blind test of crystal structure prediction: structure ranking methods.

Author

Hunnisett LM, Francia N, Nyman J, Abraham NS, Aitipamula S, Alkhidir T, Almehairbi M, Anelli A, Anstine DM, Anthony JE, Arnold JE, Bahrami F, Bellucci MA, Beran GJO, Bhardwaj RM, Bianco R, Bis JA, Boese AD, Bramley J, Braun DE, Butler PWV, Cadden J, Carino S, Červinka C, Chan EJ, Chang C, Clarke SM, Coles SJ, Cook CJ, Cooper RI, Darden T, Day GM, Deng W, Dietrich H, DiPasquale A, Dhokale B, van Eijck BP, Elsegood MRJ, Firaha D, Fu W, Fukuzawa K, Galanakis N, Goto H, Greenwell C, Guo R, Harter J, Helfferich J, Hoja J, Hone J, Hong R, Hušák M, Ikabata Y, Isayev O, Ishaque O, Jain V, Jin Y, Jing A, Johnson ER, Jones I, Jose KVJ, Kabova EA, Keates A, Kelly PF, Klimeš J, Kostková V, Li H, Lin X, List A, Liu C, Liu YM, Liu Z, Lončarić I, Lubach JW, Ludík J, Maryewski AA, Marom N, Matsui H, Mattei A, Mayo RA, Melkumov JW, Mladineo B, Mohamed S, Momenzadeh Abardeh Z, Muddana HS, Nakayama N, Nayal KS, Neumann MA, Nikhar R, Obata S, O'Connor D, Oganov AR, Okuwaki K, Otero-de-la-Roza A, Parkin S, Parunov A, Podeszwa R, Price AJA, Price LS, Price SL, Probert MR, Pulido A, Ramteke GR, Rehman AU, Reutzel-Edens SM, Rogal J, Ross MJ, Rumson AF, Sadiq G, Saeed ZM, Salimi A, Sasikumar K, Sekharan S, Shankland K, Shi B, Shi X, Shinohara K, Skillman AG, Song H, Strasser N, van de Streek J, Sugden IJ, Sun G, Szalewicz K, Tan L, Tang K, Tarczynski F, Taylor CR, Tkatchenko A, Touš P, Tuckerman ME, Unzueta PA, Utsumi Y, Vogt-Maranto L, Weatherston J, Wilkinson LJ, Willacy RD, Wojtas L, Woollam GR, Yang Y, Yang Z, Yonemochi E, Yue X, Zeng Q, Zhou T, Zhou Y, Zubatyuk R, and Cole JC

Abstract

A seventh blind test of crystal structure prediction has been organized by the Cambridge Crystallographic Data Centre. The results are presented in two parts, with this second part focusing on methods for ranking crystal structures in order of stability. The exercise involved standardized sets of structures seeded from a range of structure generation methods. Participants from 22 groups applied several periodic DFT-D methods, machine learned potentials, force fields derived from empirical data or quantum chemical calculations, and various combinations of the above. In addition, one non-energy-based scoring function was used. Results showed that periodic DFT-D methods overall agreed with experimental data within expected error margins, while one machine learned model, applying system-specific AIMnet potentials, agreed with experiment in many cases demonstrating promise as an efficient alternative to DFT-based methods. For target XXXII, a consensus was reached across periodic DFT methods, with consistently high predicted energies of experimental forms relative to the global minimum (above 4 kJ mol -1 at both low and ambient temperatures) suggesting a more stable polymorph is likely not yet observed. The calculation of free energies at ambient temperatures offered improvement of predictions only in some cases (for targets XXVII and XXXI). Several avenues for future research have been suggested, highlighting the need for greater efficiency considering the vast amounts of resources utilized in many cases., (open access.)

Published
2024
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40. The seventh blind test of crystal structure prediction: structure generation methods.

