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2. Fair-Net: A Network Architecture For Reducing Performance Disparity Between Identifiable Sub-Populations

Author

Datta, Arghya and Swamidass, S. Joshua

Subjects
Computer Science - Machine Learning, Computer Science - Artificial Intelligence, Computer Science - Computers and Society
Abstract

In real world datasets, particular groups are under-represented, much rarer than others, and machine learning classifiers will often preform worse on under-represented populations. This problem is aggravated across many domains where datasets are class imbalanced, with a minority class far rarer than the majority class. Naive approaches to handle under-representation and class imbalance include training sub-population specific classifiers that handle class imbalance or training a global classifier that overlooks sub-population disparities and aims to achieve high overall accuracy by handling class imbalance. In this study, we find that these approaches are vulnerable in class imbalanced datasets with minority sub-populations. We introduced Fair-Net, a branched multitask neural network architecture that improves both classification accuracy and probability calibration across identifiable sub-populations in class imbalanced datasets. Fair-Nets is a straightforward extension to the output layer and error function of a network, so can be incorporated in far more complex architectures. Empirical studies with three real world benchmark datasets demonstrate that Fair-Net improves classification and calibration performance, substantially reducing performance disparity between gender and racial sub-populations.

Published
2021
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7. Deep learning long-range information in undirected graphs with wave networks

Author

Matlock, Matthew K., Datta, Arghya, Dang, Na Le, Jiang, Kevin, and Swamidass, S. Joshua

Subjects
Computer Science - Machine Learning, Statistics - Machine Learning
Abstract

Graph algorithms are key tools in many fields of science and technology. Some of these algorithms depend on propagating information between distant nodes in a graph. Recently, there have been a number of deep learning architectures proposed to learn on undirected graphs. However, most of these architectures aggregate information in the local neighborhood of a node, and therefore they may not be capable of efficiently propagating long-range information. To solve this problem we examine a recently proposed architecture, wave, which propagates information back and forth across an undirected graph in waves of nonlinear computation. We compare wave to graph convolution, an architecture based on local aggregation, and find that wave learns three different graph-based tasks with greater efficiency and accuracy. These three tasks include (1) labeling a path connecting two nodes in a graph, (2) solving a maze presented as an image, and (3) computing voltages in a circuit. These tasks range from trivial to very difficult, but wave can extrapolate from small training examples to much larger testing examples. These results show that wave may be able to efficiently solve a wide range of problems that require long-range information propagation across undirected graphs. An implementation of the wave network, and example code for the maze problem are included in the tflon deep learning toolkit (https://bitbucket.org/mkmatlock/tflon).

Published
2018

8. A survey of current trends in computational drug repositioning

Author

Li, Jiao, Zheng, Si, Chen, Bin, Butte, Atul J, Swamidass, S Joshua, and Lu, Zhiyong

Subjects
Networking and Information Technology R&D (NITRD), Development of treatments and therapeutic interventions, 5.1 Pharmaceuticals, Generic health relevance, Good Health and Well Being, Computational Biology, Data Mining, Drug Combinations, Drug Repositioning, Genomics, Humans, Machine Learning, Molecular Structure, Phenotype, Surveys and Questionnaires, computational drug repositioning, integrative strategies, genome, phenome, chemical structure, drug combination, prediction validation, Biochemistry and Cell Biology, Computation Theory and Mathematics, Other Information and Computing Sciences, Bioinformatics
Abstract

Computational drug repositioning or repurposing is a promising and efficient tool for discovering new uses from existing drugs and holds the great potential for precision medicine in the age of big data. The explosive growth of large-scale genomic and phenotypic data, as well as data of small molecular compounds with granted regulatory approval, is enabling new developments for computational repositioning. To achieve the shortest path toward new drug indications, advanced data processing and analysis strategies are critical for making sense of these heterogeneous molecular measurements. In this review, we show recent advancements in the critical areas of computational drug repositioning from multiple aspects. First, we summarize available data sources and the corresponding computational repositioning strategies. Second, we characterize the commonly used computational techniques. Third, we discuss validation strategies for repositioning studies, including both computational and experimental methods. Finally, we highlight potential opportunities and use-cases, including a few target areas such as cancers. We conclude with a brief discussion of the remaining challenges in computational drug repositioning.

Published
2016

9. Standard operating procedure for somatic variant refinement of sequencing data with paired tumor and normal samples

Author

Barnell, Erica K., Ronning, Peter, Campbell, Katie M., Krysiak, Kilannin, Ainscough, Benjamin J., Sheta, Lana M., Pema, Shahil P., Schmidt, Alina D., Richters, Megan, Cotto, Kelsy C., Danos, Arpad M., Ramirez, Cody, Skidmore, Zachary L., Spies, Nicholas C., Hundal, Jasreet, Sediqzad, Malik S., Kunisaki, Jason, Gomez, Felicia, Trani, Lee, Matlock, Matthew, Wagner, Alex H., Swamidass, S. Joshua, Griffith, Malachi, and Griffith, Obi L.

Published
2019
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10. Large scale study of multiple-molecule queries

Author

Nasr, Ramzi J, Swamidass, S Joshua, and Baldi, Pierre F

Abstract

Background: In ligand-based screening, as well as in other chemoinformatics applications, one seeks to effectively search large repositories of molecules in order to retrieve molecules that are similar typically to a single molecule lead. However, in some case, multiple molecules from the same family are available to seed the query and search for other members of the same family. Multiple-molecule query methods have been less studied than single-molecule query methods. Furthermore, the previous studies have relied on proprietary data and sometimes have not used proper cross-validation methods to assess the results. In contrast, here we develop and compare multiple-molecule query methods using several large publicly available data sets and background. We also create a framework based on a strict cross-validation protocol to allow unbiased benchmarking for direct comparison in future studies across several performance metrics. Results: Fourteen different multiple-molecule query methods were defined and benchmarked using: (1) 41 publicly available data sets of related molecules with similar biological activity; and (2) publicly available background data sets consisting of up to 175,000 molecules randomly extracted from the ChemDB database and other sources. Eight of the fourteen methods were parameter free, and six of them fit one or two free parameters to the data using a careful cross-validation protocol. All the methods were assessed and compared for their ability to retrieve members of the same family against the background data set by using several performance metrics including the Area Under the Accumulation Curve (AUAC), Area Under the Curve (AUC), F1-measure, and BEDROC metrics. Consistent with the previous literature, the best parameter-free methods are the MAX-SIM and MIN-RANK methods, which score a molecule to a family by the maximum similarity, or minimum ranking, obtained across the family. One new parameterized method introduced in this study and two previously defined methods, the Exponential Tanimoto Discriminant (ETD), the Tanimoto Power Discriminant (TPD), and the Binary Kernel Discriminant (BKD), outperform most other methods but are more complex, requiring one or two parameters to be fit to the data. Conclusion: Fourteen methods for multiple-molecule querying of chemical databases, including novel methods, (ETD) and (TPD), are validated using publicly available data sets, standard cross-validation protocols, and established metrics. The best results are obtained with ETD, TPD, BKD, MAX-SIM, and MIN-RANK. These results can be replicated and compared with the results of future studies using data freely downloadable from http://cdb.ics.uci.edu/.

Published
2009

12. The potential of artificial intelligence-based applications in kidney pathology

Author

Bülow, Roman D., Marsh, Jon N., Swamidass, S. Joshua, Gaut, Joseph P., and Boor, Peter

Subjects
Artificial Intelligence, Computers, Nephrology, Image Processing, Computer-Assisted, Internal Medicine, Humans, Reproducibility of Results, Kidney, Article
Abstract

PURPOSE OF REVIEW: The field of pathology is currently undergoing a significant transformation from traditional glass slides to a digital format dependent on whole slide imaging. Transitioning from glass to digital has opened the field to development and application of image analysis technology, commonly deep learning methods (artificial intelligence) to assist pathologists with tissue examination. Nephropathology is poised to leverage this technology to improve precision, accuracy, and efficiency in clinical practice. RECENT FINDINGS: Through a multidisciplinary approach, nephropathologists and computer scientists have made significant recent advances in developing artificial intelligence technology to identify histological structures within whole slide images (segmentation), quantification of histologic structures, prediction of clinical outcomes, and classifying disease. Virtual staining of tissue and automation of electron microscopy imaging are emerging applications with particular significance for nephropathology. SUMMARY: Artificial intelligence applied to image analysis in nephropathology has potential to transform the field by improving diagnostic accuracy and reproducibility, efficiency, and prognostic power. Reimbursement, demonstration of clinical utility, and seamless workflow integration are essential to widespread adoption.

