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Lane TR, Urbina F, Rank L, Gerlach J, Riabova O, Lepioshkin A, Kazakova E, Vocat A, Tkachenko V, Cole S, Makarov V, Ekins S. Machine Learning Models for Mycobacterium tuberculosisIn Vitro Activity: Prediction and Target Visualization. Mol Pharm 2022; 19:674-689. [PMID: 34964633 PMCID: PMC9121329 DOI: 10.1021/acs.molpharmaceut.1c00791] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/28/2022]
Abstract
Tuberculosis (TB) is a major global health challenge, with approximately 1.4 million deaths per year. There is still a need to develop novel treatments for patients infected with Mycobacterium tuberculosis (Mtb). There have been many large-scale phenotypic screens that have led to the identification of thousands of new compounds. Yet, there is very limited investment in TB drug discovery which points to the need for new methods to increase the efficiency of drug discovery against Mtb. We have used machine learning approaches to learn from the public Mtb data, resulting in many data sets and models with robust enrichment and hit rates leading to the discovery of new active compounds. Recently, we have curated predominantly small-molecule Mtb data and developed new machine learning classification models with 18 886 molecules at different activity cutoffs. We now describe the further validation of these Bayesian models using a library of over 1000 molecules synthesized as part of EU-funded New Medicines for TB and More Medicines for TB programs. We highlight molecular features which are enriched in these active compounds. In addition, we provide new regression and classification models that can be used for scoring compound libraries or used to design new molecules. We have also visualized these molecules in the context of known molecular targets and identified clusters in chemical property space, which may aid in future target identification efforts. Finally, we are also making these data sets publicly available, representing a significant increase to the available Mtb inhibition data in the public domain.
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Affiliation(s)
- Thomas R. Lane
- Collaborations Pharmaceuticals, Inc., 840 Main Campus Drive, Lab 3510 Raleigh, NC 27606, USA
| | - Fabio Urbina
- Collaborations Pharmaceuticals, Inc., 840 Main Campus Drive, Lab 3510 Raleigh, NC 27606, USA
| | - Laura Rank
- Collaborations Pharmaceuticals, Inc., 840 Main Campus Drive, Lab 3510 Raleigh, NC 27606, USA
| | - Jacob Gerlach
- Collaborations Pharmaceuticals, Inc., 840 Main Campus Drive, Lab 3510 Raleigh, NC 27606, USA
| | - Olga Riabova
- Research Center of Biotechnology RAS, 119071 Moscow, Russia
| | | | - Elena Kazakova
- Research Center of Biotechnology RAS, 119071 Moscow, Russia
| | - Anthony Vocat
- Global Health Institute, Ecole Polytechnique Fédérale de Lausanne, Lausanne 1015, Switzerland
| | - Valery Tkachenko
- Science Data Experts, 14909 Forest Landing Cir, Rockville, MD 20850
| | | | - Vadim Makarov
- Research Center of Biotechnology RAS, 119071 Moscow, Russia
| | - Sean Ekins
- Collaborations Pharmaceuticals, Inc., 840 Main Campus Drive, Lab 3510 Raleigh, NC 27606, USA
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Patel JS, Norambuena J, Al-Tameemi H, Ahn YM, Perryman AL, Wang X, Daher SS, Occi J, Russo R, Park S, Zimmerman M, Ho HP, Perlin DS, Dartois V, Ekins S, Kumar P, Connell N, Boyd JM, Freundlich JS. Bayesian Modeling and Intrabacterial Drug Metabolism Applied to Drug-Resistant Staphylococcus aureus. ACS Infect Dis 2021; 7:2508-2521. [PMID: 34342426 DOI: 10.1021/acsinfecdis.1c00265] [Citation(s) in RCA: 6] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/14/2022]
Abstract
We present the application of Bayesian modeling to identify chemical tools and/or drug discovery entities pertinent to drug-resistant Staphylococcus aureus infections. The quinoline JSF-3151 was predicted by modeling and then empirically demonstrated to be active against in vitro cultured clinical methicillin- and vancomycin-resistant strains while also exhibiting efficacy in a mouse peritonitis model of methicillin-resistant S. aureus infection. We highlight the utility of an intrabacterial drug metabolism (IBDM) approach to probe the mechanism by which JSF-3151 is transformed within the bacteria. We also identify and then validate two mechanisms of resistance in S. aureus: one mechanism involves increased expression of a lipocalin protein, and the other arises from the loss of function of an azoreductase. The computational and experimental approaches, discovery of an antibacterial agent, and elucidated resistance mechanisms collectively hold promise to advance our understanding of therapeutic regimens for drug-resistant S. aureus.
