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2. SmartGraph API: Programmatic Knowledge Mining in Network-Pharmacology Setting

Author

Zahoránszky-Kőhalmi, Gergely, Walker, Brandon, Miller, Nathan, Yang, Brett, Penna, Dhatri V. L., Maine, Jessica, Sheils, Timothy, Wang, Ke, King, Jennifer, Sidky, Hythem, Vuyyuru, Sridhar, Soundarajan, Jeyaraman, Michael, Samuel G., Godfrey, Alexander G., and Oprea, Tudor I.

Abstract

The recent SmartGraph platform facilitates the execution of complex drug-discovery workflows with ease in the network-pharmacology paradigm. However, at the time of its publication we identified the need for the development of an Application Programming Interface (API) that could promote biomedical data integration and hypothesis generation in an automated manner. This need was magnified at the time of the COVID-19 pandemic. This study addresses the absence of such an API. Accordingly, most functionalities of the original platform were implemented within the SmartGraph API. We demonstrate that by using the API it is possible to transform the original semiautomated workflow behind the Neo4COVID19 database to a fully automated one. The availability of the SmartGraph API lends a significant improvement to the programmatic integration of network-pharmacology-oriented knowledge graphs and analytics, as well as predictive functionalities and workflows.

Published
2024
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4. SmartGraph API: Programmatic Knowledge Mining in Network- Pharmacology Setting

Author

Zahoránszky-Kőhalmi, Gergely, primary, Walker, Brandon, additional, Miller, Nathan, additional, Yang, Brett, additional, Penna, Dhatri V. L., additional, Binder, Jessica, additional, Sheils, Timothy, additional, Wang, Ke, additional, King, Jennifer, additional, Sidky, Hythem, additional, Vuyyuru, Sridhar, additional, Soundarajan, Jeyaraman, additional, Michael, Samuel G., additional, Godfrey, Alexander G., additional, and Oprea, Tudor I., additional

Published
2024
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5. TIN-X version 3: update with expanded dataset and modernized architecture for enhanced illumination of understudied targets.

Author

Metzger, Vincent T., Cannon, Daniel C., Yang, Jeremy J., Mathias, Stephen L., Bologa, Cristian G., Waller, Anna, Schürer, Stephan C., Vidović, Dušica, Kelleher, Keith J., Sheils, Timothy K., Jensen, Lars Juhl, Lambert, Christophe G., Oprea, Tudor I., and Edwards, Jeremy S.

Subjects
NATURAL language processing, TEXT mining, DRUG target, DISEASE nomenclature, DATABASES
Abstract

TIN-X (Target Importance and Novelty eXplorer) is an interactive visualization tool for illuminating associations between diseases and potential drug targets and is publicly available at newdrugtargets.org. TIN-X uses natural language processing to identify disease and protein mentions within PubMed content using previously published tools for named entity recognition (NER) of gene/protein and disease names. Target data is obtained from the Target Central Resource Database (TCRD). Two important metrics, novelty and importance, are computed from this data and when plotted as log(importance) vs. log(novelty), aid the user in visually exploring the novelty of drug targets and their associated importance to diseases. TIN-X Version 3.0 has been significantly improved with an expanded dataset, modernized architecture including a REST API, and an improved user interface (UI). The dataset has been expanded to include not only PubMed publication titles and abstracts, but also full-text articles when available. This results in approximately 9-fold more target/disease associations compared to previous versions of TIN-X. Additionally, the TIN-X database containing this expanded dataset is now hosted in the cloud via Amazon RDS. Recent enhancements to the UI focuses on making it more intuitive for users to find diseases or drug targets of interest while providing a new, sortable table-view mode to accompany the existing plot-view mode. UI improvements also help the user browse the associated PubMed publications to explore and understand the basis of TIN-X's predicted association between a specific disease and a target of interest. While implementing these upgrades, computational resources are balanced between the webserver and the user's web browser to achieve adequate performance while accommodating the expanded dataset. Together, these advances aim to extend the duration that users can benefit from TIN-X while providing both an expanded dataset and new features that researchers can use to better illuminate understudied proteins. [ABSTRACT FROM AUTHOR]

Published
2024
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7. Pharos 2023:an integrated resource for the understudied human proteome

Author

Kelleher, Keith J., Sheils, Timothy K., Mathias, Stephen L., Yang, Jeremy J., Metzger, Vincent T., Siramshetty, Vishal B., Nguyen, Dac-Trung, Jensen, Lars Juhl, Vidovic, Dusica, Schurer, Stephan C., Holmes, Jayme, Sharma, Karlie R., Pillai, Ajay, Bologa, Cristian G., Edwards, Jeremy S., Mathe, Ewy A., Oprea, Tudor, Kelleher, Keith J., Sheils, Timothy K., Mathias, Stephen L., Yang, Jeremy J., Metzger, Vincent T., Siramshetty, Vishal B., Nguyen, Dac-Trung, Jensen, Lars Juhl, Vidovic, Dusica, Schurer, Stephan C., Holmes, Jayme, Sharma, Karlie R., Pillai, Ajay, Bologa, Cristian G., Edwards, Jeremy S., Mathe, Ewy A., and Oprea, Tudor

Abstract

The Illuminating the Druggable Genome (IDG) project aims to improve our understanding of understudied proteins and our ability to study them in the context of disease biology by perturbing them with small molecules, biologics, or other therapeutic modalities. Two main products from the IDG effort are the Target Central Resource Database (TCRD), which curates and aggregates information, and Pharos, a web interface for fusers to extract and visualize data from TCRD. Since the 2021 release, TCRD/Pharos has focused on developing visualization and analysis tools that help reveal higher-level patterns in the underlying data. The current iterations of TCRD and Pharos enable users to perform enrichment calculations based on subsets of targets, diseases, or ligands and to create interactive heat maps and UpSet charts of many types of annotations. Using several examples, we show how to address disease biology and drug discovery questions through enrichment calculations and UpSet charts.