Published
2022

14. Novel advances in biotransformation and bioactivation research – 2020 year in review

Author

Khojasteh, S Cyrus, Argikar, Upendra A, Driscoll, James P, Heck, Carley JS, King, Lloyd, Jackson, Klarissa D, Jian, Wenying, Kalgutkar, Amit S, Miller, Grover P, Kramlinger, Valerie, Rietjens, Ivonne MCM, Teitelbaum, Aaron M, Wang, Kai, Wei, Cong, Johnson, Benjamin M, Shu, Yue-Zhong, Zhuo, Xiaoliang, Meanwell, Nicholas A, Cerny, Matthew A, Obach, R Scott, Sharma, Raman, Spracklin, Douglas K, Walker, Gregory S, Goracci, Laura, Desantis, Jenny, Valeri, Aurora, Castellani, Beatrice, Eleuteri, Michela, Cruciani, Gabriele, Lall, Manjinder S, Bassyouni, Asser, Bradow, James, Brown, Maria, Bundesmann, Mark, Chen, Jinshan, Ciszewski, Gregory, Hagen, Anne E, Hyek, Dennis, Jenkinson, Stephen, Liu, Bo, Pan, Senliang, Reilly, Usa, Sach, Neal, Smaltz, Daniel J, Starr, Jeremy, Wagenaar, Melissa, de Bruyn Kops, Christina, Sicho, Martin, Mazzolari, Angelica, Kirchmair, Johannes, Korprasertthaworn, Porntipa, Chau, Nuy, Nair, Pramod C, Rowland, Andrew, Miners, John O, Wrobleski, Stephen T, Moslin, Ryan, Lin, Shuqun, Zhang, Yanlei, Spergel, Steven, Kempson, James, Tokarski, John S, Strnad, Joann, Zupa-Fernandez, Adriana, Cheng, Lihong, Shuster, David, Gillooly, Kathleen, Yang, Xiaoxia, Heimrich, Elizabeth, McIntyre, Kim W, Chaudhry, Charu, Khan, Javed, Ruzanov, Max, Tredup, Jeffrey, Mulligan, Dawn, Xie, Dianlin, Sun, Huadong, Huang, Christine, D'Arienzo, Celia, Aranibar, Nelly, Chiney, Manoj, Chimalakonda, Anjaneya, Pitts, William J, Lombardo, Louis, Carter, Percy H, Burke, James R, Weinstein, David S, Li, Jing, Liu, Ju, Enders, Jennifer, Arciprete, Michael, Tran, Chris, Aluri, Krishna, Guan, Li-Hua, O'Shea, Jonathan, Bisbe, Anna, Charisse, Klaus, Zlatev, Ivan, Najarian, Diana, Xu, Yuanxin, Kim, Jaeah, El Zahar, Noha M, Bartlett, Michael G, Katyayan, Kishore, Yi, Ping, Monk, Scott, Cassidy, Kenneth, Takahashi, Ryan H, Grandner, Jessica M, Bobba, Sudheer, Liu, Yanzhou, Beroza, Paul, Zhang, Donglu, Ma, Shuguang, Post, Noah, Yu, Rosie, Greenlee, Sarah, Gaus, Hans, Hurh, Eunju, Matson, John, Wang, Yanfeng, Tajima, Yuya, Toyoda, Takeshi, Hirayama, Yuichiro, Matsushita, Kohei, Yamada, Takanori, Ogawa, Kumiko, Watanabe, Kenji, Takamura-Enya, Takeji, Totsuka, Yukari, Wakabayashi, Keiji, Miyoshi, Noriyuki, Zhang, Jiayin, Chan, Chi-Kong, Ham, Yat-Hing, Chan, Wan, Schleiff, Mary Alexandra, Flynn, Noah R, Payakachat, Sasin, Schleiff, Benjamin Mark, Pinson, Anna O, Province, Dennis W, Swamidass, S Joshua, Boysen, Gunnar, Nardone-White, Dasean T, Bissada, Jennifer E, Abouda, Arsany A, Zhang, Zhuming, Connolly, Peter J, Lim, Heng Keang, Pande, Vineet, Meerpoel, Lieven, Teleha, Christopher, Branch, Jonathan R, Ondrus, Janine, Hickson, Ian, Bush, Tammy, Luistro, Leopoldo, Packman, Kathryn, Bischoff, James R, Ibrahim, Salam, Parrett, Christopher, Chong, Yolanda, Gottardis, Marco M, Bignan, Gilles, Mulder, Teresa, Bobba, Sudder, Johnson, Kevin M, Wang, Wei, Zhang, Chenghong, Cai, Jingwei, Choo, Edna F, Crawford, James J, Landry, Matthew L, Chen, Huifen, Kenny, Jane R, Lee, Wendy, Young, Wendy B, Geib, Timon, Thulasingam, Madhuranayaki, Haeggstrom, Jesper Z, Sleno, Lekha, Monroe, James J, Tanis, Keith Q, Podtelezhnikov, Alexei A, Nguyen, Truyen, Machotka, Sam V, Lynch, Donna, Evers, Raymond, Palamanda, Jairam, Miller, Randy R, Pippert, Todd, Cabalu, Tamara D, Johnson, Timothy E, Aslamkhan, Amy G, Kang, Wen, Tamburino, Alex M, Mitra, Kaushik, Agrawal, Nancy GB, and Sistare, Frank D

Subjects
METABOLISM, Toxicology, 030226 pharmacology & pharmacy, 03 medical and health sciences, nonp450 enzymes, 0302 clinical medicine, p450 enzymes, Biotransformation, INTRINSIC CLEARANCE, Political science, MASS-BALANCE, Humans, Pharmacology (medical), Pharmacology & Pharmacy, P450 Enzymes, General Pharmacology, Toxicology and Pharmaceutics, ANTISENSE OLIGONUCLEOTIDE, Toxicologie, VLAG, ARISTOLOCHIC ACID, Drug metabolism, Science & Technology, WIMEK, bioactivation, IDENTIFICATION, Year in review, IN-VITRO, NONHEPATOTOXIC DRUGS, Drug metabolizing enzymes, Drug development, 030220 oncology & carcinogenesis, COVALENT BINDING DATA, Engineering ethics, biotransformation, LIVER-MICROSOMES, Life Sciences & Biomedicine
Abstract

This annual review is the sixth of its kind since 2016 (see references). Our objective is to explore and share articles which we deem influential and significant in the field of biotransformation and bioactivation. These fields are constantly evolving with new molecular structures and discoveries of corresponding pathways for metabolism that impact relevant drug development with respect to efficacy and safety. Based on the selected articles, we created three sections: (1) drug design, (2) metabolites and drug metabolizing enzymes, and (3) bioactivation and safety (Table 1). Unlike in years past, more biotransformation experts have joined and contributed to this effort while striving to maintain a balance of authors from academic and industry settings.[Table: see text]. ispartof: DRUG METABOLISM REVIEWS vol:53 issue:3 pages:384-433 ispartof: location:England status: published

Published
2021

21. A U-Turn on Adam and Eve

Author

Swamidass, S. Joshua

Abstract

BioLogos updates their scientific position on Adam and Eve. This is an important step forward and a key milestone in the conversation.

