Open Source Bayesian Models. 1. Application to ADME/Tox and Drug Discovery Datasets
Open accessAbstract
On the order of hundreds of absorption, distribution, metabolism, excretion, and toxicity (ADME/Tox) models have been described in the literature in the past decade which are more often than not inaccessible to anyone but their authors. Public accessibility is also an issue with computational models for bioactivity, and the ability to share such models still remains a major challenge limiting drug discovery. We describe the creation of a reference implementation of a Bayesian model-building software module, which we have released as an open source component that is now included in the Chemistry Development Kit (CDK) project, as well as implemented in the CDD Vault and in several mobile apps. We use this implementation to build an array of Bayesian models for ADME/Tox, in vitro and in vivo bioactivity, and other physicochemical properties. We show that these models possess cross-validation receiver operator curve values comparable to those generated previously in prior publications using alternative tools. We have now described how the implementation of Bayesian models with FCFP6 descriptors generated in the CDD Vault enables the rapid production of robust machine learning models from public data or the user's own datasets. The current study sets the stage for generating models in proprietary software (such as CDD) and exporting these models in a format that could be run in open source software using CDK components. This work also demonstrates that we can enable biocomputation across distributed private or public datasets to enhance drug discovery.
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The Bayesian approach has been widely applied to ADME properties by predicting aqueous solubility, mouse liver microsomal stability, Caco-2 cell permeability, cytotoxicity and interactions with transporters.
“This Bayesian approach has also been widely applied to ADME properties by predicting aqueous solubility, mouse liver microsomal stability42, Caco-2 cell permeability43, cytotoxicity44 and interactions with transporters45.”
From Exploiting machine learning for end-to-end drug discovery and development (2019) · cites this paper as [43]
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Spjuth, O., Alvarsson, J., Berg, A., et al. (2009) · BMC Bioinformatics · doi:10.1186/1471-2105-10-397 · Cited by 62 · see citing works
In silico approaches to predicting drug metabolism, toxicology and beyond
Ekins, S. (2003) · Biochemical Society Transactions · doi:10.1042/bst0310611 · Cited by 62 · see citing works
Self-citationMobile apps for chemistry in the world of drug discovery
Williams, A. J., Ekins, S., Clark, A. M., et al. (2011) · Drug Discovery Today · doi:10.1016/j.drudis.2011.09.002 · Cited by 61 · see citing works
Self-citationIncorporating Green Chemistry Concepts into Mobile Chemistry Applications and Their Potential Uses
Ekins, S., Clark, A. M., & Williams, A. J. (2012) · ACS Sustainable Chemistry & Engineering · doi:10.1021/sc3000509 · Cited by 61 · see citing works
Self-citationComputational Approaches That Predict Metabolic Intermediate Complex Formation with CYP3A4 (+b5)
Jones, D. R., Ekins, S., Li, L., & Hall, S. D. (2007) · Drug Metabolism and Disposition · doi:10.1124/dmd.106.014613 · Cited by 60 · see citing works
Self-citationEkins, S., Freundlich, J. S., Hobrath, J. V., White, E. L., & Reynolds, R. C. (2013) · Pharmaceutical Research · doi:10.1007/s11095-013-1172-7 · Cited by 57 · see citing works
Self-citationHybrid Scoring and Classification Approaches to Predict Human Pregnane X Receptor Activators