Author

Hunnisett LM, Nyman J, Francia N, Abraham NS, Adjiman CS, Aitipamula S, Alkhidir T, Almehairbi M, Anelli A, Anstine DM, Anthony JE, Arnold JE, Bahrami F, Bellucci MA, Bhardwaj RM, Bier I, Bis JA, Boese AD, Bowskill DH, Bramley J, Brandenburg JG, Braun DE, Butler PWV, Cadden J, Carino S, Chan EJ, Chang C, Cheng B, Clarke SM, Coles SJ, Cooper RI, Couch R, Cuadrado R, Darden T, Day GM, Dietrich H, Ding Y, DiPasquale A, Dhokale B, van Eijck BP, Elsegood MRJ, Firaha D, Fu W, Fukuzawa K, Glover J, Goto H, Greenwell C, Guo R, Harter J, Helfferich J, Hofmann DWM, Hoja J, Hone J, Hong R, Hutchison G, Ikabata Y, Isayev O, Ishaque O, Jain V, Jin Y, Jing A, Johnson ER, Jones I, Jose KVJ, Kabova EA, Keates A, Kelly PF, Khakimov D, Konstantinopoulos S, Kuleshova LN, Li H, Lin X, List A, Liu C, Liu YM, Liu Z, Liu ZP, Lubach JW, Marom N, Maryewski AA, Matsui H, Mattei A, Mayo RA, Melkumov JW, Mohamed S, Momenzadeh Abardeh Z, Muddana HS, Nakayama N, Nayal KS, Neumann MA, Nikhar R, Obata S, O'Connor D, Oganov AR, Okuwaki K, Otero-de-la-Roza A, Pantelides CC, Parkin S, Pickard CJ, Pilia L, Pivina T, Podeszwa R, Price AJA, Price LS, Price SL, Probert MR, Pulido A, Ramteke GR, Rehman AU, Reutzel-Edens SM, Rogal J, Ross MJ, Rumson AF, Sadiq G, Saeed ZM, Salimi A, Salvalaglio M, Sanders de Almada L, Sasikumar K, Sekharan S, Shang C, Shankland K, Shinohara K, Shi B, Shi X, Skillman AG, Song H, Strasser N, van de Streek J, Sugden IJ, Sun G, Szalewicz K, Tan BI, Tan L, Tarczynski F, Taylor CR, Tkatchenko A, Tom R, Tuckerman ME, Utsumi Y, Vogt-Maranto L, Weatherston J, Wilkinson LJ, Willacy RD, Wojtas L, Woollam GR, Yang Z, Yonemochi E, Yue X, Zeng Q, Zhang Y, Zhou T, Zhou Y, Zubatyuk R, and Cole JC

Abstract

A seventh blind test of crystal structure prediction was organized by the Cambridge Crystallographic Data Centre featuring seven target systems of varying complexity: a silicon and iodine-containing molecule, a copper coordination complex, a near-rigid molecule, a cocrystal, a polymorphic small agrochemical, a highly flexible polymorphic drug candidate, and a polymorphic morpholine salt. In this first of two parts focusing on structure generation methods, many crystal structure prediction (CSP) methods performed well for the small but flexible agrochemical compound, successfully reproducing the experimentally observed crystal structures, while few groups were successful for the systems of higher complexity. A powder X-ray diffraction (PXRD) assisted exercise demonstrated the use of CSP in successfully determining a crystal structure from a low-quality PXRD pattern. The use of CSP in the prediction of likely cocrystal stoichiometry was also explored, demonstrating multiple possible approaches. Crystallographic disorder emerged as an important theme throughout the test as both a challenge for analysis and a major achievement where two groups blindly predicted the existence of disorder for the first time. Additionally, large-scale comparisons of the sets of predicted crystal structures also showed that some methods yield sets that largely contain the same crystal structures., (open access.)

Published
2024
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41. AQuaRef: Machine learning accelerated quantum refinement of protein structures.