Published
2023

8. Pharos 2023: an integrated resource for the understudied human proteome

Author

Kelleher, Keith J, primary, Sheils, Timothy K, additional, Mathias, Stephen L, additional, Yang, Jeremy J, additional, Metzger, Vincent T, additional, Siramshetty, Vishal B, additional, Nguyen, Dac-Trung, additional, Jensen, Lars Juhl, additional, Vidović, Dušica, additional, Schürer, Stephan C, additional, Holmes, Jayme, additional, Sharma, Karlie R, additional, Pillai, Ajay, additional, Bologa, Cristian G, additional, Edwards, Jeremy S, additional, Mathé, Ewy A, additional, and Oprea, Tudor I, additional

Published
2022
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11. Getting Started with the IDG KMC Datasets and Tools

Author

Kropiwnicki, Eryk, primary, Binder, Jessica L., additional, Yang, Jeremy J., additional, Holmes, Jayme, additional, Lachmann, Alexander, additional, Clarke, Daniel J. B., additional, Sheils, Timothy, additional, Kelleher, Keith J., additional, Metzger, Vincent T., additional, Bologa, Cristian G., additional, Oprea, Tudor I., additional, and Ma'ayan, Avi, additional

Published
2022
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12. Pharos 2023: an integrated resource for the understudied human proteome.

Author

Kelleher, Keith J, Sheils, Timothy K, Mathias, Stephen L, Yang, Jeremy J, Metzger, Vincent T, Siramshetty, Vishal B, Nguyen, Dac-Trung, Jensen, Lars Juhl, Vidović, Dušica, Schürer, Stephan C, Holmes, Jayme, Sharma, Karlie R, Pillai, Ajay, Bologa, Cristian G, Edwards, Jeremy S, Mathé, Ewy A, and Oprea, Tudor I

Published
2023
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14. Erratum: CATMoS: Collaborative Acute Toxicity Modeling Suite

Author

Mansouri, Kamel, primary, Karmaus, Agnes L., additional, Fitzpatrick, Jeremy, additional, Patlewicz, Grace, additional, Pradeep, Prachi, additional, Alberga, Domenico, additional, Alepee, Nathalie, additional, Allen, Timothy E.H., additional, Allen, Dave, additional, Alves, Vinicius M., additional, Andrade, Carolina H., additional, Auernhammer, Tyler R., additional, Ballabio, Davide, additional, Bell, Shannon, additional, Benfenati, Emilio, additional, Bhattacharya, Sudin, additional, Bastos, Joyce V., additional, Boyd, Stephen, additional, Brown, J. B., additional, Capuzzi, Stephen J., additional, Chushak, Yaroslav, additional, Ciallella, Heather, additional, Clark, Alex M., additional, Consonni, Viviana, additional, Daga, Pankaj R., additional, Ekins, Sean, additional, Farag, Sherif, additional, Fedorov, Maxim, additional, Fourches, Denis, additional, Gadaleta, Domenico, additional, Gao, Feng, additional, Gearhart, Jeffery M., additional, Goh, Garett, additional, Goodman, Jonathan M., additional, Grisoni, Francesca, additional, Grulke, Christopher M., additional, Hartung, Thomas, additional, Hirn, Matthew, additional, Karpov, Pavel, additional, Korotcov, Alexandru, additional, Lavado, Giovanna J., additional, Lawless, Michael, additional, Li, Xinhao, additional, Luechtefeld, Thomas, additional, Lunghini, Filippo, additional, Mangiatordi, Giuseppe F., additional, Marcou, Gilles, additional, Marsh, Dan, additional, Martin, Todd, additional, Mauri, Andrea, additional, Muratov, Eugene N., additional, Myatt, Glenn J., additional, Nguyen, Dac-Trung, additional, Nicolotti, Orazio, additional, Note, Reine, additional, Pande, Paritosh, additional, Parks, Amanda K., additional, Peryea, Tyler, additional, Polash, Ahsan H., additional, Rallo, Robert, additional, Roncaglioni, Alessandra, additional, Rowlands, Craig, additional, Ruiz, Patricia, additional, Russo, Daniel P., additional, Sayed, Ahmed, additional, Sayre, Risa, additional, Sheils, Timothy, additional, Siegel, Charles, additional, Silva, Arthur C., additional, Simeonov, Anton, additional, Sosnin, Sergey, additional, Southall, Noel, additional, Strickland, Judy, additional, Tang, Yun, additional, Teppen, Brian, additional, Tetko, Igor V., additional, Thomas, Dennis, additional, Tkachenko, Valery, additional, Todeschini, Roberto, additional, Toma, Cosimo, additional, Tripodi, Ignacio, additional, Trisciuzzi, Daniela, additional, Tropsha, Alexander, additional, Varnek, Alexandre, additional, Vukovic, Kristijan, additional, Wang, Zhongyu, additional, Wang, Liguo, additional, Waters, Katrina M., additional, Wedlake, Andrew J., additional, Wijeyesakere, Sanjeeva J., additional, Wilson, Dan, additional, Xiao, Zijun, additional, Yang, Hongbin, additional, Zahoranszky-Kohalmi, Gergely, additional, Zakharov, Alexey V., additional, Zhang, Fagen F., additional, Zhang, Zhen, additional, Zhao, Tongan, additional, Zhu, Hao, additional, Zorn, Kimberley M., additional, Casey, Warren, additional, and Kleinstreuer, Nicole C., additional

Published
2021
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16. TCRD and Pharos 2021:mining the human proteome for disease biology

Author

Sheils, Timothy K., Mathias, Stephen L., Kelleher, Keith J., Siramshetty, Vishal B., Nguyen, Dac Trung, Bologa, Cristian G., Jensen, Lars Juhl, Vidović, Dušica, Koleti, Amar, Schürer, Stephan C., Waller, Anna, Yang, Jeremy J., Holmes, Jayme, Bocci, Giovanni, Southall, Noel, Dharkar, Poorva, Mathé, Ewy, Simeonov, Anton, Oprea, Tudor I., Sheils, Timothy K., Mathias, Stephen L., Kelleher, Keith J., Siramshetty, Vishal B., Nguyen, Dac Trung, Bologa, Cristian G., Jensen, Lars Juhl, Vidović, Dušica, Koleti, Amar, Schürer, Stephan C., Waller, Anna, Yang, Jeremy J., Holmes, Jayme, Bocci, Giovanni, Southall, Noel, Dharkar, Poorva, Mathé, Ewy, Simeonov, Anton, and Oprea, Tudor I.