Kortagere, S., Chekmarev, D. S., Welsh, W. J., & Ekins, S. (2008) · Pharmaceutical Research · doi:10.1007/s11095-008-9809-7 · Cited by 56 · see citing works
Self-citationDu, F., Yu, H., Zou, B., et al. (2011) · Assay and Drug Development Technologies · doi:10.1089/adt.2011.0425 · Cited by 51 · see citing works
Implementation of an ADME enabling selection and visualization tool for drug discovery
Stoner, C. L., Gifford, E., Stankovic, C. J., Lepsy, C. S., Brodfuehrer, J., Prasad, J. V. N. V., & Surendran, N. (2004) · Journal of Pharmaceutical Sciences · doi:10.1002/jps.20020 · Cited by 50 · see citing works
Integrated in Silico−in Vitro Strategy for Addressing Cytochrome P450 3A4 Time-Dependent Inhibition
Zientek, M. A., Stoner, C. L., Ayscue, R. R., et al. (2010) · Chemical Research in Toxicology · doi:10.1021/tx900417f · Cited by 50 · see citing works
Comprehensive Computational Assessment of ADME Properties Using Mapping Techniques
Balakin, K. V., Ivanenkov, Y. A., Savchuk, N., Ivashchenko, A. A., & Ekins, S. (2005) · Current Drug Discovery Technologies · doi:10.2174/1570163054064666 · Cited by 50 · see citing works
Self-citationFinding Promiscuous Old Drugs for New Uses
Ekins, S., & Williams, A. J. (2011) · Pharmaceutical Research · doi:10.1007/s11095-011-0486-6 · Cited by 50 · see citing works
Self-citationHu, Y., Unwalla, R. J., Denny, R. A., Bikker, J. A., Di, L., & Humblet, C. C. (2009) · Journal of Computer-Aided Molecular Design · doi:10.1007/s10822-009-9309-9 · Cited by 49 · see citing works
Hajjo, R., Grulke, C., Golbraikh, A., Setola, V., Huang, X., Roth, B. L., & Tropsha, A. (2010) · Journal of Medicinal Chemistry · doi:10.1021/jm100600y · Cited by 49 · see citing works
Towards interoperable and reproducible QSAR analyses: Exchange of datasets
Spjuth, O., Willighagen, E., Guha, R., Eklund, M., & Wikberg, J. E. S. (2010) · Journal of Cheminformatics · doi:10.1186/1758-2946-2-5 · Cited by 48 · see citing works
The Chembl Database: A Taster for Medicinal Chemists
Papadatos, G., & Overington, J. P. (2014) · Future Medicinal Chemistry · doi:10.4155/fmc.14.8 · Cited by 48 · see citing works
Guha, R., & Schürer, S. C. (2008) · Journal of Computer-Aided Molecular Design · doi:10.1007/s10822-008-9192-9 · Cited by 47 · see citing works
Computational mapping tools for drug discovery
Ivanenkov, Y. A., Savchuk, N., Ekins, S., & Balakin, K. V. (2009) · Drug Discovery Today · doi:10.1016/j.drudis.2009.05.016 · Cited by 47 · see citing works
Self-citationEthell, B. T., Ekins, S., Wang, J., et al. (2002) · Drug Metabolism and Disposition · doi:10.1124/dmd.30.6.734 · Cited by 47 · see citing works
Self-citationEkins, S., Pottorf, R. S., Reynolds, R. C., Williams, A. J., Clark, A. M., & Freundlich, J. S. (2014) · Journal of Chemical Information and Modeling · doi:10.1021/ci500077v · Cited by 43 · see citing works
Self-citationKOHONEN MAPS FOR PREDICTION OF BINDING TO HUMAN CYTOCHROME P450 3A4
Balakin, K. V., Ekins, S., Bugrim, A., Ivanenkov, Y. A., Korolev, D., Nikolsky, Y., Skorenko, A. V., Ivashchenko, A. A., Savchuk, N., & Nikolskaya, T. (2004) · Drug Metabolism and Disposition · doi:10.1124/dmd.104.000356 · Cited by 43 · see citing works
Self-citationPan, Y., Li, L., Kim, G., Ekins, S., Wang, H., & Swaan, P. W. (2010) · Drug Metabolism and Disposition · doi:10.1124/dmd.110.035808 · Cited by 42 · see citing works
Self-citationEkins, S. (2006) · Wiley eBooks · doi:10.1002/9780470145890 · Cited by 42 · see citing works
Self-citationEkins, S., Freundlich, J. S., & Reynolds, R. C. (2014) · Journal of Chemical Information and Modeling · doi:10.1021/ci500264r · Cited by 41 · see citing works
Self-citationWeb Service Infrastructure for Chemoinformatics
Dong, X. P., Gilbert, K. E., Guha, R., et al. (2007) · Journal of Chemical Information and Modeling · doi:10.1021/ci6004349 · Cited by 40 · see citing works