Published
2021
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22. Deep Learning Coordinate-Free Quantum Chemistry

Author

Matlock, Matthew K., primary, Hoffman, Max, additional, Dang, Na Le, additional, Folmsbee, Dakota L., additional, Langkamp, Luke A., additional, Hutchison, Geoffrey R., additional, Kumar, Neeraj, additional, Sarullo, Kathryn, additional, and Swamidass, S. Joshua, additional

Published
2021
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40. Evolution and Functional Information

Author

Matlock, Matthew K. and Swamidass, S. Joshua

Subjects
0303 health sciences, 03 medical and health sciences, Sequence, 0302 clinical medicine, Protein sequencing, Computer science, 030220 oncology & carcinogenesis, Selection (linguistics), Mutual information, Function (mathematics), Argument (linguistics), Algorithm, 030304 developmental biology
Abstract

“Functional Information”---estimated from the mutual information of protein sequence alignments---has been proposed as a reliable way of estimating the number of proteins with a specified function and the consequent difficulty of evolving a new function. The fantastic rarity of functional proteins computed by this approach emboldens some to argue that evolution is impossible. Random searches, it seems, would have no hope of finding new functions. Here, we use simulations to demonstrate that sequence alignments are a poor estimate of functional information. The mutual information of sequence alignments fantastically underestimates of the true number of functional proteins. In addition to functional constraints, mutual information is also strongly influenced by a family9s history, mutational bias, and selection. Regardless, even if functional information could be reliably calculated, it tells us nothing about the difficulty of evolving new functions, because it does not estimate the distance between a new function and existing functions. Moreover, the pervasive observation of multifunctional proteins suggests that functions are actually very close to one another and abundant. Multifunctional proteins would be impossible if the FI argument against evolution were true.

Published
2017

41. Opportunities and obstacles for deep learning in biology and medicine

Author

Ching, Travers, primary, Himmelstein, Daniel S., additional, Beaulieu-Jones, Brett K., additional, Kalinin, Alexandr A., additional, Do, Brian T., additional, Way, Gregory P., additional, Ferrero, Enrico, additional, Agapow, Paul-Michael, additional, Zietz, Michael, additional, Hoffman, Michael M., additional, Xie, Wei, additional, Rosen, Gail L., additional, Lengerich, Benjamin J., additional, Israeli, Johnny, additional, Lanchantin, Jack, additional, Woloszynek, Stephen, additional, Carpenter, Anne E., additional, Shrikumar, Avanti, additional, Xu, Jinbo, additional, Cofer, Evan M., additional, Lavender, Christopher A., additional, Turaga, Srinivas C., additional, Alexandari, Amr M., additional, Lu, Zhiyong, additional, Harris, David J., additional, DeCaprio, Dave, additional, Qi, Yanjun, additional, Kundaje, Anshul, additional, Peng, Yifan, additional, Wiley, Laura K., additional, Segler, Marwin H. S., additional, Boca, Simina M., additional, Swamidass, S. Joshua, additional, Huang, Austin, additional, Gitter, Anthony, additional, and Greene, Casey S., additional

Published
2018
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43. Open Source Drug Discovery with the Malaria Box Compound Collection for Neglected Diseases and Beyond

Author

Institute for Medical Engineering and Science, Harvard University--MIT Division of Health Sciences and Technology, Bhatia, Sangeeta N, Van Voorhis, Wesley C., Adams, John H., Adelfio, Roberto, Ahyong, Vida, Akabas, Myles H., Alano, Pietro, Alday, Aintzane, Yesmalie, Alemán Resto, Alsibaee, Aishah, Alzualde, Ainhoa, Andrews, Katherine T., Avery, Simon V., Avery, Vicky M., Ayong, Lawrence, Baker, Mark, Baker, Stephen, Ben Mamoun, Choukri, Bickle, Quentin, Bounaadja, Lotfi, Bowling, Tana, Bosch, Jürgen, Boucher, Lauren E., Boyom, Fabrice F., Brea, Jose, Brennan, Marian, Burton, Audrey, Caffrey, Conor R., Camarda, Grazia, Carrasquilla, Manuela, Carter, Dee, Cassera, Maria Belen, Ken, Chih-Chien Cheng, Chindaudomsate, Worathad, Chubb, Anthony, Colon, Beatrice L., Colón-López, Daisy D., Corbett, Yolanda, Crowther, Gregory J., Cowan, Noemi, D’Alessandro, Sarah, Le Dang, Na, Delves, Michael, DeRisi, Joseph L., Du, Alan Y., Duffy, Sandra, Abd El-Salam El-Sayed, Shimaa, Ferdig, Michael T., Fernández Robledo, José A., Fidock, David A., Florent, Isabelle, Fokou, Patrick V. T., Galstian, Ani, Gamo, Francisco Javier, Gokool, Suzanne, Gold, Ben, Golub, Todd, Goldgof, Gregory M., Guha, Rajarshi, Guiguemde, W. Armand, Gural, Nil, Guy, R. Kiplin, Hansen, Michael A. E., Hanson, Kirsten K., Hemphill, Andrew, Hooft van Huijsduijnen, Rob, Horii, Takaaki, Horrocks, Paul, Hughes, Tyler B., Huston, Christopher, Igarashi, Ikuo, Ingram-Sieber, Katrin, Itoe, Maurice A., Jadhav, Ajit, Naranuntarat Jensen, Amornrat, Jensen, Laran T., Jiang, Rays H. Y., Kaiser, Annette, Keiser, Jennifer, Ketas, Thomas, Kicka, Sebastien, Kim, Sunyoung, Kirk, Kiaran, Kumar, Vidya P., Kyle, Dennis E., Lafuente, Maria Jose, Landfear, Scott, Lee, Nathan, Lee, Sukjun, Lehane, Adele M., Li, Fengwu, Little, David, Liu, Liqiong, Llinás, Manuel, Loza, Maria I., Lubar, Aristea, Lucantoni, Leonardo, Lucet, Isabelle, Maes, Louis, Mancama, Dalu, Mansour, Nuha R., March, Sandra, McGowan, Sheena, Medina Vera, Iset, Meister, Stephan, Mercer, Luke, Mestres, Jordi, Mfopa, Alvine N., Misra, Raj N., Moon, Seunghyun, Moore, John P., Morais Rodrigues da Costa, Francielly, Müller, Joachim, Muriana, Arantza, Nakazawa Hewitt, Stephen, Nare, Bakela, Nathan, Carl, Narraidoo, Nathalie, Nawaratna, Sujeevi, Ojo, Kayode K., Ortiz, Diana, Panic, Gordana, Papadatos, George, Parapini, Silvia, Patra, Kailash, Pham, Ngoc, Prats, Sarah, Plouffe, David M., Poulsen, Sally-Ann, Pradhan, Anupam, Quevedo, Celia, Quinn, Ronald J., Rice, Christopher A., Ruecker, Andrea, St. Onge, Robert, Salgado Ferreira, Rafaela, Samra, Jasmeet, Robinett, Natalie G., Schlecht, Ulrich, Schmitt, Marjorie, Silva Villela, Filipe, Silvestrini, Francesco, Sinden, Robert, Smith, Dennis A., Soldati, Thierry, Spitzmüller, Andreas, Stamm, Serge Maximilian, Sullivan, David J., Sullivan, William, Suresh, Sundari, Suzuki, Brian M., Suzuki, Yo, Swamidass, S. Joshua, Taramelli, Donatella, Tchokouaha, Lauve R. Y., Theron, Anjo, Thomas, David, Tonissen, Kathryn F., Townson, Simon, Tripathi, Abhai K., Trofimov, Valentin, Udenze, Kenneth O., Ullah, Imran, Vallieres, Cindy, Vigil, Edgar, Vinetz, Joseph M., Voong Vinh, Phat, Vu, Hoan, Watanabe, Nao-aki, Weatherby, Kate, White, Pamela M., Wilks, Andrew F., Winzeler, Elizabeth A., Wojcik, Edward, Wree, Melanie, Wu, Wesley, Yokoyama, Naoaki, Zollo, Paul H. A., Abla, Nada, Blasco, Benjamin, Burrows, Jeremy, Laleu, Benoît, Leroy, Didier, Spangenberg, Thomas, Wells, Timothy, Willis, Paul A., Institute for Medical Engineering and Science, Harvard University--MIT Division of Health Sciences and Technology, Bhatia, Sangeeta N, Van Voorhis, Wesley C., Adams, John H., Adelfio, Roberto, Ahyong, Vida, Akabas, Myles H., Alano, Pietro, Alday, Aintzane, Yesmalie, Alemán Resto, Alsibaee, Aishah, Alzualde, Ainhoa, Andrews, Katherine T., Avery, Simon V., Avery, Vicky M., Ayong, Lawrence, Baker, Mark, Baker, Stephen, Ben Mamoun, Choukri, Bickle, Quentin, Bounaadja, Lotfi, Bowling, Tana, Bosch, Jürgen, Boucher, Lauren E., Boyom, Fabrice F., Brea, Jose, Brennan, Marian, Burton, Audrey, Caffrey, Conor R., Camarda, Grazia, Carrasquilla, Manuela, Carter, Dee, Cassera, Maria Belen, Ken, Chih-Chien Cheng, Chindaudomsate, Worathad, Chubb, Anthony, Colon, Beatrice L., Colón-López, Daisy D., Corbett, Yolanda, Crowther, Gregory J., Cowan, Noemi, D’Alessandro, Sarah, Le Dang, Na, Delves, Michael, DeRisi, Joseph L., Du, Alan Y., Duffy, Sandra, Abd El-Salam El-Sayed, Shimaa, Ferdig, Michael T., Fernández Robledo, José A., Fidock, David A., Florent, Isabelle, Fokou, Patrick V. T., Galstian, Ani, Gamo, Francisco Javier, Gokool, Suzanne, Gold, Ben, Golub, Todd, Goldgof, Gregory M., Guha, Rajarshi, Guiguemde, W. Armand, Gural, Nil, Guy, R. Kiplin, Hansen, Michael A. E., Hanson, Kirsten K., Hemphill, Andrew, Hooft van Huijsduijnen, Rob, Horii, Takaaki, Horrocks, Paul, Hughes, Tyler B., Huston, Christopher, Igarashi, Ikuo, Ingram-Sieber, Katrin, Itoe, Maurice A., Jadhav, Ajit, Naranuntarat Jensen, Amornrat, Jensen, Laran T., Jiang, Rays H. Y., Kaiser, Annette, Keiser, Jennifer, Ketas, Thomas, Kicka, Sebastien, Kim, Sunyoung, Kirk, Kiaran, Kumar, Vidya P., Kyle, Dennis E., Lafuente, Maria Jose, Landfear, Scott, Lee, Nathan, Lee, Sukjun, Lehane, Adele M., Li, Fengwu, Little, David, Liu, Liqiong, Llinás, Manuel, Loza, Maria I., Lubar, Aristea, Lucantoni, Leonardo, Lucet, Isabelle, Maes, Louis, Mancama, Dalu, Mansour, Nuha R., March, Sandra, McGowan, Sheena, Medina Vera, Iset, Meister, Stephan, Mercer, Luke, Mestres, Jordi, Mfopa, Alvine N., Misra, Raj N., Moon, Seunghyun, Moore, John P., Morais Rodrigues da Costa, Francielly, Müller, Joachim, Muriana, Arantza, Nakazawa Hewitt, Stephen, Nare, Bakela, Nathan, Carl, Narraidoo, Nathalie, Nawaratna, Sujeevi, Ojo, Kayode K., Ortiz, Diana, Panic, Gordana, Papadatos, George, Parapini, Silvia, Patra, Kailash, Pham, Ngoc, Prats, Sarah, Plouffe, David M., Poulsen, Sally-Ann, Pradhan, Anupam, Quevedo, Celia, Quinn, Ronald J., Rice, Christopher A., Ruecker, Andrea, St. Onge, Robert, Salgado Ferreira, Rafaela, Samra, Jasmeet, Robinett, Natalie G., Schlecht, Ulrich, Schmitt, Marjorie, Silva Villela, Filipe, Silvestrini, Francesco, Sinden, Robert, Smith, Dennis A., Soldati, Thierry, Spitzmüller, Andreas, Stamm, Serge Maximilian, Sullivan, David J., Sullivan, William, Suresh, Sundari, Suzuki, Brian M., Suzuki, Yo, Swamidass, S. Joshua, Taramelli, Donatella, Tchokouaha, Lauve R. Y., Theron, Anjo, Thomas, David, Tonissen, Kathryn F., Townson, Simon, Tripathi, Abhai K., Trofimov, Valentin, Udenze, Kenneth O., Ullah, Imran, Vallieres, Cindy, Vigil, Edgar, Vinetz, Joseph M., Voong Vinh, Phat, Vu, Hoan, Watanabe, Nao-aki, Weatherby, Kate, White, Pamela M., Wilks, Andrew F., Winzeler, Elizabeth A., Wojcik, Edward, Wree, Melanie, Wu, Wesley, Yokoyama, Naoaki, Zollo, Paul H. A., Abla, Nada, Blasco, Benjamin, Burrows, Jeremy, Laleu, Benoît, Leroy, Didier, Spangenberg, Thomas, Wells, Timothy, and Willis, Paul A.