Author

Zubatyuk R, Biczysko M, Ranasinghe K, Moriarty NW, Gokcan H, Kruse H, Poon BK, Adams PD, Waller MP, Roitberg AE, Isayev O, and Afonine PV

Abstract

Cryo-EM and X-ray crystallography provide crucial experimental data for obtaining atomic-detail models of biomacromolecules. Refining these models relies on library-based stereochemical restraints, which, in addition to being limited to known chemical entities, do not include meaningful noncovalent interactions relying solely on nonbonded repulsions. Quantum mechanical (QM) calculations could alleviate these issues but are too expensive for large molecules. We present a novel AI-enabled Quantum Refinement (AQuaRef) based on AIMNet2 neural network potential mimicking QM at substantially lower computational costs. By refining 41 cryo-EM and 30 X-ray structures, we show that this approach yields atomic models with superior geometric quality compared to standard techniques, while maintaining an equal or better fit to experimental data., Competing Interests: Competing Interests The authors declare no competing interests.

Published
2024
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42. Investigation of an anticancer activity of combination of interferon-alpha and gemcitabine on pancreatic cancer cells.

Author

Isayev O, Sokolova DV, Anisimova NY, Spirina TS, and Gasimov E

Subjects
Humans, Apoptosis drug effects, Carcinoma, Pancreatic Ductal drug therapy, Carcinoma, Pancreatic Ductal pathology, Carcinoma, Pancreatic Ductal metabolism, Cell Line, Tumor, Deoxycytidine analogs & derivatives, Deoxycytidine pharmacology, Deoxycytidine therapeutic use, Drug Synergism, Antineoplastic Combined Chemotherapy Protocols pharmacology, Antineoplastic Combined Chemotherapy Protocols therapeutic use, Cell Proliferation drug effects, Gemcitabine, Interferon-alpha pharmacology, Pancreatic Neoplasms drug therapy, Pancreatic Neoplasms pathology, Pancreatic Neoplasms metabolism
Abstract

Abstract: Pancreatic ductal adenocarcinoma (PDAC) is still a clinical challenge due to its deteriorated prognosis. Therefore, new combination chemotherapeutic approaches are of research interest. In this work, we attempted to characterize the effects of gemcitabine and interferon-alpha as well as the combination of both on the metabolic, pro-apoptotic, and proliferative activity of MiaPaca and Panc-1 cells. We showed that the exposure of both drugs in combination increases effectively the metabolic activity of cells of MiaPaca and Panc-1 cell lines compared to the monotherapies. Based on the data from the analysis of apoptosis, the underlying molecular effect of metabolic and proliferative inhibition is an increase in the number of cells in the early apoptosis. These data can be of interest in the context of future preclinical research., (Copyright © 2024 Copyright: © 2024 Journal of Cancer Research and Therapeutics.)

Published
2024
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43. Ultrastructural investigation of iron oxide nanoparticles accumulation in the liver of common carp (Cyprinus carpio Linnaeus, 1758).

Author

Hajiyeva A, Mamedov C, Gasimov E, Rzayev F, Isayev O, Khalilov R, Eftekhari A, and Zoroufchi Benis K

Subjects
Animals, Microscopy, Electron, Transmission, Magnetic Iron Oxide Nanoparticles toxicity, Carps metabolism, Liver metabolism, Liver drug effects, Liver ultrastructure, Water Pollutants, Chemical toxicity
Abstract