Abstract

In 2014, the National Institutes of Health (NIH) initiated the Illuminating the Druggable Genome (IDG) program to identify and improve our understanding of poorly characterized proteins that can potentially be modulated using small molecules or biologics. Two resources produced from these efforts are: The Target Central Resource Database (TCRD) (http://juniper.health.unm.edu/tcrd/) and Pharos (https://pharos.nih.gov/), a web interface to browse the TCRD. The ultimate goal of these resources is to highlight and facilitate research into currently understudied proteins, by aggregating a multitude of data sources, and ranking targets based on the amount of data available, and presenting data in machine learning ready format. Since the 2017 release, both TCRD and Pharos have produced two major releases, which have incorporated or expanded an additional 25 data sources. Recently incorporated data types include human and viral-human protein-protein interactions, protein-disease and protein-phenotype associations, and drug-induced gene signatures, among others. These aggregated data have enabled us to generate new visualizations and content sections in Pharos, in order to empower users to find new areas of study in the druggable genome.

Published
2021

17. CATMoS: Collaborative Acute Toxicity Modeling Suite

Author

Mansouri, K, Karmaus, A, Fitzpatrick, J, Patlewicz, G, Pradeep, P, Alberga, D, Alepee, N, Allen, T, Allen, D, Alves, V, Andrade, C, Auernhammer, T, Ballabio, D, Bell, S, Benfenati, E, Bhattacharya, S, Bastos, J, Boyd, S, Brown, J, Capuzzi, S, Chushak, Y, Ciallella, H, Clark, A, Consonni, V, Daga, P, Ekins, S, Farag, S, Fedorov, M, Fourches, D, Gadaleta, D, Gao, F, Gearhart, J, Goh, G, Goodman, J, Grisoni, F, Grulke, C, Hartung, T, Hirn, M, Karpov, P, Korotcov, A, Lavado, G, Lawless, M, Li, X, Luechtefeld, T, Lunghini, F, Mangiatordi, G, Marcou, G, Marsh, D, Martin, T, Mauri, A, Muratov, E, Myatt, G, Nguyen, D, Nicolotti, O, Note, R, Pande, P, Parks, A, Peryea, T, Polash, A, Rallo, R, Roncaglioni, A, Rowlands, C, Ruiz, P, Russo, D, Sayed, A, Sayre, R, Sheils, T, Siegel, C, Silva, A, Simeonov, A, Sosnin, S, Southall, N, Strickland, J, Tang, Y, Teppen, B, Tetko, I, Thomas, D, Tkachenko, V, Todeschini, R, Toma, C, Tripodi, I, Trisciuzzi, D, Tropsha, A, Varnek, A, Vukovic, K, Wang, Z, Wang, L, Waters, K, Wedlake, A, Wijeyesakere, S, Wilson, D, Xiao, Z, Yang, H, Zahoranszky-Kohalmi, G, Zakharov, A, Zhang, F, Zhang, Z, Zhao, T, Zhu, H, Zorn, K, Casey, W, Kleinstreuer, N, Mansouri, Kamel, Karmaus, Agnes L, Fitzpatrick, Jeremy, Patlewicz, Grace, Pradeep, Prachi, Alberga, Domenico, Alepee, Nathalie, Allen, Timothy E H, Allen, Dave, Alves, Vinicius M, Andrade, Carolina H, Auernhammer, Tyler R, Ballabio, Davide, Bell, Shannon, Benfenati, Emilio, Bhattacharya, Sudin, Bastos, Joyce V, Boyd, Stephen, Brown, J B, Capuzzi, Stephen J, Chushak, Yaroslav, Ciallella, Heather, Clark, Alex M, Consonni, Viviana, Daga, Pankaj R, Ekins, Sean, Farag, Sherif, Fedorov, Maxim, Fourches, Denis, Gadaleta, Domenico, Gao, Feng, Gearhart, Jeffery M, Goh, Garett, Goodman, Jonathan M, Grisoni, Francesca, Grulke, Christopher M, Hartung, Thomas, Hirn, Matthew, Karpov, Pavel, Korotcov, Alexandru, Lavado, Giovanna J, Lawless, Michael, Li, Xinhao, Luechtefeld, Thomas, Lunghini, Filippo, Mangiatordi, Giuseppe F, Marcou, Gilles, Marsh, Dan, Martin, Todd, Mauri, Andrea, Muratov, Eugene N, Myatt, Glenn J, Nguyen, Dac-Trung, Nicolotti, Orazio, Note, Reine, Pande, Paritosh, Parks, Amanda K, Peryea, Tyler, Polash, Ahsan H, Rallo, Robert, Roncaglioni, Alessandra, Rowlands, Craig, Ruiz, Patricia, Russo, Daniel P, Sayed, Ahmed, Sayre, Risa, Sheils, Timothy, Siegel, Charles, Silva, Arthur C, Simeonov, Anton, Sosnin, Sergey, Southall, Noel, Strickland, Judy, Tang, Yun, Teppen, Brian, Tetko, Igor V, Thomas, Dennis, Tkachenko, Valery, Todeschini, Roberto, Toma, Cosimo, Tripodi, Ignacio, Trisciuzzi, Daniela, Tropsha, Alexander, Varnek, Alexandre, Vukovic, Kristijan, Wang, Zhongyu, Wang, Liguo, Waters, Katrina M, Wedlake, Andrew J, Wijeyesakere, Sanjeeva J, Wilson, Dan, Xiao, Zijun, Yang, Hongbin, Zahoranszky-Kohalmi, Gergely, Zakharov, Alexey V, Zhang, Fagen F, Zhang, Zhen, Zhao, Tongan, Zhu, Hao, Zorn, Kimberley M, Casey, Warren, Kleinstreuer, Nicole C, Mansouri, K, Karmaus, A, Fitzpatrick, J, Patlewicz, G, Pradeep, P, Alberga, D, Alepee, N, Allen, T, Allen, D, Alves, V, Andrade, C, Auernhammer, T, Ballabio, D, Bell, S, Benfenati, E, Bhattacharya, S, Bastos, J, Boyd, S, Brown, J, Capuzzi, S, Chushak, Y, Ciallella, H, Clark, A, Consonni, V, Daga, P, Ekins, S, Farag, S, Fedorov, M, Fourches, D, Gadaleta, D, Gao, F, Gearhart, J, Goh, G, Goodman, J, Grisoni, F, Grulke, C, Hartung, T, Hirn, M, Karpov, P, Korotcov, A, Lavado, G, Lawless, M, Li, X, Luechtefeld, T, Lunghini, F, Mangiatordi, G, Marcou, G, Marsh, D, Martin, T, Mauri, A, Muratov, E, Myatt, G, Nguyen, D, Nicolotti, O, Note, R, Pande, P, Parks, A, Peryea, T, Polash, A, Rallo, R, Roncaglioni, A, Rowlands, C, Ruiz, P, Russo, D, Sayed, A, Sayre, R, Sheils, T, Siegel, C, Silva, A, Simeonov, A, Sosnin, S, Southall, N, Strickland, J, Tang, Y, Teppen, B, Tetko, I, Thomas, D, Tkachenko, V, Todeschini, R, Toma, C, Tripodi, I, Trisciuzzi, D, Tropsha, A, Varnek, A, Vukovic, K, Wang, Z, Wang, L, Waters, K, Wedlake, A, Wijeyesakere, S, Wilson, D, Xiao, Z, Yang, H, Zahoranszky-Kohalmi, G, Zakharov, A, Zhang, F, Zhang, Z, Zhao, T, Zhu, H, Zorn, K, Casey, W, Kleinstreuer, N, Mansouri, Kamel, Karmaus, Agnes L, Fitzpatrick, Jeremy, Patlewicz, Grace, Pradeep, Prachi, Alberga, Domenico, Alepee, Nathalie, Allen, Timothy E H, Allen, Dave, Alves, Vinicius M, Andrade, Carolina H, Auernhammer, Tyler R, Ballabio, Davide, Bell, Shannon, Benfenati, Emilio, Bhattacharya, Sudin, Bastos, Joyce V, Boyd, Stephen, Brown, J B, Capuzzi, Stephen J, Chushak, Yaroslav, Ciallella, Heather, Clark, Alex M, Consonni, Viviana, Daga, Pankaj R, Ekins, Sean, Farag, Sherif, Fedorov, Maxim, Fourches, Denis, Gadaleta, Domenico, Gao, Feng, Gearhart, Jeffery M, Goh, Garett, Goodman, Jonathan M, Grisoni, Francesca, Grulke, Christopher M, Hartung, Thomas, Hirn, Matthew, Karpov, Pavel, Korotcov, Alexandru, Lavado, Giovanna J, Lawless, Michael, Li, Xinhao, Luechtefeld, Thomas, Lunghini, Filippo, Mangiatordi, Giuseppe F, Marcou, Gilles, Marsh, Dan, Martin, Todd, Mauri, Andrea, Muratov, Eugene N, Myatt, Glenn J, Nguyen, Dac-Trung, Nicolotti, Orazio, Note, Reine, Pande, Paritosh, Parks, Amanda K, Peryea, Tyler, Polash, Ahsan H, Rallo, Robert, Roncaglioni, Alessandra, Rowlands, Craig, Ruiz, Patricia, Russo, Daniel P, Sayed, Ahmed, Sayre, Risa, Sheils, Timothy, Siegel, Charles, Silva, Arthur C, Simeonov, Anton, Sosnin, Sergey, Southall, Noel, Strickland, Judy, Tang, Yun, Teppen, Brian, Tetko, Igor V, Thomas, Dennis, Tkachenko, Valery, Todeschini, Roberto, Toma, Cosimo, Tripodi, Ignacio, Trisciuzzi, Daniela, Tropsha, Alexander, Varnek, Alexandre, Vukovic, Kristijan, Wang, Zhongyu, Wang, Liguo, Waters, Katrina M, Wedlake, Andrew J, Wijeyesakere, Sanjeeva J, Wilson, Dan, Xiao, Zijun, Yang, Hongbin, Zahoranszky-Kohalmi, Gergely, Zakharov, Alexey V, Zhang, Fagen F, Zhang, Zhen, Zhao, Tongan, Zhu, Hao, Zorn, Kimberley M, Casey, Warren, and Kleinstreuer, Nicole C