Abstract

A major cause of the paucity of new starting points for drug discovery is the lack of interaction between academia and industry. Much of the global resource in biology is present in universities, whereas the focus of medicinal chemistry is still largely within industry. Open source drug discovery, with sharing of information, is clearly a first step towards overcoming this gap. But the interface could especially be bridged through a scale-up of open sharing of physical compounds, which would accelerate the finding of new starting points for drug discovery. The Medicines for Malaria Venture Malaria Box is a collection of over 400 compounds representing families of structures identified in phenotypic screens of pharmaceutical and academic libraries against the Plasmodium falciparum malaria parasite. The set has now been distributed to almost 200 research groups globally in the last two years, with the only stipulation that information from the screens is deposited in the public domain. This paper reports for the first time on 236 screens that have been carried out against the Malaria Box and compares these results with 55 assays that were previously published, in a format that allows a meta-analysis of the combined dataset. The combined biochemical and cellular assays presented here suggest mechanisms of action for 135 (34%) of the compounds active in killing multiple life-cycle stages of the malaria parasite, including asexual blood, liver, gametocyte, gametes and insect ookinete stages. In addition, many compounds demonstrated activity against other pathogens, showing hits in assays with 16 protozoa, 7 helminths, 9 bacterial and mycobacterial species, the dengue fever mosquito vector, and the NCI60 human cancer cell line panel of 60 human tumor cell lines. Toxicological, pharmacokinetic and metabolic properties were collected on all the compounds, assisting in the selection of the most promising candidates for murine proof-of-concept experiments and medicinal chemis