In recent years, the intensive production of nanoparticles with a wide application has led to their transfer to the environment, including the water ecosystem. The accumulation of nanoparticles in fish, causing various pathological changes in the host, raises certain concerns. In the current study, we investigated the penetration and bioaccumulation of Fe 3 O 4 nanoparticles, in the liver of common carp (Cyprinus carpio Linnaeus, 1758). Common carp juveniles were exposed to Fe 3 O 4 nanoparticles at concentrations of 10 and 100 mg. After 7 days, their livers were examined by light and transmission electron microscopes. Compared to normal fish's liver, after using a small concentration (10 mg) of nanoparticles, changes were observed in erythrocytes, hepatocytes, intracellular canaliculi, and bile ducts of the liver. At a high concentration (100 mg), the intensity of changes increased significantly. The liver's capsule was damaged, and a considerable number of hepatocytes were completely destroyed. Additionally, the walls of blood vessels and biliary ductule walls was notably disturbed. It was found that the intensity of pathologies occurring in the liver, increases proportionally with higher concentrations of nanoparticles. Confirmation via electron microscopic methods revealed that Fe 3 O 4 nanoparticles, when administered with food to common carp, enter the fish's liver through erythrocytes localized in the lumen of blood vessels. From there, they traverse through the endothelium of vessels, proceed to hepatocytes, including cytoplasmic organelles, intracellular canaliculi, biliary ductules, and eventually reach the bile ducts. Fe 3 O 4 nanoparticles in all structural elements of fish liver was up to 20 nm. Therefore, high concentrations of nanoparticles in the environment harms the bodies of aquatic organisms, including fish. The changes identified in the liver of common carp in the present study are valuable information in assessing possible risks to other components of the aquatic ecosystem and organisms., Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2024. Published by Elsevier B.V.)

Published
2024
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44. Machine Learning of Reactive Potentials.

Author

Yang Y, Zhang S, Ranasinghe KD, Isayev O, and Roitberg AE

Abstract

In the past two decades, machine learning potentials (MLPs) have driven significant developments in chemical, biological, and material sciences. The construction and training of MLPs enable fast and accurate simulations and analysis of thermodynamic and kinetic properties. This review focuses on the application of MLPs to reaction systems with consideration of bond breaking and formation. We review the development of MLP models, primarily with neural network and kernel-based algorithms, and recent applications of reactive MLPs (RMLPs) to systems at different scales. We show how RMLPs are constructed, how they speed up the calculation of reactive dynamics, and how they facilitate the study of reaction trajectories, reaction rates, free energy calculations, and many other calculations. Different data sampling strategies applied in building RMLPs are also discussed with a focus on how to collect structures for rare events and how to further improve their performance with active learning.

Published
2024
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46. Exploring the frontiers of condensed-phase chemistry with a general reactive machine learning potential.

Author

Zhang S, Makoś MZ, Jadrich RB, Kraka E, Barros K, Nebgen BT, Tretiak S, Isayev O, Lubbers N, Messerly RA, and Smith JS

Abstract

Atomistic simulation has a broad range of applications from drug design to materials discovery. Machine learning interatomic potentials (MLIPs) have become an efficient alternative to computationally expensive ab initio simulations. For this reason, chemistry and materials science would greatly benefit from a general reactive MLIP, that is, an MLIP that is applicable to a broad range of reactive chemistry without the need for refitting. Here we develop a general reactive MLIP (ANI-1xnr) through automated sampling of condensed-phase reactions. ANI-1xnr is then applied to study five distinct systems: carbon solid-phase nucleation, graphene ring formation from acetylene, biofuel additives, combustion of methane and the spontaneous formation of glycine from early earth small molecules. In all studies, ANI-1xnr closely matches experiment (when available) and/or previous studies using traditional model chemistry methods. As such, ANI-1xnr proves to be a highly general reactive MLIP for C, H, N and O elements in the condensed phase, enabling high-throughput in silico reactive chemistry experimentation., (© 2024. This is a U.S. Government work and not under copyright protection in the US; foreign copyright protection may apply.)

Published
2024
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47. In silico screening of LRRK2 WDR domain inhibitors using deep docking and free energy simulations.

Author

Gutkin E, Gusev F, Gentile F, Ban F, Koby SB, Narangoda C, Isayev O, Cherkasov A, and Kurnikova MG