Abstract

Background: Humans are exposed to tens of thousands of chemical substances that need to be assessed for their potential toxicity. Acute systemic toxicity testing serves as the basis for regulatory hazard classification, labeling, and risk management. However, it is cost- and time-prohibitive to evaluate all new and existing chemicals using traditional rodent acute toxicity tests. In silica models built using existing data facilitate rapid acute tox- icity predictions without using animals. Objkctivks: The U.S. Interagency Coordinating Committee on the Validation of Alternative Methods (ICCVAM) Acute Toxicity Workgroup organ- ized an international collaboration to develop in silica models for predicting acute oral toxicity based on five different end points: Lethal Dose 50 (LD50 value, U.S. Environmental Protection Agency hazard (four) categories, Globally Harmonized System for Classification and Labeling hazard (five) categories, very toxic chemicals [LD50 (LD50 ≤ 50 mg/kg)], and nontoxic chemicals (LD50 > 2,000 mg/kg). Mkthods: An acute oral toxicity data inventory for 11,992 chemicals was compiled, split into training and evaluation sets, and made available to 35 participating international research groups that submitted a total of 139 predictive models. Predictions that fell within the applicability domains of the submitted models were evaluated using external validation sets. These were then combined into consensus models to leverage strengths of individual approaches. Results: The resulting consensus predictions, which leverage the collective strengths of each individual model, form the Collaborative Acute Toxicity Modeling Suite (CATMoS). CATMoS demonstrated high performance in terms of accuracy and robustness when compared with in viva results. Discussion: CATMoS is being evaluated by regulatory agencies for its utility and applicability as a potential replacement for in viva rat acute oral toxicity studies. CATMoS predictions for more than 800,000 chemica