Published
2017

44. Open Source Drug Discovery with the Malaria Box Compound Collection for Neglected Diseases and Beyond

Author

Phillips, Margaret A., Van Voorhis, Wesley C., Adams, John H., Adelfio, Roberto, Ahyong, Vida, Akabas, Myles H., Alano, Pietro, Alday, Aintzane, Alsibaee, Aishah, Alzualde, Ainhoa, Andrews, Katherine T., Avery, Simon V., Avery, Vicky M., Ayong, Lawrence, Baker, Mark, Baker, Stephen, Ben Mamoun, Choukri, Bhatia, Sangeeta, Bickle, Quentin, Bounaadja, Lotfi, Bowling, Tana, Boucher, Lauren E., Boyom, Fabrice F., Brea, Jose, Brennan, Marian, Burton, Audrey, Caffrey, Conor R., Camarda, Grazia, Carrasquilla, Manuela, Carter, Dee, Belen Cassera, Maria, Chih-Chien Cheng, Ken, Chindaudomsate, Worathad, Chubb, Anthony, Colon, Beatrice L., Corbett, Yolanda, Crowther, Gregory J., Cowan, Noemi, Le Dang, Na, Delves, Michael, DeRisi, Joseph L., Du, Alan Y., Duffy, Sandra, Abd El-Salam El-Sayed, Shimaa, Ferdig, Michael T., Fidock, David A., Florent, Isabelle, Fokou, Patrick V. T., Galstian, Ani, Gamo, Francisco Javier, Gokool, Suzanne, Gold, Ben, Golub, Todd, Goldgof, Gregory M., Guha, Rajarshi, Guiguemde, W. Armand, Gural, Nil, Guy, R. Kiplin, Hansen, Michael A. E., Hanson, Kirsten K., Hemphill, Andrew, Hooft van Huijsduijnen, Rob, Horii, Takaaki, Horrocks, Paul, Hughes, Tyler B., Huston, Christopher, Igarashi, Ikuo, Ingram-Sieber, Katrin, Itoe, Maurice A., Jadhav, Ajit, Naranuntarat Jensen, Amornrat, Jensen, Laran T., Jiang, Rays H.Y., Kaiser, Annette, Keiser, Jennifer, Ketas, Thomas, Kicka, Sebastien, Kim, Sunyoung, Kirk, Kiaran, Kumar, Vidya P., Kyle, Dennis E., Lafuente, Maria Jose, Landfear, Scott, Lee, Nathan, Lee, Sukjun, Lehane, Adele M., Li, Fengwu, Little, David, Liu, Liqiong, Loza, Maria I., Lubar, Aristea, Lucantoni, Leonardo, Lucet, Isabelle, Maes, Louis, Mancama, Dalu, Mansour, Nuha R., March, Sandra, McGowan, Sheena, Medina Vera, Iset, Meister, Stephan, Mercer, Luke, Mestres, Jordi, Mfopa, Alvine N., Misra, Raj N., Moon, Seunghyun, Moore, John P., Morais Rodrigues da Costa, Francielly, Muriana, Arantza, Nakazawa Hewitt, Stephen, Nare, Bakela, Nathan, Carl, Narraidoo, Nathalie, Nawaratna, Sujeevi, Ojo, Kayode K., Ortiz, Diana, Panic, Gordana, Papadatos, George, Parapini, Silvia, Patra, Kailash, Pham, Ngoc, Prats, Sarah, Plouffe, David M., Poulsen, Sally-Ann, Pradhan, Anupam, Quevedo, Celia, Quinn, Ronald J., Rice, Christopher A., Abdo Rizk, Mohamed, Ruecker, Andrea, St. Onge, Robert, Salgado Ferreira, Rafaela, Samra, Jasmeet, Robinett, Natalie G., Schlecht, Ulrich, Schmitt, Marjorie, Silva Villela, Filipe, Silvestrini, Francesco, Sinden, Robert, Smith, Dennis A., Soldati, Thierry, Stamm, Serge Maximilian, Sullivan, David J., Sullivan, William, Suresh, Sundari, Suzuki, Brian M., Suzuki, Yo, Swamidass, S. Joshua, Taramelli, Donatella, Tchokouaha, Lauve R.Y., Theron, Anjo, Thomas, David, Tonissen, Kathryn F., Townson, Simon, Tripathi, Abhai K., Trofimov, Valentin, Udenze, Kenneth O., Ullah, Imran, Vallieres, Cindy, Vigil, Edgar, Vinetz, Joseph M., Voong Vinh, Phat, Vu, Hoan, Watanabe, Nao-aki, Weatherby, Kate, White, Pamela M., Wilks, Andrew F., Winzeler, Elizabeth A., Wojcik, Edward, Wree, Melanie, Wu, Wesley, Yokoyama, Naoaki, Zollo, Paul H. A., Abla, Nada, Blasco, Benjamin, Burrows, Jeremy, Leroy, Didier, Spangenberg, Thomas, Wells, Timothy, and Willis, Paul A.

Abstract

A major cause of the paucity of new starting points for drug discovery is the lack of interaction between academia and industry. Much of the global resource in biology is present in universities, whereas the focus of medicinal chemistry is still largely within industry. Open source drug discovery, with sharing of information, is clearly a first step towards overcoming this gap. But the interface could especially be bridged through a scale-up of open sharing of physical compounds, which would accelerate the finding of new starting points for drug discovery. The Medicines for Malaria Venture Malaria Box is a collection of over 400 compounds representing families of structures identified in phenotypic screens of pharmaceutical and academic libraries against the Plasmodium falciparum malaria parasite. The set has now been distributed to almost 200 research groups globally in the last two years, with the only stipulation that information from the screens is deposited in the public domain. This paper reports for the first time on 236 screens that have been carried out against the Malaria Box and compares these results with 55 assays that were previously published, in a format that allows a meta-analysis of the combined dataset. The combined biochemical and cellular assays presented here suggest mechanisms of action for 135 (34%) of the compounds active in killing multiple life-cycle stages of the malaria parasite, including asexual blood, liver, gametocyte, gametes and insect ookinete stages. In addition, many compounds demonstrated activity against other pathogens, showing hits in assays with 16 protozoa, 7 helminths, 9 bacterial and mycobacterial species, the dengue fever mosquito vector, and the NCI60 human cancer cell line panel of 60 human tumor cell lines. Toxicological, pharmacokinetic and metabolic properties were collected on all the compounds, assisting in the selection of the most promising candidates for murine proof-of-concept experiments and medicinal chemistry programs. The data for all of these assays are presented and analyzed to show how outstanding leads for many indications can be selected. These results reveal the immense potential for translating the dispersed expertise in biological assays involving human pathogens into drug discovery starting points, by providing open access to new families of molecules, and emphasize how a small additional investment made to help acquire and distribute compounds, and sharing the data, can catalyze drug discovery for dozens of different indications. Another lesson is that when multiple screens from different groups are run on the same library, results can be integrated quickly to select the most valuable starting points for subsequent medicinal chemistry efforts.

Published
2016

45. Significance of Multiple Bioactivation Pathways for Meclofenamate as Revealed through Modeling and Reaction Kinetics

Author

Schleiff, Mary Alexandra, Flynn, Noah R., Payakachat, Sasin, Schleiff, Benjamin Mark, Pinson, Anna O., Province, Dennis W., Swamidass, S. Joshua, Boysen, Gunnar, and Miller, Grover P.

Abstract

Meclofenamate is a nonsteroidal anti-inflammatory drug used in the treatment of mild-to-moderate pain yet poses a rare risk of hepatotoxicity through an unknown mechanism. Nonsteroidal anti-inflammatory drug (NSAID) bioactivation is a common molecular initiating event for hepatotoxicity. Thus, we hypothesized a similar mechanism for meclofenamate and leveraged computational and experimental approaches to identify and characterize its bioactivation. Analyses employing our XenoNet model indicated possible pathways to meclofenamate bioactivation into 19 reactive metabolites subsequently trapped into glutathione adducts. We describe the first reported bioactivation kinetics for meclofenamate and relative importance of those pathways using human liver microsomes. The findings validated only four of the many bioactivation pathways predicted by modeling. For experimental studies, dansyl glutathione was a critical trap for reactive quinone metabolites and provided a way to characterize adduct structures by mass spectrometry and quantitate yields during reactions. Of the four quinone adducts, we were able to characterize structures for three of them. Based on kinetics, the most efficient bioactivation pathway led to the monohydroxy para-quinone-imine followed by the dechloro-ortho-quinone-imine. Two very inefficient pathways led to the dihydroxy ortho-quinone and a likely multiply adducted quinone. When taken together, bioactivation pathways for meclofenamate accounted for approximately 13% of total metabolism. In sum, XenoNet facilitated prediction of reactive metabolite structures, whereas quantitative experimental studies provided a tractable approach to validate actual bioactivation pathways for meclofenamate. Our results provide a foundation for assessing reactive metabolite load more accurately for future comparative studies with other NSAIDs and drugs in general.Significance StatementMeclofenamate bioactivation may initiate hepatotoxicity, yet common risk assessment approaches are often cumbersome and inefficient and yield qualitative insights that do not scale relative bioactivation risks. We developed and applied innovative computational modeling and quantitative kinetics to identify and validate meclofenamate bioactivation pathways and relevance as a function of time and concentration. This strategy yielded novel insights on meclofenamate bioactivation and provides a tractable approach to more accurately and efficiently assess other drug bioactivations and correlate risks to toxicological outcomes.