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Affiliation(s)
- Jimmy S. Patel
- Department of Pharmacology, Physiology, and Neuroscience, Rutgers University − New Jersey Medical School, 185 South Orange Ave, Newark, New Jersey 07103, United States
| | - Javiera Norambuena
- Department of Biochemistry and Microbiology, Rutgers, The State University of New Jersey, New Brunswick, New Jersey 08901, United States
| | - Hassan Al-Tameemi
- Department of Biochemistry and Microbiology, Rutgers, The State University of New Jersey, New Brunswick, New Jersey 08901, United States
| | - Yong-Mo Ahn
- Department of Pharmacology, Physiology, and Neuroscience, Rutgers University − New Jersey Medical School, 185 South Orange Ave, Newark, New Jersey 07103, United States
| | - Alexander L. Perryman
- Department of Pharmacology, Physiology, and Neuroscience, Rutgers University − New Jersey Medical School, 185 South Orange Ave, Newark, New Jersey 07103, United States
| | - Xin Wang
- Department of Pharmacology, Physiology, and Neuroscience, Rutgers University − New Jersey Medical School, 185 South Orange Ave, Newark, New Jersey 07103, United States
| | - Samer S. Daher
- Department of Pharmacology, Physiology, and Neuroscience, Rutgers University − New Jersey Medical School, 185 South Orange Ave, Newark, New Jersey 07103, United States
| | - James Occi
- Department of Medicine, Center for Emerging and Re-emerging Pathogens, Rutgers University − New Jersey Medical School, Newark, New Jersey 07103, United States
| | - Riccardo Russo
- Department of Medicine, Center for Emerging and Re-emerging Pathogens, Rutgers University − New Jersey Medical School, Newark, New Jersey 07103, United States
| | - Steven Park
- Public Health Research Institute, Rutgers University − New Jersey Medical School, 225 Warren St, Newark, New Jersey 07103, United States
| | - Matthew Zimmerman
- Public Health Research Institute, Rutgers University − New Jersey Medical School, 225 Warren St, Newark, New Jersey 07103, United States
| | - Hsin-Pin Ho
- Public Health Research Institute, Rutgers University − New Jersey Medical School, 225 Warren St, Newark, New Jersey 07103, United States
| | - David S. Perlin
- Public Health Research Institute, Rutgers University − New Jersey Medical School, 225 Warren St, Newark, New Jersey 07103, United States
| | - Véronique Dartois
- Public Health Research Institute, Rutgers University − New Jersey Medical School, 225 Warren St, Newark, New Jersey 07103, United States
| | - Sean Ekins
- Collaborations in Chemistry, 5616 Hilltop Needmore Road, Fuquay-Varina, North Carolina 27526, United States
| | - Pradeep Kumar
- Department of Medicine, Center for Emerging and Re-emerging Pathogens, Rutgers University − New Jersey Medical School, Newark, New Jersey 07103, United States
| | - Nancy Connell
- Department of Medicine, Center for Emerging and Re-emerging Pathogens, Rutgers University − New Jersey Medical School, Newark, New Jersey 07103, United States
| | - Jeffrey M. Boyd
- Department of Biochemistry and Microbiology, Rutgers, The State University of New Jersey, New Brunswick, New Jersey 08901, United States
| | - Joel S. Freundlich
- Department of Pharmacology, Physiology, and Neuroscience, Rutgers University − New Jersey Medical School, 185 South Orange Ave, Newark, New Jersey 07103, United States
- Department of Medicine, Center for Emerging and Re-emerging Pathogens, Rutgers University − New Jersey Medical School, Newark, New Jersey 07103, United States
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Ekins S, Gerlach J, Zorn KM, Antonio BM, Lin Z, Gerlach A. Repurposing Approved Drugs as Inhibitors of K v7.1 and Na v1.8 to Treat Pitt Hopkins Syndrome. Pharm Res 2019; 36:137. [PMID: 31332533 DOI: 10.1007/s11095-019-2671-y] [Citation(s) in RCA: 26] [Impact Index Per Article: 5.2] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/16/2018] [Accepted: 07/10/2019] [Indexed: 12/18/2022]