Published
2021

18. CATMoS: Collaborative Acute Toxicity Modeling Suite

Author

Mansouri, Kamel, primary, Karmaus, Agnes L., additional, Fitzpatrick, Jeremy, additional, Patlewicz, Grace, additional, Pradeep, Prachi, additional, Alberga, Domenico, additional, Alepee, Nathalie, additional, Allen, Timothy E.H., additional, Allen, Dave, additional, Alves, Vinicius M., additional, Andrade, Carolina H., additional, Auernhammer, Tyler R., additional, Ballabio, Davide, additional, Bell, Shannon, additional, Benfenati, Emilio, additional, Bhattacharya, Sudin, additional, Bastos, Joyce V., additional, Boyd, Stephen, additional, Brown, J.B., additional, Capuzzi, Stephen J., additional, Chushak, Yaroslav, additional, Ciallella, Heather, additional, Clark, Alex M., additional, Consonni, Viviana, additional, Daga, Pankaj R., additional, Ekins, Sean, additional, Farag, Sherif, additional, Fedorov, Maxim, additional, Fourches, Denis, additional, Gadaleta, Domenico, additional, Gao, Feng, additional, Gearhart, Jeffery M., additional, Goh, Garett, additional, Goodman, Jonathan M., additional, Grisoni, Francesca, additional, Grulke, Christopher M., additional, Hartung, Thomas, additional, Hirn, Matthew, additional, Karpov, Pavel, additional, Korotcov, Alexandru, additional, Lavado, Giovanna J., additional, Lawless, Michael, additional, Li, Xinhao, additional, Luechtefeld, Thomas, additional, Lunghini, Filippo, additional, Mangiatordi, Giuseppe F., additional, Marcou, Gilles, additional, Marsh, Dan, additional, Martin, Todd, additional, Mauri, Andrea, additional, Muratov, Eugene N., additional, Myatt, Glenn J., additional, Nguyen, Dac-Trung, additional, Nicolotti, Orazio, additional, Note, Reine, additional, Pande, Paritosh, additional, Parks, Amanda K., additional, Peryea, Tyler, additional, Polash, Ahsan H., additional, Rallo, Robert, additional, Roncaglioni, Alessandra, additional, Rowlands, Craig, additional, Ruiz, Patricia, additional, Russo, Daniel P., additional, Sayed, Ahmed, additional, Sayre, Risa, additional, Sheils, Timothy, additional, Siegel, Charles, additional, Silva, Arthur C., additional, Simeonov, Anton, additional, Sosnin, Sergey, additional, Southall, Noel, additional, Strickland, Judy, additional, Tang, Yun, additional, Teppen, Brian, additional, Tetko, Igor V., additional, Thomas, Dennis, additional, Tkachenko, Valery, additional, Todeschini, Roberto, additional, Toma, Cosimo, additional, Tripodi, Ignacio, additional, Trisciuzzi, Daniela, additional, Tropsha, Alexander, additional, Varnek, Alexandre, additional, Vukovic, Kristijan, additional, Wang, Zhongyu, additional, Wang, Liguo, additional, Waters, Katrina M., additional, Wedlake, Andrew J., additional, Wijeyesakere, Sanjeeva J., additional, Wilson, Dan, additional, Xiao, Zijun, additional, Yang, Hongbin, additional, Zahoranszky-Kohalmi, Gergely, additional, Zakharov, Alexey V., additional, Zhang, Fagen F., additional, Zhang, Zhen, additional, Zhao, Tongan, additional, Zhu, Hao, additional, Zorn, Kimberley M., additional, Casey, Warren, additional, and Kleinstreuer, Nicole C., additional

Published
2021
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19. TCRD and Pharos 2021: mining the human proteome for disease biology

Author

Sheils, Timothy K, primary, Mathias, Stephen L, additional, Kelleher, Keith J, additional, Siramshetty, Vishal B, additional, Nguyen, Dac-Trung, additional, Bologa, Cristian G, additional, Jensen, Lars Juhl, additional, Vidović, Dušica, additional, Koleti, Amar, additional, Schürer, Stephan C, additional, Waller, Anna, additional, Yang, Jeremy J, additional, Holmes, Jayme, additional, Bocci, Giovanni, additional, Southall, Noel, additional, Dharkar, Poorva, additional, Mathé, Ewy, additional, Simeonov, Anton, additional, and Oprea, Tudor I, additional

Published
2020
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21. How to Illuminate the Druggable Genome Using Pharos

Author

Sheils, Timothy, Mathias, Stephen L., Siramshetty, Vishal B., Bocci, Giovanni, Bologa, Cristian G., Yang, Jeremy J., Waller, Anna, Southall, Noel, Nguyen, Dac Trung, Oprea, Tudor I., Sheils, Timothy, Mathias, Stephen L., Siramshetty, Vishal B., Bocci, Giovanni, Bologa, Cristian G., Yang, Jeremy J., Waller, Anna, Southall, Noel, Nguyen, Dac Trung, and Oprea, Tudor I.

Abstract

Pharos is an integrated web-based informatics platform for the analysis of data aggregated by the Illuminating the Druggable Genome (IDG) Knowledge Management Center, an NIH Common Fund initiative. The current version of Pharos (as of October 2019) spans 20,244 proteins in the human proteome, 19,880 disease and phenotype associations, and 226,829 ChEMBL compounds. This resource not only collates and analyzes data from over 60 high-quality resources to generate these types, but also uses text indexing to find less apparent connections between targets, and has recently begun to collaborate with institutions that generate data and resources. Proteins are ranked according to a knowledge-based classification system, which can help researchers to identify less studied “dark” targets that could be potentially further illuminated. This is an important process for both drug discovery and target validation, as more knowledge can accelerate target identification, and previously understudied proteins can serve as novel targets in drug discovery. Two basic protocols illustrate the levels of detail available for targets and several methods of finding targets of interest. An Alternate Protocol illustrates the difference in available knowledge between less and more studied targets.

Published
2020

22. SmartGraph:a network pharmacology investigation platform

Author

Zahoranszky-Kohalmi, Gergely, Sheils, Timothy, Oprea, Tudor I., Zahoranszky-Kohalmi, Gergely, Sheils, Timothy, and Oprea, Tudor I.