Published
2021
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47. Learning a Local-Variable Model of Aromatic and Conjugated Systems

Author

Matlock, Matthew K., Dang, Na Le, and Swamidass, S. Joshua

Abstract

A collection of new approaches to building and training neural networks, collectively referred to as deep learning, are attracting attention in theoretical chemistry. Several groups aim to replace computationally expensive ab initioquantum mechanics calculations with learned estimators. This raises questions about the representability of complex quantum chemical systems with neural networks. Can local-variable models efficiently approximate nonlocal quantum chemical features? Here, we find that convolutional architectures, those that only aggregate information locally, cannot efficiently represent aromaticity and conjugation in large systems. They cannot represent long-range nonlocality known to be important in quantum chemistry. This study uses aromatic and conjugated systems computed from molecule graphs, though reproducing quantum simulations is the ultimate goal. This task, by definition, is both computable and known to be important to chemistry. The failure of convolutional architectures on this focused task calls into question their use in modeling quantum mechanics. To remedy this heretofore unrecognized deficiency, we introduce a new architecture that propagates information back and forth in waves of nonlinear computation. This architecture is still a local-variable model, and it is both computationally and representationally efficient, processing molecules in sublinear time with far fewer parameters than convolutional networks. Wave-like propagation models aromatic and conjugated systems with high accuracy, and even models the impact of small structural changes on large molecules. This new architecture demonstrates that some nonlocal features of quantum chemistry can be efficiently represented in local variable models.

Published
2024
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48. Open Source Drug Discovery with the Malaria Box Compound Collection for Neglected Diseases and Beyond

Author

Van Voorhis, Wesley C., Adams, John H., Adelfio, Roberto, Ahyong, Vida, Akabas, Myles H., Alano, Pietro, Alday, Aintzane, Resto, Yesmalie Aleman, Alsibaee, Aishah, Alzualde, Ainhoa, Andrews, Katherine T., Avery, Simon V., Avery, Vicky M., Ayong, Lawrence, Baker, Mark, Baker, Stephen, Ben Mamoun, Choukri, Bhatia, Sangeeta, Bickle, Quentin, Bounaadja, Lotfi, Bowling, Tana, Bosch, Juergen, Boucher, Lauren E., Boyom, Fabrice F., Brea, Jose, Brennan, Marian, Burton, Audrey, Caffrey, Conor R., Camarda, Grazia, Carrasquilla, Manuela, Carter, Dee, Cassera, Maria B., Cheng, Ken Chih-Chien, Chindaudomsate, Worathad, Chubb, Anthony, Colon, Beatrice L., Colon-Lopez, Daisy D., Corbett, Yolanda, Crowther, Gregory J., Cowan, Noemi, D'Alessandro, Sarah, Le Dang, Na, Delves, Michael, DeRisi, Joseph L., Du, Alan Y., Duffy, Sandra, El-Sayed, Shimaa Abd El-Salam, Ferdig, Michael T., Robledo, Jose A. Fernandez, Fidock, David A., Florent, Isabelle, Fokou, Patrick V. T., Galstian, Ani, Javier Gamo, Francisco, Gokool, Suzanne, Gold, Ben, Golub, Todd, Goldgof, Gregory M., Guha, Rajarshi, Guiguemde, W. Armand, Gural, Nil, Guy, R. Kiplin, Hansen, Michael A. E., Hanson, Kirsten K., Hemphill, Andrew, van Huijsduijnen, Rob Hooft, Horii, Takaaki, Horrocks, Paul, Hughes, Tyler B., Huston, Christopher, Igarashi, Ikuo, Ingram-Sieber, Katrin, Itoe, Maurice A., Jadhav, Ajit, Jensen, Amornrat Naranuntarat, Jensen, Laran T., Jiang, Rays H. Y., Kaiser, Annette, Keiser, Jennifer, Ketas, Thomas, Kicka, Sebastien, Kim, Sunyoung, Kirk, Kiaran, Kumar, Vidya P., Kyle, Dennis E., Jose Lafuente, Maria, Landfear, Scott, Lee, Nathan, Lee, Sukjun, Lehane, Adele M., Li, Fengwu, Little, David, Liu, Liqiong, Llinas, Manuel, Loza, Maria I., Lubar, Aristea, Lucantoni, Leonardo, Lucet, Isabelle, Maes, Louis, Mancama, Dalu, Mansour, Nuha R., March, Sandra, McGowan, Sheena, Vera, Iset Medina, Meister, Stephan, Mercer, Luke, Mestres, Jordi, Mfopa, Alvine N., Misra, Raj N., Moon, Seunghyun, Moore, John P., Rodrigues da Costa, Francielly Morais, Mueller, Joachim, Muriana, Arantza, Hewitt, Stephen Nakazawa, Nare, Bakela, Nathan, Carl, Narraidoo, Nathalie, Nawaratna, Sujeevi, Ojo, Kayode K., Ortiz, Diana, Panic, Gordana, Papadatos, George, Parapini, Silvia, Patra, Kailash, Ngoc Pham, Prats, Sarah, Plouffe, David M., Poulsen, Sally-Ann, Pradhan, Anupam, Quevedo, Celia, Quinn, Ronald J., Rice, Christopher A., Rizk, Mohamed Abdo, Ruecker, Andrea, St Onge, Robert, Ferreira, Rafaela Salgado, Samra, Jasmeet, Robinett, Natalie G., Schlecht, Ulrich, Schmitt, Marjorie, Villela, Filipe Silva, Silvestrini, Francesco, Sinden, Robert, Smith, Dennis A., Soldati, Thierry, Spitzmueller, Andreas, Stamm, Serge Maximilian, Sullivan, David J., Sullivan, William G., Suresh, Sundari, Suzuki, Brian M., Suzuki, Yo, Swamidass, S. Joshua, Taramelli, Donatella, Tchokouaha, Lauve R. Y., Theron, Anjo, Thomas, David, Tonissen, Kathryn F., Townson, Simon, Tripathi, Abhai K., Trofimov, Valentin, Udenze, Kenneth O., Ullah, Imran, Vallieres, Cindy, Vigil, Edgar, Vinetz, Joseph M., Phat Voong Vinh, Hoan Vu, Watanabe, Nao-aki, Weatherby, Kate, White, Pamela M., Wilks, Andrew F., Winzeler, Elizabeth A., Wojcik, Edward, Wree, Melanie, Wu, Wesley, Yokoyama, Naoaki, Zollo, Paul H. A., Abla, Nada, Blasco, Benjamin, Burrows, Jeremy, Laleu, Benoit, Leroy, Didier, Spangenberg, Thomas, Wells, Timothy, Willis, Paul A., Van Voorhis, Wesley C., Adams, John H., Adelfio, Roberto, Ahyong, Vida, Akabas, Myles H., Alano, Pietro, Alday, Aintzane, Resto, Yesmalie Aleman, Alsibaee, Aishah, Alzualde, Ainhoa, Andrews, Katherine T., Avery, Simon V., Avery, Vicky M., Ayong, Lawrence, Baker, Mark, Baker, Stephen, Ben Mamoun, Choukri, Bhatia, Sangeeta, Bickle, Quentin, Bounaadja, Lotfi, Bowling, Tana, Bosch, Juergen, Boucher, Lauren E., Boyom, Fabrice F., Brea, Jose, Brennan, Marian, Burton, Audrey, Caffrey, Conor R., Camarda, Grazia, Carrasquilla, Manuela, Carter, Dee, Cassera, Maria B., Cheng, Ken Chih-Chien, Chindaudomsate, Worathad, Chubb, Anthony, Colon, Beatrice L., Colon-Lopez, Daisy D., Corbett, Yolanda, Crowther, Gregory J., Cowan, Noemi, D'Alessandro, Sarah, Le Dang, Na, Delves, Michael, DeRisi, Joseph L., Du, Alan Y., Duffy, Sandra, El-Sayed, Shimaa Abd El-Salam, Ferdig, Michael T., Robledo, Jose A. Fernandez, Fidock, David A., Florent, Isabelle, Fokou, Patrick V. T., Galstian, Ani, Javier Gamo, Francisco, Gokool, Suzanne, Gold, Ben, Golub, Todd, Goldgof, Gregory M., Guha, Rajarshi, Guiguemde, W. Armand, Gural, Nil, Guy, R. Kiplin, Hansen, Michael A. E., Hanson, Kirsten K., Hemphill, Andrew, van Huijsduijnen, Rob Hooft, Horii, Takaaki, Horrocks, Paul, Hughes, Tyler B., Huston, Christopher, Igarashi, Ikuo, Ingram-Sieber, Katrin, Itoe, Maurice A., Jadhav, Ajit, Jensen, Amornrat Naranuntarat, Jensen, Laran T., Jiang, Rays H. Y., Kaiser, Annette, Keiser, Jennifer, Ketas, Thomas, Kicka, Sebastien, Kim, Sunyoung, Kirk, Kiaran, Kumar, Vidya P., Kyle, Dennis E., Jose Lafuente, Maria, Landfear, Scott, Lee, Nathan, Lee, Sukjun, Lehane, Adele M., Li, Fengwu, Little, David, Liu, Liqiong, Llinas, Manuel, Loza, Maria I., Lubar, Aristea, Lucantoni, Leonardo, Lucet, Isabelle, Maes, Louis, Mancama, Dalu, Mansour, Nuha R., March, Sandra, McGowan, Sheena, Vera, Iset Medina, Meister, Stephan, Mercer, Luke, Mestres, Jordi, Mfopa, Alvine N., Misra, Raj N., Moon, Seunghyun, Moore, John P., Rodrigues da Costa, Francielly Morais, Mueller, Joachim, Muriana, Arantza, Hewitt, Stephen Nakazawa, Nare, Bakela, Nathan, Carl, Narraidoo, Nathalie, Nawaratna, Sujeevi, Ojo, Kayode K., Ortiz, Diana, Panic, Gordana, Papadatos, George, Parapini, Silvia, Patra, Kailash, Ngoc Pham, Prats, Sarah, Plouffe, David M., Poulsen, Sally-Ann, Pradhan, Anupam, Quevedo, Celia, Quinn, Ronald J., Rice, Christopher A., Rizk, Mohamed Abdo, Ruecker, Andrea, St Onge, Robert, Ferreira, Rafaela Salgado, Samra, Jasmeet, Robinett, Natalie G., Schlecht, Ulrich, Schmitt, Marjorie, Villela, Filipe Silva, Silvestrini, Francesco, Sinden, Robert, Smith, Dennis A., Soldati, Thierry, Spitzmueller, Andreas, Stamm, Serge Maximilian, Sullivan, David J., Sullivan, William G., Suresh, Sundari, Suzuki, Brian M., Suzuki, Yo, Swamidass, S. Joshua, Taramelli, Donatella, Tchokouaha, Lauve R. Y., Theron, Anjo, Thomas, David, Tonissen, Kathryn F., Townson, Simon, Tripathi, Abhai K., Trofimov, Valentin, Udenze, Kenneth O., Ullah, Imran, Vallieres, Cindy, Vigil, Edgar, Vinetz, Joseph M., Phat Voong Vinh, Hoan Vu, Watanabe, Nao-aki, Weatherby, Kate, White, Pamela M., Wilks, Andrew F., Winzeler, Elizabeth A., Wojcik, Edward, Wree, Melanie, Wu, Wesley, Yokoyama, Naoaki, Zollo, Paul H. A., Abla, Nada, Blasco, Benjamin, Burrows, Jeremy, Laleu, Benoit, Leroy, Didier, Spangenberg, Thomas, Wells, Timothy, and Willis, Paul A.