Open Drug Discovery Teams: A Chemistry Mobile App for Collaboration
Ekins, S., Clark, A. M., & Williams, A. J. (2012) · Molecular Informatics · doi:10.1002/minf.201200034 · Cited by 38 · see citing works
Self-citationSimilarity based SAR (SIBAR) as tool for early ADME profiling
Klein, C., Kaiser, D., Kopp, S., Chiba, P., & Ecker, G. F. (2002) · Journal of Computer-Aided Molecular Design · doi:10.1023/a:1023828527638 · Cited by 33 · see citing works
TB Mobile: a mobile app for anti-tuberculosis molecules with known targets
Ekins, S., Clark, A. M., & Sarker, M. (2013) · Journal of Cheminformatics · doi:10.1186/1758-2946-5-13 · Cited by 33 · see citing works
Self-citationClark, A. M., Sarker, M., & Ekins, S. (2014) · Journal of Cheminformatics · doi:10.1186/s13321-014-0038-2 · Cited by 33 · see citing works
Self-citationQUANTITATIVE STRUCTURE-METABOLISM RELATIONSHIP MODELING OF METABOLIC N-DEALKYLATION REACTION RATES
Balakin, K. V., Ekins, S., Bugrim, A., et al. (2004) · Drug Metabolism and Disposition · doi:10.1124/dmd.104.000364 · Cited by 33 · see citing works
Self-citationTowards a new age of virtual ADME/TOX and multidimensional drug discovery
Ekins, S., Boulanger, B., Swaan, P. W., & Hupcey, M. A. Z. (2000) · Molecular Diversity · doi:10.1023/a:1021376212320 · Cited by 32 · see citing works
Self-citationThe Collaborative Drug Discovery (CDD) Database
Ekins, S., & Bunin, B. A. (2013) · Methods in molecular biology · doi:10.1007/978-1-62703-342-8_10 · Cited by 30 · see citing works
Self-citationClassification of Metabolites with Kernel-Partial Least Squares (K-PLS)
Embrechts, M. J., & Ekins, S. (2006) · Drug Metabolism and Disposition · doi:10.1124/dmd.106.013185 · Cited by 30 · see citing works
Self-citationEfficient ring perception for the Chemistry Development Kit
May, J. W., & Steinbeck, C. (2014) · Journal of Cheminformatics · doi:10.1186/1758-2946-6-3 · Cited by 30 · see citing works
Redefining Cheminformatics with Intuitive Collaborative Mobile Apps
Clark, A. M., Ekins, S., & Williams, A. J. (2012) · Molecular Informatics · doi:10.1002/minf.201200010 · Cited by 27 · see citing works
Self-citationBigger data, collaborative tools and the future of predictive drug discovery
Ekins, S., Clark, A. M., Swamidass, S. J., et al. (2014) · Journal of Computer-Aided Molecular Design · doi:10.1007/s10822-014-9762-y · Cited by 27 · see citing works
Self-citationSun, H., Veith, H., Xia, M., Austin, C. P., Tice, R. R., & Huang, R. (2012) · Molecular Informatics · doi:10.1002/minf.201200065 · Cited by 24 · see citing works
Monitoring Compound Integrity With Cytochrome P450 Assays and qHTS
MacArthur, R., Leister, W., Veith, H., Shinn, P., Southall, N. T., Austin, C. P., Inglese, J., & Auld, D. S. (2009) · SLAS DISCOVERY · doi:10.1177/1087057109336954 · Cited by 23 · see citing works
The Development and Validation of a Computational Model to Predict Rat Liver Microsomal Clearance
Chang, C., Duignan, D. B., Johnson, K. D., et al. (2009) · Journal of Pharmaceutical Sciences · doi:10.1002/jps.21651 · Cited by 23 · see citing works
Computational Prediction and Validation of an Expert’s Evaluation of Chemical Probes
Litterman, N. K., Lipinski, C. A., Bunin, B. A., et al. (2014) · Journal of Chemical Information and Modeling · doi:10.1021/ci500445u · Cited by 22 · see citing works
Ekins, S., & Williams, A. J. (2010) · MedChemComm · doi:10.1039/c0md00129e · Cited by 22 · see citing works
Self-citationSuzuki, T., Kameda, M., Ando, M., et al. (2009) · Bioorganic & Medicinal Chemistry Letters · doi:10.1016/j.bmcl.2009.07.132 · Cited by 19 · see citing works
Ekins, S., Diao, L., & Polli, J. E. (2012) · Molecular Pharmaceutics · doi:10.1021/mp200438v · Cited by 17 · see citing works