Abstract

Motivation Drug discovery investigations need to incorporate network pharmacology concepts while navigating the complex landscape of drug-target and target-target interactions. This task requires solutions that integrate high-quality biomedical data, combined with analytic and predictive workflows as well as efficient visualization. SmartGraph is an innovative platform that utilizes state-of-the-art technologies such as a Neo4j graph-database, Angular web framework, RxJS asynchronous event library and D3 visualization to accomplish these goals. Results The SmartGraph framework integrates high quality bioactivity data and biological pathway information resulting in a knowledgebase comprised of 420,526 unique compound-target interactions defined between 271,098 unique compounds and 2018 targets. SmartGraph then performs bioactivity predictions based on the 63,783 Bemis-Murcko scaffolds extracted from these compounds. Through several use-cases, we illustrate the use of SmartGraph to generate hypotheses for elucidating mechanism-of-action, drug-repurposing and off-target prediction. Availability: https://smartgraph.ncats.io/. .

Published
2020

23. How to Illuminate the Druggable Genome Using Pharos

Author

Sheils, Timothy, primary, Mathias, Stephen L., additional, Siramshetty, Vishal B., additional, Bocci, Giovanni, additional, Bologa, Cristian G., additional, Yang, Jeremy J., additional, Waller, Anna, additional, Southall, Noel, additional, Nguyen, Dac‐Trung, additional, and Oprea, Tudor I., additional

Published
2020
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26. Pharos:Collating protein information to shed light on the druggable genome

Author

Nguyen, Dac-Trung, Mathias, Stephen, Bologa, Cristian, Brunak, Soren, Fernandez, Nicolas, Gaulton, Anna, Hersey, Anne, Holmes, Jayme, Jensen, Lars Juhl, Karlsson, Anneli, Liu, Guixia, Ma'ayan, Avi, Mandava, Geetha, Mani, Subramani, Mehta, Saurabh, Overington, John, Patel, Juhee, Rouillard, Andrew D, Schürer, Stephan, Sheils, Timothy, Simeonov, Anton, Sklar, Larry A, Southall, Noel, Ursu, Oleg, Vidovic, Dusica, Waller, Anna, Yang, Jeremy, Jadhav, Ajit, Oprea, Tudor I, Guha, Rajarshi, Nguyen, Dac-Trung, Mathias, Stephen, Bologa, Cristian, Brunak, Soren, Fernandez, Nicolas, Gaulton, Anna, Hersey, Anne, Holmes, Jayme, Jensen, Lars Juhl, Karlsson, Anneli, Liu, Guixia, Ma'ayan, Avi, Mandava, Geetha, Mani, Subramani, Mehta, Saurabh, Overington, John, Patel, Juhee, Rouillard, Andrew D, Schürer, Stephan, Sheils, Timothy, Simeonov, Anton, Sklar, Larry A, Southall, Noel, Ursu, Oleg, Vidovic, Dusica, Waller, Anna, Yang, Jeremy, Jadhav, Ajit, Oprea, Tudor I, and Guha, Rajarshi

Abstract

The 'druggable genome' encompasses several protein families, but only a subset of targets within them have attracted significant research attention and thus have information about them publicly available. The Illuminating the Druggable Genome (IDG) program was initiated in 2014, has the goal of developing experimental techniques and a Knowledge Management Center (KMC) that would collect and organize information about protein targets from four families, representing the most common druggable targets with an emphasis on understudied proteins. Here, we describe two resources developed by the KMC: the Target Central Resource Database (TCRD) which collates many heterogeneous gene/protein datasets and Pharos (https://pharos.nih.gov), a multimodal web interface that presents the data from TCRD. We briefly describe the types and sources of data considered by the KMC and then highlight features of the Pharos interface designed to enable intuitive access to the IDG knowledgebase. The aim of Pharos is to encourage 'serendipitous browsing', whereby related, relevant information is made easily discoverable. We conclude by describing two use cases that highlight the utility of Pharos and TCRD.

Published
2017

28. Pharos: Collating protein information to shed light on the druggable genome

Author

Nguyen, Dac-Trung, primary, Mathias, Stephen, additional, Bologa, Cristian, additional, Brunak, Soren, additional, Fernandez, Nicolas, additional, Gaulton, Anna, additional, Hersey, Anne, additional, Holmes, Jayme, additional, Jensen, Lars Juhl, additional, Karlsson, Anneli, additional, Liu, Guixia, additional, Ma'ayan, Avi, additional, Mandava, Geetha, additional, Mani, Subramani, additional, Mehta, Saurabh, additional, Overington, John, additional, Patel, Juhee, additional, Rouillard, Andrew D., additional, Schürer, Stephan, additional, Sheils, Timothy, additional, Simeonov, Anton, additional, Sklar, Larry A., additional, Southall, Noel, additional, Ursu, Oleg, additional, Vidovic, Dusica, additional, Waller, Anna, additional, Yang, Jeremy, additional, Jadhav, Ajit, additional, Oprea, Tudor I., additional, and Guha, Rajarshi, additional

Published
2016
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29. Pharos: Collating protein information to shed light on the druggable genome.

Author

Dac-Trung Nguyen, Mathias, Stephen, Bologa, Cristian, Brunak, Soren, Fernandez, Nicolas, Gaulton, Anna, Hersey, Anne, Holmes, Jayme, Jensen, Lars Juhl, Karlsson, Anneli, Guixia Liu, Ma'ayan, Avi, Mandava, Geetha, Mani, Subramani, Mehta, Saurabh, Overington, John, Patel, Juhee, Rouillard, Andrew D., Schürer, Stephan, and Sheils, Timothy

Published
2017
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30. CATMoS: Collaborative Acute Toxicity Modeling Suite