Abstract
PURPOSE Pitt Hopkins Syndrome (PTHS) is a rare genetic disorder caused by mutations of a specific gene, transcription factor 4 (TCF4), located on chromosome 18. PTHS results in individuals that have moderate to severe intellectual disability, with most exhibiting psychomotor delay. PTHS also exhibits features of autistic spectrum disorders, which are characterized by the impaired ability to communicate and socialize. PTHS is comorbid with a higher prevalence of epileptic seizures which can be present from birth or which commonly develop in childhood. Attenuated or absent TCF4 expression results in increased translation of peripheral ion channels Kv7.1 and Nav1.8 which triggers an increase in after-hyperpolarization and altered firing properties. METHODS We now describe a high throughput screen (HTS) of 1280 approved drugs and machine learning models developed from this data. The ion channels were expressed in either CHO (KV7.1) or HEK293 (Nav1.8) cells and the HTS used either 86Rb+ efflux (KV7.1) or a FLIPR assay (Nav1.8). RESULTS The HTS delivered 55 inhibitors of Kv7.1 (4.2% hit rate) and 93 inhibitors of Nav1.8 (7.2% hit rate) at a screening concentration of 10 μM. These datasets also enabled us to generate and validate Bayesian machine learning models for these ion channels. We also describe a structure activity relationship for several dihydropyridine compounds as inhibitors of Nav1.8. CONCLUSIONS This work could lead to the potential repurposing of nicardipine or other dihydropyridine calcium channel antagonists as potential treatments for PTHS acting via Nav1.8, as there are currently no approved treatments for this rare disorder.
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Affiliation(s)
- Sean Ekins
- Collaborations Pharmaceuticals, Inc., 840 Main Campus Drive, Lab 3510, Raleigh, North Carolina, 27606, USA.
| | - Jacob Gerlach
- Collaborations Pharmaceuticals, Inc., 840 Main Campus Drive, Lab 3510, Raleigh, North Carolina, 27606, USA
| | - Kimberley M Zorn
- Collaborations Pharmaceuticals, Inc., 840 Main Campus Drive, Lab 3510, Raleigh, North Carolina, 27606, USA
| | - Brett M Antonio
- Icagen, Inc., 4222 Emperor Blvd, Durham, North Carolina, 27703, USA
| | - Zhixin Lin
- Icagen, Inc., 4222 Emperor Blvd, Durham, North Carolina, 27703, USA
| | - Aaron Gerlach
- Icagen, Inc., 4222 Emperor Blvd, Durham, North Carolina, 27703, USA
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Lane T, Russo DP, Zorn KM, Clark AM, Korotcov A, Tkachenko V, Reynolds RC, Perryman AL, Freundlich JS, Ekins AS. Comparing and Validating Machine Learning Models for Mycobacterium tuberculosis Drug Discovery. Mol Pharm 2018; 15:4346-4360. [PMID: 29672063 PMCID: PMC6167198 DOI: 10.1021/acs.molpharmaceut.8b00083] [Citation(s) in RCA: 64] [Impact Index Per Article: 10.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/31/2022]
Abstract
Tuberculosis is a global health dilemma. In 2016, the WHO reported 10.4 million incidences and 1.7 million deaths. The need to develop new treatments for those infected with Mycobacterium tuberculosis ( Mtb) has led to many large-scale phenotypic screens and many thousands of new active compounds identified in vitro. However, with limited funding, efforts to discover new active molecules against Mtb needs to be more efficient. Several computational machine learning approaches have been shown to have good enrichment and hit rates. We have curated small molecule Mtb data and developed new models with a total of 18,886 molecules with activity cutoffs of 10 μM, 1 μM, and 100 nM. These data sets were used to evaluate different machine learning methods (including deep learning) and metrics and to generate predictions for additional molecules published in 2017. One Mtb model, a combined in vitro and in vivo data Bayesian model at a 100 nM activity yielded the following metrics for 5-fold cross validation: accuracy = 0.88, precision = 0.22, recall = 0.91, specificity = 0.88, kappa = 0.31, and MCC = 0.41. We have also curated an evaluation set ( n = 153 compounds) published in 2017, and when used to test our model, it showed the comparable statistics (accuracy = 0.83, precision = 0.27, recall = 1.00, specificity = 0.81, kappa = 0.36, and MCC = 0.47). We have also compared these models with additional machine learning algorithms showing Bayesian machine learning models constructed with literature Mtb data generated by different laboratories generally were equivalent to or outperformed deep neural networks with external test sets. Finally, we have also compared our training and test sets to show they were suitably diverse and different in order to represent useful evaluation sets. Such Mtb machine learning models could help prioritize compounds for testing in vitro and in vivo.
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Affiliation(s)
- Thomas Lane
- Collaborations Pharmaceuticals, Inc., Main Campus Drive, Lab 3510 Raleigh, NC 27606, USA
- Department of Biochemistry and Biophysics, University of North Carolina, Chapel Hill, NC 27599, USA
| | - Daniel P. Russo
- Collaborations Pharmaceuticals, Inc., Main Campus Drive, Lab 3510 Raleigh, NC 27606, USA
- The Rutgers Center for Computational and Integrative Biology, Camden, NJ, 08102, USA
| | - Kimberley M. Zorn
- Collaborations Pharmaceuticals, Inc., Main Campus Drive, Lab 3510 Raleigh, NC 27606, USA
| | - Alex M. Clark
- Molecular Materials Informatics, Inc., 1900 St. Jacques #302, Montreal H3J 2S1, Quebec, Canada
| | - Alexandru Korotcov
- Science Data Software, LLC, 14914 Bradwill Court, Rockville, MD 20850, USA
| | - Valery Tkachenko
- Science Data Software, LLC, 14914 Bradwill Court, Rockville, MD 20850, USA
| | - Robert C. Reynolds
- Department of Medicine, Division of Hematology and Oncology, University of Alabama at Birmingham, NP 2540 J, 1720 2Avenue South, Birmingham, AL 35294-3300, USA
| | - Alexander L. Perryman
- Department of Pharmacology, Physiology and Neuroscience, Rutgers University-New Jersey Medical School, Newark, New Jersey 07103, USA
| | - Joel S. Freundlich
- Department of Pharmacology, Physiology and Neuroscience, Rutgers University-New Jersey Medical School, Newark, New Jersey 07103, USA
- Division of Infectious Diseases, Department of Medicine, and the Ruy V. Lourenço Center for the Study of Emerging and Re-emerging Pathogens, Rutgers University–New Jersey Medical School, Newark, New Jersey 07103, USA
| | - and Sean Ekins
- Collaborations Pharmaceuticals, Inc., Main Campus Drive, Lab 3510 Raleigh, NC 27606, USA
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Chemical Diversity in the G Protein-Coupled Receptor Superfamily. Trends Pharmacol Sci 2018; 39:494-512. [PMID: 29576399 DOI: 10.1016/j.tips.2018.02.004] [Citation(s) in RCA: 59] [Impact Index Per Article: 9.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/18/2017] [Revised: 02/05/2018] [Accepted: 02/06/2018] [Indexed: 12/20/2022]
Abstract
G protein-coupled receptors (GPCRs) are the largest family of cell signaling transmembrane proteins that can be modulated by a plethora of chemical compounds. Systematic cheminformatics analysis of structurally and pharmacologically characterized GPCR ligands shows that cocrystallized GPCR ligands cover a significant part of chemical ligand space, despite their limited number. Many GPCR ligands and substructures interact with multiple receptors, providing a basis for polypharmacological ligand design. Experimentally determined GPCR structures represent a variety of binding sites and receptor-ligand interactions that can be translated to chemically similar ligands for which structural data are lacking. This integration of structural, pharmacological, and chemical information on GPCR-ligand interactions enables the extension of the structural GPCR-ligand interactome and the structure-based design of novel modulators of GPCR function.