Abstract

A major cause of the paucity of new starting points for drug discovery is the lack of interaction between academia and industry. Much of the global resource in biology is present in universities, whereas the focus of medicinal chemistry is still largely within industry. Open source drug discovery, with sharing of information, is clearly a first step towards overcoming this gap. But the interface could especially be bridged through a scale-up of open sharing of physical compounds, which would accelerate the finding of new starting points for drug discovery. The Medicines for Malaria Venture Malaria Box is a collection of over 400 compounds representing families of structures identified in phenotypic screens of pharmaceutical and academic libraries against the Plasmodium falciparum malaria parasite. The set has now been distributed to almost 200 research groups globally in the last two years, with the only stipulation that information from the screens is deposited in the public domain. This paper reports for the first time on 236 screens that have been carried out against the Malaria Box and compares these results with 55 assays that were previously published, in a format that allows a meta-analysis of the combined dataset. The combined biochemical and cellular assays presented here suggest mechanisms of action for 135 (34%) of the compounds active in killing multiple life-cycle stages of the malaria parasite, including asexual blood, liver, gametocyte, gametes and insect ookinete stages. In addition, many compounds demonstrated activity against other pathogens, showing hits in assays with 16 protozoa, 7 helminths, 9 bacterial and mycobacterial species, the dengue fever mosquito vector, and the NCI60 human cancer cell line panel of 60 human tumor cell lines. Toxicological, pharmacokinetic and metabolic properties were collected on all the compounds, assisting in the selection of the most promising candidates for murine proof-of-concept experiments and medicinal chemis