Author

Mansouri, Kamel, Karmaus, Agnes L, Fitzpatrick, Jeremy, Patlewicz, Grace, Pradeep, Prachi, Alberga, Domenico, Alepee, Nathalie, Allen, Timothy EH, Allen, Dave, Alves, Vinicius M, Andrade, Carolina H, Auernhammer, Tyler R, Ballabio, Davide, Bell, Shannon, Benfenati, Emilio, Bhattacharya, Sudin, Bastos, Joyce V, Boyd, Stephen, Brown, JB, Capuzzi, Stephen J, Chushak, Yaroslav, Ciallella, Heather, Clark, Alex M, Consonni, Viviana, Daga, Pankaj R, Ekins, Sean, Farag, Sherif, Fedorov, Maxim, Fourches, Denis, Gadaleta, Domenico, Gao, Feng, Gearhart, Jeffery M, Goh, Garett, Goodman, Jonathan M, Grisoni, Francesca, Grulke, Christopher M, Hartung, Thomas, Hirn, Matthew, Karpov, Pavel, Korotcov, Alexandru, Lavado, Giovanna J, Lawless, Michael, Li, Xinhao, Luechtefeld, Thomas, Lunghini, Filippo, Mangiatordi, Giuseppe F, Marcou, Gilles, Marsh, Dan, Martin, Todd, Mauri, Andrea, Muratov, Eugene N, Myatt, Glenn J, Nguyen, Dac-Trung, Nicolotti, Orazio, Note, Reine, Pande, Paritosh, Parks, Amanda K, Peryea, Tyler, Polash, Ahsan H, Rallo, Robert, Roncaglioni, Alessandra, Rowlands, Craig, Ruiz, Patricia, Russo, Daniel P, Sayed, Ahmed, Sayre, Risa, Sheils, Timothy, Siegel, Charles, Silva, Arthur C, Simeonov, Anton, Sosnin, Sergey, Southall, Noel, Strickland, Judy, Tang, Yun, Teppen, Brian, Tetko, Igor V, Thomas, Dennis, Tkachenko, Valery, Todeschini, Roberto, Toma, Cosimo, Tripodi, Ignacio, Trisciuzzi, Daniela, Tropsha, Alexander, Varnek, Alexandre, Vukovic, Kristijan, Wang, Zhongyu, Wang, Liguo, Waters, Katrina M, Wedlake, Andrew J, Wijeyesakere, Sanjeeva J, Wilson, Dan, Xiao, Zijun, Yang, Hongbin, Zahoranszky-Kohalmi, Gergely, Zakharov, Alexey V, Zhang, Fagen F, Zhang, Zhen, Zhao, Tongan, Zhu, Hao, Zorn, Kimberley M, Casey, Warren, and Kleinstreuer, Nicole C

Subjects
Government Agencies, 13. Climate action, Toxicity Tests, Acute, Animals, Computer Simulation, United States Environmental Protection Agency, United States, 3. Good health, Rats
Abstract

BACKGROUND: Humans are exposed to tens of thousands of chemical substances that need to be assessed for their potential toxicity. Acute systemic toxicity testing serves as the basis for regulatory hazard classification, labeling, and risk management. However, it is cost- and time-prohibitive to evaluate all new and existing chemicals using traditional rodent acute toxicity tests. In silico models built using existing data facilitate rapid acute toxicity predictions without using animals. OBJECTIVES: The U.S. Interagency Coordinating Committee on the Validation of Alternative Methods (ICCVAM) Acute Toxicity Workgroup organized an international collaboration to develop in silico models for predicting acute oral toxicity based on five different end points: Lethal Dose 50 (LD50 value, U.S. Environmental Protection Agency hazard (four) categories, Globally Harmonized System for Classification and Labeling hazard (five) categories, very toxic chemicals [LD50 (LD50≤50mg/kg)], and nontoxic chemicals (LD50>2,000mg/kg). METHODS: An acute oral toxicity data inventory for 11,992 chemicals was compiled, split into training and evaluation sets, and made available to 35 participating international research groups that submitted a total of 139 predictive models. Predictions that fell within the applicability domains of the submitted models were evaluated using external validation sets. These were then combined into consensus models to leverage strengths of individual approaches. RESULTS: The resulting consensus predictions, which leverage the collective strengths of each individual model, form the Collaborative Acute Toxicity Modeling Suite (CATMoS). CATMoS demonstrated high performance in terms of accuracy and robustness when compared with in vivo results. DISCUSSION: CATMoS is being evaluated by regulatory agencies for its utility and applicability as a potential replacement for in vivo rat acute oral toxicity studies. CATMoS predictions for more than 800,000 chemicals have been made available via the National Toxicology Program's Integrated Chemical Environment tools and data sets (ice.ntp.niehs.nih.gov). The models are also implemented in a free, standalone, open-source tool, OPERA, which allows predictions of new and untested chemicals to be made. https://doi.org/10.1289/EHP8495.

31. RaMP-DB 2.0: a renovated knowledgebase for deriving biological and chemical insight from metabolites, proteins, and genes.

Author

Braisted J, Patt A, Tindall C, Sheils T, Neyra J, Spencer K, Eicher T, and Mathé EA

Subjects
Databases, Factual, Gene Expression Profiling, Knowledge Bases, Proteins, Software, Metabolomics
Abstract

Motivation: Functional interpretation of high-throughput metabolomic and transcriptomic results is a crucial step in generating insight from experimental data. However, pathway and functional information for genes and metabolites are distributed among many siloed resources, limiting the scope of analyses that rely on a single knowledge source., Results: RaMP-DB 2.0 is a web interface, relational database, API and R package designed for straightforward and comprehensive functional interpretation of metabolomic and multi-omic data. RaMP-DB 2.0 has been upgraded with an expanded breadth and depth of functional and chemical annotations (ClassyFire, LIPID MAPS, SMILES, InChIs, etc.), with new data types related to metabolites and lipids incorporated. To streamline entity resolution across multiple source databases, we have implemented a new semi-automated process, thereby lessening the burden of harmonization and supporting more frequent updates. The associated RaMP-DB 2.0 R package now supports queries on pathways, common reactions (e.g. metabolite-enzyme relationship), chemical functional ontologies, chemical classes and chemical structures, as well as enrichment analyses on pathways (multi-omic) and chemical classes. Lastly, the RaMP-DB web interface has been completely redesigned using the Angular framework., Availability and Implementation: The code used to build all components of RaMP-DB 2.0 are freely available on GitHub at https://github.com/ncats/ramp-db, https://github.com/ncats/RaMP-Client/ and https://github.com/ncats/RaMP-Backend. The RaMP-DB web application can be accessed at https://rampdb.nih.gov/., Supplementary Information: Supplementary data are available at Bioinformatics online., (Published by Oxford University Press 2022.)