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6
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Ekins S, Clark AM, Dole K, Gregory K, Mcnutt AM, Spektor AC, Weatherall C, Litterman NK, Bunin BA. Data Mining and Computational Modeling of High-Throughput Screening Datasets. Methods Mol Biol 2018; 1755:197-221. [PMID: 29671272 DOI: 10.1007/978-1-4939-7724-6_14] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/23/2022]
Abstract
We are now seeing the benefit of investments made over the last decade in high-throughput screening (HTS) that is resulting in large structure activity datasets entering public and open databases such as ChEMBL and PubChem. The growth of academic HTS screening centers and the increasing move to academia for early stage drug discovery suggests a great need for the informatics tools and methods to mine such data and learn from it. Collaborative Drug Discovery, Inc. (CDD) has developed a number of tools for storing, mining, securely and selectively sharing, as well as learning from such HTS data. We present a new web based data mining and visualization module directly within the CDD Vault platform for high-throughput drug discovery data that makes use of a novel technology stack following modern reactive design principles. We also describe CDD Models within the CDD Vault platform that enables researchers to share models, share predictions from models, and create models from distributed, heterogeneous data. Our system is built on top of the Collaborative Drug Discovery Vault Activity and Registration data repository ecosystem which allows users to manipulate and visualize thousands of molecules in real time. This can be performed in any browser on any platform. In this chapter we present examples of its use with public datasets in CDD Vault. Such approaches can complement other cheminformatics tools, whether open source or commercial, in providing approaches for data mining and modeling of HTS data.
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Affiliation(s)
- Sean Ekins
- Collaborations Pharmaceuticals, Inc., 840 Main Campus Drive, Lab 3510, Raleigh, NC, 27606, USA.
| | - Alex M Clark
- Collaborative Drug Discovery, Inc., Burlingame, CA, USA
- Molecular Materials Informatics, Inc., Montreal, QC, Canada
| | - Krishna Dole
- Collaborative Drug Discovery, Inc., Burlingame, CA, USA
| | | | | | | | | | | | - Barry A Bunin
- Collaborative Drug Discovery, Inc., Burlingame, CA, USA
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7
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Korotcov A, Tkachenko V, Russo DP, Ekins S. Comparison of Deep Learning With Multiple Machine Learning Methods and Metrics Using Diverse Drug Discovery Data Sets. Mol Pharm 2017; 14:4462-4475. [PMID: 29096442 PMCID: PMC5741413 DOI: 10.1021/acs.molpharmaceut.7b00578] [Citation(s) in RCA: 180] [Impact Index Per Article: 25.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/30/2022]
Abstract
Machine learning methods have been applied to many data sets in pharmaceutical research for several decades. The relative ease and availability of fingerprint type molecular descriptors paired with Bayesian methods resulted in the widespread use of this approach for a diverse array of end points relevant to drug discovery. Deep learning is the latest machine learning algorithm attracting attention for many of pharmaceutical applications from docking to virtual screening. Deep learning is based on an artificial neural network with multiple hidden layers and has found considerable traction for many artificial intelligence applications. We have previously suggested the need for a comparison of different machine learning methods with deep learning across an array of varying data sets that is applicable to pharmaceutical research. End points relevant to pharmaceutical research include absorption, distribution, metabolism, excretion, and toxicity (ADME/Tox) properties, as well as activity against pathogens and drug discovery data sets. In this study, we have used data sets for solubility, probe-likeness, hERG, KCNQ1, bubonic plague, Chagas, tuberculosis, and