Abstract

The Critical Assessment of Computational Hit-Finding Experiments (CACHE) Challenge series is focused on identifying small molecule inhibitors of protein targets using computational methods. Each challenge contains two phases, hit-finding and follow-up optimization, each of which is followed by experimental validation of the computational predictions. For the CACHE Challenge #1, the Leucine-Rich Repeat Kinase 2 (LRRK2) WD40 Repeat (WDR) domain was selected as the target for in silico hit-finding and optimization. Mutations in LRRK2 are the most common genetic cause of the familial form of Parkinson's disease. The LRRK2 WDR domain is an understudied drug target with no known molecular inhibitors. Herein we detail the first phase of our winning submission to the CACHE Challenge #1. We developed a framework for the high-throughput structure-based virtual screening of a chemically diverse small molecule space. Hit identification was performed using the large-scale Deep Docking (DD) protocol followed by absolute binding free energy (ABFE) simulations. ABFEs were computed using an automated molecular dynamics (MD)-based thermodynamic integration (TI) approach. 4.1 billion ligands from Enamine REAL were screened with DD followed by ABFEs computed by MD TI for 793 ligands. 76 ligands were prioritized for experimental validation, with 59 compounds successfully synthesized and 5 compounds identified as hits, yielding a 8.5% hit rate. Our results demonstrate the efficacy of the combined DD and ABFE approaches for hit identification for a target with no previously known hits. This approach is widely applicable for the efficient screening of ultra-large chemical libraries as well as rigorous protein-ligand binding affinity estimation leveraging modern computational resources., Competing Interests: There are no conflicts to declare., (This journal is © The Royal Society of Chemistry.)

Published
2024
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48. MLatom 3: A Platform for Machine Learning-Enhanced Computational Chemistry Simulations and Workflows.

Author

Dral PO, Ge F, Hou YF, Zheng P, Chen Y, Barbatti M, Isayev O, Wang C, Xue BX, Pinheiro M Jr, Su Y, Dai Y, Chen Y, Zhang L, Zhang S, Ullah A, Zhang Q, and Ou Y

Abstract

Machine learning (ML) is increasingly becoming a common tool in computational chemistry. At the same time, the rapid development of ML methods requires a flexible software framework for designing custom workflows. MLatom 3 is a program package designed to leverage the power of ML to enhance typical computational chemistry simulations and to create complex workflows. This open-source package provides plenty of choice to the users who can run simulations with the command-line options, input files, or with scripts using MLatom as a Python package, both on their computers and on the online XACS cloud computing service at XACScloud.com. Computational chemists can calculate energies and thermochemical properties, optimize geometries, run molecular and quantum dynamics, and simulate (ro)vibrational, one-photon UV/vis absorption, and two-photon absorption spectra with ML, quantum mechanical, and combined models. The users can choose from an extensive library of methods containing pretrained ML models and quantum mechanical approximations such as AIQM1 approaching coupled-cluster accuracy. The developers can build their own models using various ML algorithms. The great flexibility of MLatom is largely due to the extensive use of the interfaces to many state-of-the-art software packages and libraries.

Published
2024
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49. Integrating QSAR modelling and deep learning in drug discovery: the emergence of deep QSAR.

Author

Tropsha A, Isayev O, Varnek A, Schneider G, and Cherkasov A

Subjects
Humans, Artificial Intelligence, Computing Methodologies, Quantum Theory, Drug Discovery methods, Drug Design, Quantitative Structure-Activity Relationship, Deep Learning
Abstract

Quantitative structure-activity relationship (QSAR) modelling, an approach that was introduced 60 years ago, is widely used in computer-aided drug design. In recent years, progress in artificial intelligence techniques, such as deep learning, the rapid growth of databases of molecules for virtual screening and dramatic improvements in computational power have supported the emergence of a new field of QSAR applications that we term 'deep QSAR'. Marking a decade from the pioneering applications of deep QSAR to tasks involved in small-molecule drug discovery, we herein describe key advances in the field, including deep generative and reinforcement learning approaches in molecular design, deep learning models for synthetic planning and the application of deep QSAR models in structure-based virtual screening. We also reflect on the emergence of quantum computing, which promises to further accelerate deep QSAR applications and the need for open-source and democratized resources to support computer-aided drug design., (© 2023. Springer Nature Limited.)

Published
2024
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50. A community effort in SARS-CoV-2 drug discovery.