Published
2023
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32. NCATS Inxight Drugs: a comprehensive and curated portal for translational research.

Author

Siramshetty VB, Grishagin I, Nguyễn ÐT, Peryea T, Skovpen Y, Stroganov O, Katzel D, Sheils T, Jadhav A, Mathé EA, and Southall NT

Subjects
Humans, National Center for Advancing Translational Sciences (U.S.), Translational Research, Biomedical classification, United States, Databases, Factual, Databases, Pharmaceutical, Pharmaceutical Preparations classification, United States Food and Drug Administration
Abstract

The United States has a complex regulatory scheme for marketing drugs. Understanding drug regulatory status is a daunting task that requires integrating data from many sources from the United States Food and Drug Administration (FDA), US government publications, and other processes related to drug development. At NCATS, we created Inxight Drugs (https://drugs.ncats.io), a web resource that attempts to address this challenge in a systematic manner. NCATS Inxight Drugs incorporates and unifies a wealth of data, including those supplied by the FDA and from independent public sources. The database offers a substantial amount of manually curated literature data unavailable from other sources. Currently, the database contains 125 036 product ingredients, including 2566 US approved drugs, 6242 marketed drugs, and 9684 investigational drugs. All substances are rigorously defined according to the ISO 11238 standard to comply with existing regulatory standards for unique drug substance identification. A special emphasis was placed on capturing manually curated and referenced data on treatment modalities and semantic relationships between substances. A supplementary resource 'Novel FDA Drug Approvals' features regulatory details of newly approved FDA drugs. The database is regularly updated using NCATS Stitcher data integration tool that automates data aggregation and supports full data access through a RESTful API., (Published by Oxford University Press on behalf of Nucleic Acids Research 2021.)

Published
2022
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33. TCRD and Pharos 2021: mining the human proteome for disease biology.

Author

Sheils TK, Mathias SL, Kelleher KJ, Siramshetty VB, Nguyen DT, Bologa CG, Jensen LJ, Vidović D, Koleti A, Schürer SC, Waller A, Yang JJ, Holmes J, Bocci G, Southall N, Dharkar P, Mathé E, Simeonov A, and Oprea TI

Subjects
Animals, Anticonvulsants chemistry, Anticonvulsants therapeutic use, Antiviral Agents chemistry, Antiviral Agents therapeutic use, Biological Products chemistry, Biological Products therapeutic use, Data Mining statistics & numerical data, Host-Pathogen Interactions drug effects, Host-Pathogen Interactions genetics, Humans, Internet, Machine Learning statistics & numerical data, Mice, Mice, Knockout, Molecular Targeted Therapy methods, Neurodegenerative Diseases classification, Neurodegenerative Diseases drug therapy, Neurodegenerative Diseases virology, Protein Interaction Mapping, Proteome agonists, Proteome antagonists & inhibitors, Proteome genetics, Proteome metabolism, Small Molecule Libraries chemistry, Small Molecule Libraries therapeutic use, Virus Diseases classification, Virus Diseases drug therapy, Virus Diseases virology, Databases, Factual, Genome, Human, Neurodegenerative Diseases genetics, Proteomics methods, Software, Virus Diseases genetics
Abstract

In 2014, the National Institutes of Health (NIH) initiated the Illuminating the Druggable Genome (IDG) program to identify and improve our understanding of poorly characterized proteins that can potentially be modulated using small molecules or biologics. Two resources produced from these efforts are: The Target Central Resource Database (TCRD) (http://juniper.health.unm.edu/tcrd/) and Pharos (https://pharos.nih.gov/), a web interface to browse the TCRD. The ultimate goal of these resources is to highlight and facilitate research into currently understudied proteins, by aggregating a multitude of data sources, and ranking targets based on the amount of data available, and presenting data in machine learning ready format. Since the 2017 release, both TCRD and Pharos have produced two major releases, which have incorporated or expanded an additional 25 data sources. Recently incorporated data types include human and viral-human protein-protein interactions, protein-disease and protein-phenotype associations, and drug-induced gene signatures, among others. These aggregated data have enabled us to generate new visualizations and content sections in Pharos, in order to empower users to find new areas of study in the druggable genome., (© The Author(s) 2020. Published by Oxford University Press on behalf of Nucleic Acids Research.)

Published
2021
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34. Pharos: Collating protein information to shed light on the druggable genome.

Author

Nguyen DT, Mathias S, Bologa C, Brunak S, Fernandez N, Gaulton A, Hersey A, Holmes J, Jensen LJ, Karlsson A, Liu G, Ma'ayan A, Mandava G, Mani S, Mehta S, Overington J, Patel J, Rouillard AD, Schürer S, Sheils T, Simeonov A, Sklar LA, Southall N, Ursu O, Vidovic D, Waller A, Yang J, Jadhav A, Oprea TI, and Guha R

Subjects
Cluster Analysis, Computational Biology methods, Humans, Obesity drug therapy, Obesity genetics, Obesity metabolism, Software, Web Browser, Databases, Genetic, Drug Discovery methods, Genomics methods, Pharmacogenetics methods, Search Engine
Abstract

The 'druggable genome' encompasses several protein families, but only a subset of targets within them have attracted significant research attention and thus have information about them publicly available. The Illuminating the Druggable Genome (IDG) program was initiated in 2014, has the goal of developing experimental techniques and a Knowledge Management Center (KMC) that would collect and organize information about protein targets from four families, representing the most common druggable targets with an emphasis on understudied proteins. Here, we describe two resources developed by the KMC: the Target Central Resource Database (TCRD) which collates many heterogeneous gene/protein datasets and Pharos (https://pharos.nih.gov), a multimodal web interface that presents the data from TCRD. We briefly describe the types and sources of data considered by the KMC and then highlight features of the Pharos interface designed to enable intuitive access to the IDG knowledgebase. The aim of Pharos is to encourage 'serendipitous browsing', whereby related, relevant information is made easily discoverable. We conclude by describing two use cases that highlight the utility of Pharos and TCRD., (Published by Oxford University Press on behalf of Nucleic Acids Research 2016. This work is written by (a) US Government employee(s) and is in the public domain in the US.)

Published
2017
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