Self-citationCombined Analysis of Phenotypic and Target-Based Screening in Assay Networks
Swamidass, S. J., Schillebeeckx, C., Matlock, M. K., Hurle, M. R., & Agarwal, P. (2014) · SLAS DISCOVERY · doi:10.1177/1087057114523068 · Cited by 17 · see citing works
Cheminformatics workflows using mobile apps
Clark, A. M., Williams, A. J., & Ekins, S. (2013) · Chem-Bio Informatics Journal · doi:10.1273/cbij.13.1 · Cited by 15 · see citing works
Self-citationTemesi, D. G., Martin, S. W., Smith, R., Jones, C. R., & Middleton, B. J. (2010) · Rapid Communications in Mass Spectrometry · doi:10.1002/rcm.4546 · Cited by 14 · see citing works
Wessel, M. D., & Mente, S. (2001) · Annual reports in medicinal chemistry · doi:10.1016/s0065-7743(01)36065-7 · Cited by 13 · see citing works
Ekins, S., Embrechts, M. J., Breneman, C. M., et al. (2006) · Computational Toxicology · doi:10.1002/9780470145890.ch15 · Cited by 11 · see citing works
Self-citationPharmacophores for Human ADME/Tox‐Related Proteins
Chang, C. K., & Ekins, S. (2006) · Methods and principles in medicinal chemistry · doi:10.1002/3527609164.ch14 · Cited by 10 · see citing works
Self-citationSecurely Measuring the Overlap between Private Datasets with Cryptosets
Swamidass, S. J., Matlock, M. K., & Rozenblit, L. (2015) · PLoS ONE · doi:10.1371/journal.pone.0117898 · Cited by 8 · see citing works
Sharing Chemical Relationships Does Not Reveal Structures
Matlock, M. K., & Swamidass, S. J. (2013) · Journal of Chemical Information and Modeling · doi:10.1021/ci400399a · Cited by 8 · see citing works
In Silico Tools for Drug Absorption Prediction
Paranjpe, P. V., Grass, G. M., & Sinko, P. J. (2003) · American Journal of Drug Delivery · doi:10.2165/00137696-200301020-00005 · Cited by 7 · see citing works
Self-citationAccessing, Using, and Creating Chemical Property Databases for Computational Toxicology Modeling
Williams, A. J., Ekins, S., Spjuth, O., et al. (2012) · Methods in molecular biology · doi:10.1007/978-1-62703-050-2_10 · Cited by 7 · see citing works
Self-citation
Flagged first, then by citations
Research works printed but not linked: 3
- Wessel M. D.; Mente S. ADME by computer. Annu. Rep. Med. Chem. 2001, 36, 257– 266.
- Ekins S.; Swaan P. W. Computational models for enzymes, transporters, channels and receptors relevant to ADME/TOX. Rev. Comput. Chem. 2004, 20, 333– 415.
- Litterman N. K.; Lipinski C. A.; Bunin B. A.; Ekins S. Computational Prediction and Validation of an Expert’s Evaluation of Chemical Probes. J. Chem. Inf. Model. 2014, 54, 2996– 3004. 25244007 PMC4955571
Cited by
Sorted by citations
Daina, A., Michielin, O., & Zoete, V. (2017) · Scientific Reports · doi:10.1038/srep42717 · Cited by 18,367 · see citing works
Exploiting machine learning for end-to-end drug discovery and development
Ekins, S., Puhl, A. C., Zorn, K. M., Lane, T. R., Russo, D. P., Klein, J. J., Hickey, A. J., & Clark, A. M. (2019) · Nature Materials · doi:10.1038/s41563-019-0338-z · Cited by 578 · see citing works
Self-citationComparing Multiple Machine Learning Algorithms and Metrics for Estrogen Receptor Binding Prediction
Russo, D. P., Zorn, K. M., Clark, A. M., Zhu, H., & Ekins, S. (2018) · Molecular Pharmaceutics · doi:10.1021/acs.molpharmaceut.8b00546 · Cited by 152 · see citing works
Self-citationToxicity prediction based on artificial intelligence: A multidisciplinary overview
Pérez Santín, E., Rodríguez Solana, R., González García, M., García Suárez, M. D. M., Blanco Díaz, G. D., Cima Cabal, M. D., Moreno Rojas, J. M., & López Sánchez, J. I. (2021) · WIREs Computational Molecular Science · doi:10.1002/wcms.1516 · Cited by 153 · see citing works
Machine learning models identify molecules active against the Ebola virus in vitro