malaria to compare different machine learning methods using FCFP6 fingerprints. These data sets represent whole cell screens, individual proteins, physicochemical properties as well as a data set with a complex end point. Our aim was to assess whether deep learning offered any improvement in testing when assessed using an array of metrics including AUC, F1 score, Cohen's kappa, Matthews correlation coefficient and others. Based on ranked normalized scores for the metrics or data sets Deep Neural Networks (DNN) ranked higher than SVM, which in turn was ranked higher than all the other machine learning methods. Visualizing these properties for training and test sets using radar type plots indicates when models are inferior or perhaps over trained. These results also suggest the need for assessing deep learning further using multiple metrics with much larger scale comparisons, prospective testing as well as assessment of different fingerprints and DNN architectures beyond those used.
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Affiliation(s)
- Alexandru Korotcov
- Science Data Software, LLC, 14914 Bradwill Court, Rockville, MD 20850, USA
| | - Valery Tkachenko
- Science Data Software, LLC, 14914 Bradwill Court, Rockville, MD 20850, USA
| | - Daniel P Russo
- Collaborations Pharmaceuticals, Inc., 840 Main Campus Drive, Lab 3510, Raleigh, NC 27606, USA
- The Rutgers Center for Computational and Integrative Biology, Camden, NJ, 08102, USA
| | - Sean Ekins
- Collaborations Pharmaceuticals, Inc., 840 Main Campus Drive, Lab 3510, Raleigh, NC 27606, USA
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Willighagen EL, Mayfield JW, Alvarsson J, Berg A, Carlsson L, Jeliazkova N, Kuhn S, Pluskal T, Rojas-Chertó M, Spjuth O, Torrance G, Evelo CT, Guha R, Steinbeck C. The Chemistry Development Kit (CDK) v2.0: atom typing, depiction, molecular formulas, and substructure searching. J Cheminform 2017; 9:33. [PMID: 29086040 PMCID: PMC5461230 DOI: 10.1186/s13321-017-0220-4] [Citation(s) in RCA: 210] [Impact Index Per Article: 30.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/25/2016] [Accepted: 05/16/2017] [Indexed: 12/15/2022] Open
Abstract
Background The Chemistry Development Kit (CDK) is a widely used open source cheminformatics toolkit, providing data structures to represent chemical concepts along with methods to manipulate such structures and perform computations on them. The library implements a wide variety of cheminformatics algorithms ranging from chemical structure canonicalization to molecular descriptor calculations and pharmacophore perception. It is used in drug discovery, metabolomics, and toxicology. Over the last 10 years, the code base has grown significantly, however, resulting in many complex interdependencies among components and poor performance of many algorithms. Results We report improvements to the CDK v2.0 since the v1.2 release series, specifically addressing the increased functional complexity and poor performance. We first summarize the addition of new functionality, such atom typing and molecular formula handling, and improvement to existing functionality that has led to significantly better performance for substructure searching, molecular fingerprints, and rendering of molecules. Second, we outline how the CDK has evolved with respect to quality control and the approaches we have adopted to ensure stability, including a code review mechanism. Conclusions This paper highlights our continued efforts to provide a community driven, open source cheminformatics library, and shows that such collaborative projects can thrive over extended periods of time, resulting in a high-quality and performant library. By taking advantage of community support and contributions, we show that an open source cheminformatics project can act as a peer reviewed publishing platform for scientific computing software.CDK 2.0 provides new features and improved performance ![]() Electronic supplementary material The online version of this article (doi:10.1186/s13321-017-0220-4) contains supplementary material, which is available to authorized users.