Published
2016
Full Text
View/download PDF

49. Open Source Drug Discovery with the Malaria Box Compound Collection for Neglected Diseases and Beyond

Author

Biochemistry, Center for Drug Discovery, Van Voorhis, Wesley C., Adams, John H., Adelfio, Roberto, Ahyong, Vida, Akabas, Myles H., Alano, Pietro, Alday, Aintzane, Resto, Yesmalie Aleman, Alsibaee, Aishah, Alzualde, Ainhoa, Andrews, Katherine T., Avery, Simon V., Avery, Vicky M., Ayong, Lawrence, Baker, Mark, Baker, Stephen, Ben Mamoun, Choukri, Bhatia, Sangeeta, Bickle, Quentin, Bounaadja, Lotfi, Bowling, Tana, Bosch, Juergen, Boucher, Lauren E., Boyom, Fabrice F., Brea, Jose, Brennan, Marian, Burton, Audrey, Caffrey, Conor R., Camarda, Grazia, Carrasquilla, Manuela, Carter, Dee, Cassera, Maria B., Cheng, Ken Chih-Chien, Chindaudomsate, Worathad, Chubb, Anthony, Colon, Beatrice L., Colon-Lopez, Daisy D., Corbett, Yolanda, Crowther, Gregory J., Cowan, Noemi, D'Alessandro, Sarah, Le Dang, Na, Delves, Michael, DeRisi, Joseph L., Du, Alan Y., Duffy, Sandra, El-Sayed, Shimaa Abd El-Salam, Ferdig, Michael T., Robledo, Jose A. Fernandez, Fidock, David A., Florent, Isabelle, Fokou, Patrick V. T., Galstian, Ani, Javier Gamo, Francisco, Gokool, Suzanne, Gold, Ben, Golub, Todd, Goldgof, Gregory M., Guha, Rajarshi, Guiguemde, W. Armand, Gural, Nil, Guy, R. Kiplin, Hansen, Michael A. E., Hanson, Kirsten K., Hemphill, Andrew, van Huijsduijnen, Rob Hooft, Horii, Takaaki, Horrocks, Paul, Hughes, Tyler B., Huston, Christopher, Igarashi, Ikuo, Ingram-Sieber, Katrin, Itoe, Maurice A., Jadhav, Ajit, Jensen, Amornrat Naranuntarat, Jensen, Laran T., Jiang, Rays H. Y., Kaiser, Annette, Keiser, Jennifer, Ketas, Thomas, Kicka, Sebastien, Kim, Sunyoung, Kirk, Kiaran, Kumar, Vidya P., Kyle, Dennis E., Jose Lafuente, Maria, Landfear, Scott, Lee, Nathan, Lee, Sukjun, Lehane, Adele M., Li, Fengwu, Little, David, Liu, Liqiong, Llinas, Manuel, Loza, Maria I., Lubar, Aristea, Lucantoni, Leonardo, Lucet, Isabelle, Maes, Louis, Mancama, Dalu, Mansour, Nuha R., March, Sandra, McGowan, Sheena, Vera, Iset Medina, Meister, Stephan, Mercer, Luke, Mestres, Jordi, Mfopa, Alvine N., Misra, Raj N., Moon, Seunghyun, Moore, John P., Rodrigues da Costa, Francielly Morais, Mueller, Joachim, Muriana, Arantza, Hewitt, Stephen Nakazawa, Nare, Bakela, Nathan, Carl, Narraidoo, Nathalie, Nawaratna, Sujeevi, Ojo, Kayode K., Ortiz, Diana, Panic, Gordana, Papadatos, George, Parapini, Silvia, Patra, Kailash, Ngoc Pham, Prats, Sarah, Plouffe, David M., Poulsen, Sally-Ann, Pradhan, Anupam, Quevedo, Celia, Quinn, Ronald J., Rice, Christopher A., Rizk, Mohamed Abdo, Ruecker, Andrea, St Onge, Robert, Ferreira, Rafaela Salgado, Samra, Jasmeet, Robinett, Natalie G., Schlecht, Ulrich, Schmitt, Marjorie, Villela, Filipe Silva, Silvestrini, Francesco, Sinden, Robert, Smith, Dennis A., Soldati, Thierry, Spitzmueller, Andreas, Stamm, Serge Maximilian, Sullivan, David J., Sullivan, William G., Suresh, Sundari, Suzuki, Brian M., Suzuki, Yo, Swamidass, S. Joshua, Taramelli, Donatella, Tchokouaha, Lauve R. Y., Theron, Anjo, Thomas, David, Tonissen, Kathryn F., Townson, Simon, Tripathi, Abhai K., Trofimov, Valentin, Udenze, Kenneth O., Ullah, Imran, Vallieres, Cindy, Vigil, Edgar, Vinetz, Joseph M., Phat Voong Vinh, Hoan Vu, Watanabe, Nao-aki, Weatherby, Kate, White, Pamela M., Wilks, Andrew F., Winzeler, Elizabeth A., Wojcik, Edward, Wree, Melanie, Wu, Wesley, Yokoyama, Naoaki, Zollo, Paul H. A., Abla, Nada, Blasco, Benjamin, Burrows, Jeremy, Laleu, Benoit, Leroy, Didier, Spangenberg, Thomas, Wells, Timothy, Willis, Paul A., Biochemistry, Center for Drug Discovery, Van Voorhis, Wesley C., Adams, John H., Adelfio, Roberto, Ahyong, Vida, Akabas, Myles H., Alano, Pietro, Alday, Aintzane, Resto, Yesmalie Aleman, Alsibaee, Aishah, Alzualde, Ainhoa, Andrews, Katherine T., Avery, Simon V., Avery, Vicky M., Ayong, Lawrence, Baker, Mark, Baker, Stephen, Ben Mamoun, Choukri, Bhatia, Sangeeta, Bickle, Quentin, Bounaadja, Lotfi, Bowling, Tana, Bosch, Juergen, Boucher, Lauren E., Boyom, Fabrice F., Brea, Jose, Brennan, Marian, Burton, Audrey, Caffrey, Conor R., Camarda, Grazia, Carrasquilla, Manuela, Carter, Dee, Cassera, Maria B., Cheng, Ken Chih-Chien, Chindaudomsate, Worathad, Chubb, Anthony, Colon, Beatrice L., Colon-Lopez, Daisy D., Corbett, Yolanda, Crowther, Gregory J., Cowan, Noemi, D'Alessandro, Sarah, Le Dang, Na, Delves, Michael, DeRisi, Joseph L., Du, Alan Y., Duffy, Sandra, El-Sayed, Shimaa Abd El-Salam, Ferdig, Michael T., Robledo, Jose A. Fernandez, Fidock, David A., Florent, Isabelle, Fokou, Patrick V. T., Galstian, Ani, Javier Gamo, Francisco, Gokool, Suzanne, Gold, Ben, Golub, Todd, Goldgof, Gregory M., Guha, Rajarshi, Guiguemde, W. Armand, Gural, Nil, Guy, R. Kiplin, Hansen, Michael A. E., Hanson, Kirsten K., Hemphill, Andrew, van Huijsduijnen, Rob Hooft, Horii, Takaaki, Horrocks, Paul, Hughes, Tyler B., Huston, Christopher, Igarashi, Ikuo, Ingram-Sieber, Katrin, Itoe, Maurice A., Jadhav, Ajit, Jensen, Amornrat Naranuntarat, Jensen, Laran T., Jiang, Rays H. Y., Kaiser, Annette, Keiser, Jennifer, Ketas, Thomas, Kicka, Sebastien, Kim, Sunyoung, Kirk, Kiaran, Kumar, Vidya P., Kyle, Dennis E., Jose Lafuente, Maria, Landfear, Scott, Lee, Nathan, Lee, Sukjun, Lehane, Adele M., Li, Fengwu, Little, David, Liu, Liqiong, Llinas, Manuel, Loza, Maria I., Lubar, Aristea, Lucantoni, Leonardo, Lucet, Isabelle, Maes, Louis, Mancama, Dalu, Mansour, Nuha R., March, Sandra, McGowan, Sheena, Vera, Iset Medina, Meister, Stephan, Mercer, Luke, Mestres, Jordi, Mfopa, Alvine N., Misra, Raj N., Moon, Seunghyun, Moore, John P., Rodrigues da Costa, Francielly Morais, Mueller, Joachim, Muriana, Arantza, Hewitt, Stephen Nakazawa, Nare, Bakela, Nathan, Carl, Narraidoo, Nathalie, Nawaratna, Sujeevi, Ojo, Kayode K., Ortiz, Diana, Panic, Gordana, Papadatos, George, Parapini, Silvia, Patra, Kailash, Ngoc Pham, Prats, Sarah, Plouffe, David M., Poulsen, Sally-Ann, Pradhan, Anupam, Quevedo, Celia, Quinn, Ronald J., Rice, Christopher A., Rizk, Mohamed Abdo, Ruecker, Andrea, St Onge, Robert, Ferreira, Rafaela Salgado, Samra, Jasmeet, Robinett, Natalie G., Schlecht, Ulrich, Schmitt, Marjorie, Villela, Filipe Silva, Silvestrini, Francesco, Sinden, Robert, Smith, Dennis A., Soldati, Thierry, Spitzmueller, Andreas, Stamm, Serge Maximilian, Sullivan, David J., Sullivan, William G., Suresh, Sundari, Suzuki, Brian M., Suzuki, Yo, Swamidass, S. Joshua, Taramelli, Donatella, Tchokouaha, Lauve R. Y., Theron, Anjo, Thomas, David, Tonissen, Kathryn F., Townson, Simon, Tripathi, Abhai K., Trofimov, Valentin, Udenze, Kenneth O., Ullah, Imran, Vallieres, Cindy, Vigil, Edgar, Vinetz, Joseph M., Phat Voong Vinh, Hoan Vu, Watanabe, Nao-aki, Weatherby, Kate, White, Pamela M., Wilks, Andrew F., Winzeler, Elizabeth A., Wojcik, Edward, Wree, Melanie, Wu, Wesley, Yokoyama, Naoaki, Zollo, Paul H. A., Abla, Nada, Blasco, Benjamin, Burrows, Jeremy, Laleu, Benoit, Leroy, Didier, Spangenberg, Thomas, Wells, Timothy, and Willis, Paul A.

Abstract

A major cause of the paucity of new starting points for drug discovery is the lack of interaction between academia and industry. Much of the global resource in biology is present in universities, whereas the focus of medicinal chemistry is still largely within industry. Open source drug discovery, with sharing of information, is clearly a first step towards overcoming this gap. But the interface could especially be bridged through a scale-up of open sharing of physical compounds, which would accelerate the finding of new starting points for drug discovery. The Medicines for Malaria Venture Malaria Box is a collection of over 400 compounds representing families of structures identified in phenotypic screens of pharmaceutical and academic libraries against the Plasmodium falciparum malaria parasite. The set has now been distributed to almost 200 research groups globally in the last two years, with the only stipulation that information from the screens is deposited in the public domain. This paper reports for the first time on 236 screens that have been carried out against the Malaria Box and compares these results with 55 assays that were previously published, in a format that allows a meta-analysis of the combined dataset. The combined biochemical and cellular assays presented here suggest mechanisms of action for 135 (34%) of the compounds active in killing multiple life-cycle stages of the malaria parasite, including asexual blood, liver, gametocyte, gametes and insect ookinete stages. In addition, many compounds demonstrated activity against other pathogens, showing hits in assays with 16 protozoa, 7 helminths, 9 bacterial and mycobacterial species, the dengue fever mosquito vector, and the NCI60 human cancer cell line panel of 60 human tumor cell lines. Toxicological, pharmacokinetic and metabolic properties were collected on all the compounds, assisting in the selection of the most promising candidates for murine proof-of-concept experiments and medicinal chemis

Published
2016

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