Author

Schimunek J, Seidl P, Elez K, Hempel T, Le T, Noé F, Olsson S, Raich L, Winter R, Gokcan H, Gusev F, Gutkin EM, Isayev O, Kurnikova MG, Narangoda CH, Zubatyuk R, Bosko IP, Furs KV, Karpenko AD, Kornoushenko YV, Shuldau M, Yushkevich A, Benabderrahmane MB, Bousquet-Melou P, Bureau R, Charton B, Cirou BC, Gil G, Allen WJ, Sirimulla S, Watowich S, Antonopoulos N, Epitropakis N, Krasoulis A, Itsikalis V, Theodorakis S, Kozlovskii I, Maliutin A, Medvedev A, Popov P, Zaretckii M, Eghbal-Zadeh H, Halmich C, Hochreiter S, Mayr A, Ruch P, Widrich M, Berenger F, Kumar A, Yamanishi Y, Zhang KYJ, Bengio E, Bengio Y, Jain MJ, Korablyov M, Liu CH, Marcou G, Glaab E, Barnsley K, Iyengar SM, Ondrechen MJ, Haupt VJ, Kaiser F, Schroeder M, Pugliese L, Albani S, Athanasiou C, Beccari A, Carloni P, D'Arrigo G, Gianquinto E, Goßen J, Hanke A, Joseph BP, Kokh DB, Kovachka S, Manelfi C, Mukherjee G, Muñiz-Chicharro A, Musiani F, Nunes-Alves A, Paiardi G, Rossetti G, Sadiq SK, Spyrakis F, Talarico C, Tsengenes A, Wade RC, Copeland C, Gaiser J, Olson DR, Roy A, Venkatraman V, Wheeler TJ, Arthanari H, Blaschitz K, Cespugli M, Durmaz V, Fackeldey K, Fischer PD, Gorgulla C, Gruber C, Gruber K, Hetmann M, Kinney JE, Padmanabha Das KM, Pandita S, Singh A, Steinkellner G, Tesseyre G, Wagner G, Wang ZF, Yust RJ, Druzhilovskiy DS, Filimonov DA, Pogodin PV, Poroikov V, Rudik AV, Stolbov LA, Veselovsky AV, De Rosa M, De Simone G, Gulotta MR, Lombino J, Mekni N, Perricone U, Casini A, Embree A, Gordon DB, Lei D, Pratt K, Voigt CA, Chen KY, Jacob Y, Krischuns T, Lafaye P, Zettor A, Rodríguez ML, White KM, Fearon D, Von Delft F, Walsh MA, Horvath D, Brooks CL 3rd, Falsafi B, Ford B, García-Sastre A, Yup Lee S, Naffakh N, Varnek A, Klambauer G, and Hermans TM

Subjects
Humans, Pandemics, Biological Assay, Drug Discovery, SARS-CoV-2, COVID-19
Abstract

The COVID-19 pandemic continues to pose a substantial threat to human lives and is likely to do so for years to come. Despite the availability of vaccines, searching for efficient small-molecule drugs that are widely available, including in low- and middle-income countries, is an ongoing challenge. In this work, we report the results of an open science community effort, the "Billion molecules against COVID-19 challenge", to identify small-molecule inhibitors against SARS-CoV-2 or relevant human receptors. Participating teams used a wide variety of computational methods to screen a minimum of 1 billion virtual molecules against 6 protein targets. Overall, 31 teams participated, and they suggested a total of 639,024 molecules, which were subsequently ranked to find 'consensus compounds'. The organizing team coordinated with various contract research organizations (CROs) and collaborating institutions to synthesize and test 878 compounds for biological activity against proteases (Nsp5, Nsp3, TMPRSS2), nucleocapsid N, RdRP (only the Nsp12 domain), and (alpha) spike protein S. Overall, 27 compounds with weak inhibition/binding were experimentally identified by binding-, cleavage-, and/or viral suppression assays and are presented here. Open science approaches such as the one presented here contribute to the knowledge base of future drug discovery efforts in finding better SARS-CoV-2 treatments., (© 2023 The Authors. Molecular Informatics published by Wiley-VCH GmbH.)

Published
2024
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