Ekins, S., Freundlich, J. S., Clark, A. M., Anantpadma, M., Davey, R. A., & Madrid, P. (2015) · F1000Research · doi:10.12688/f1000research.7217.1 · Cited by 84 · see citing works
Self-citationArtificial intelligence facilitates drug design in the big data era
Wang, L., Ding, J., Pan, L., Cao, D., Jiang, H., & Ding, X. (2019) · Chemometrics and Intelligent Laboratory Systems · doi:10.1016/j.chemolab.2019.103850 · Cited by 80 · see citing works
Computational methods for prediction of in vitro effects of new chemical structures
Banerjee, P., Siramshetty, V. B., Drwal, M. N., & Preissner, R. (2016) · Journal of Cheminformatics · doi:10.1186/s13321-016-0162-2 · Cited by 60 · see citing works
Ebola Virus Bayesian Machine Learning Models Enable New in Vitro Leads
Anantpadma, M., Lane, T., Zorn, K. M., Lingerfelt, M. A., Clark, A. M., Freundlich, J. S., Davey, R. A., Madrid, P. B., & Ekins, S. (2019) · ACS Omega · doi:10.1021/acsomega.8b02948 · Cited by 54 · see citing works
Self-citationPredicting Mouse Liver Microsomal Stability with “Pruned” Machine Learning Models and Public Data
Perryman, A. L., Stratton, T. P., Ekins, S., & Freundlich, J. S. (2016) · Pharmaceutical Research · doi:10.1007/s11095-015-1800-5 · Cited by 50 · see citing works
Self-citation
- Authors
- Clark, A. M., Dole, K., Coulon-Spektor, A., McNutt, A., Grass, G., Freundlich, J. S., Reynolds, R. C., & Ekins, S.
- Affiliations
- Molecular Materials Informatics, Inc., 1900 St. Jacques No. 302, Montreal H3J 2S1, Quebec, Canada
- Collaborative Drug Discovery (United States) (US)
- Collaborative Drug Discovery, 1633 Bayshore Highway, Suite 342, Burlingame, California 94010, United States
- G2 Research, Inc., P.O. Box 1242, Tahoe City, California 96145, United States
- Rutgers, The State University of New Jersey (US)
- University of Alabama at Birmingham (US)
- Corresponding author
- Clark, Alex M.
- Ekins, Sean
- Venue
- Journal of Chemical Information and Modeling
- Publisher
- American Chemical Society (ACS)
- Published
- 2015-06-03
- Volume
- 55
- Issue
- 6
- Pages
- 1231-1245
- ISSN
- 1549-9596 (ISSN-L)
- 1549-960X
- PMC
- PMC4478615
- OpenAlex
- W2401677822
- Cited by
- 114
- References
- 148
- Citations by year
- 2026: 1 · 2025: 5 · 2024: 9 · 2023: 7 · 2022: 7 · 2021: 19 · 2020: 18 · 2019: 9 · 2018: 10 · 2017: 10 · 2016: 12 · 2015: 7
- FWCI
- 13.37
- Type
- journal article
- Language
- en
- Open access
- hybrid
- doi.org
- Licence
- Locations
- Journal of Chemical Information and Modeling (publishedVersion, other-oa)
- Europe PMC (PubMed Central) (publishedVersion)
- PubMed Central (publishedVersion)
- PubMed (publishedVersion)
- Funders
- National Institute of Allergy and Infectious Diseases (1RC1AI086677-01)
- National Center for Advancing Translational Sciences (9R44TR000942-02)
- Topics
- Physical Sciences › Computer Science › Computational Theory and Mathematics › Computational Drug Discovery Methods
- Physical Sciences › Materials Science › Materials Chemistry › Machine Learning in Materials Science
- Physical Sciences › Chemistry › Physical and Theoretical Chemistry › History and advancements in chemistry
- Keywords
- Bayesian model, ADME-Tox, drug discovery, open source software, Chemistry Development Kit, bioactivity prediction, physicochemical properties, model sharing, biocomputation, mobile application
- MeSH terms
- Animals, Bayes Theorem, Computer Simulation, Pharmaceutical Preparations, Humans, Software, Mice, Drug Discovery, Databases, Pharmaceutical, Drug-Related Side Effects and Adverse Reactions, Absorption, Physicochemical
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Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
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