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Affiliation(s)
- Egon L Willighagen
- Department of Bioinformatics - BiGCaT, NUTRIM, Maastricht University, 6200 MD, Maastricht, The Netherlands.
| | | | - Jonathan Alvarsson
- Department of Pharmaceutical Biosciences, Uppsala University, 751 24, Uppsala, Sweden
| | - Arvid Berg
- Department of Pharmaceutical Biosciences, Uppsala University, 751 24, Uppsala, Sweden
| | - Lars Carlsson
- AstraZeneca, Innovative Medicines & Early Development, Quantitative Biology, Möndal, Sweden
| | | | - Stefan Kuhn
- Department of Informatics, University of Leicester, Leicester, UK
| | - Tomáš Pluskal
- Whitehead Institute for Biomedical Research, 455 Main Street, Cambridge, MA, 02142, USA
| | | | - Ola Spjuth
- Department of Pharmaceutical Biosciences, Uppsala University, 751 24, Uppsala, Sweden
| | | | - Chris T Evelo
- Department of Bioinformatics - BiGCaT, NUTRIM, Maastricht University, 6200 MD, Maastricht, The Netherlands
| | - Rajarshi Guha
- National Center for Advancing Translational Sciences, 9800 Medical Center Drive, Rockville, MD, 20850, USA
| | - Christoph Steinbeck
- Institute for Inorganic and Analytical Chemistry, Friedrich-Schiller-University, Lessingstr. 8, 07743, Jena, Germany
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9
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Awale M, Reymond JL. The polypharmacology browser: a web-based multi-fingerprint target prediction tool using ChEMBL bioactivity data. J Cheminform 2017; 9:11. [PMID: 28270862 PMCID: PMC5319934 DOI: 10.1186/s13321-017-0199-x] [Citation(s) in RCA: 64] [Impact Index Per Article: 9.1] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/29/2016] [Accepted: 02/10/2017] [Indexed: 12/31/2022] Open
Abstract
Background Several web-based tools have been reported recently which predict the possible targets of a small molecule by similarity to compounds of known bioactivity using molecular fingerprints (fps), however predictions in each case rely on similarities computed from only one or two fps. Considering that structural similarity and therefore the predicted targets strongly depend on the method used for comparison, it would be highly desirable to predict targets using a broader set of fps simultaneously. Results Herein, we present the polypharmacology browser (PPB), a web-based platform which predicts possible targets for small molecules by searching for nearest neighbors using ten different fps describing composition, substructures, molecular shape and pharmacophores. PPB searches through 4613 groups of at least 10 same target annotated bioactive molecules from ChEMBL and returns a list of predicted targets ranked by consensus voting scheme and p value. A validation study across 670 drugs with up to 20 targets showed that combining the predictions from all 10 fps gives the best results, with on average 50% of the known targets of a drug being correctly predicted with a hit rate of 25%. Furthermore, when profiling a new inhibitor of the calcium channel TRPV6 against 24 targets taken from a safety screen panel, we observed inhibition in 5 out of 5 targets predicted by PPB and in 7 out of 18 targets not predicted by PPB. The rate of correct (5/12) and incorrect (0/12) predictions for this compound by PPB was comparable to that of other web-based prediction tools. Conclusion PPB offers a versatile platform for target prediction based on multi-fingerprint comparisons, and is freely accessible at www.gdb.unibe.ch as a valuable support for drug discovery.. ![]() Electronic supplementary material The online version of this article (doi:10.1186/s13321-017-0199-x) contains supplementary material, which is available to authorized users.
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Affiliation(s)
- Mahendra Awale
- Department of Chemistry and Biochemistry, National Center of Competence in Research NCCR Chemical Biology and NCCR TransCure, University of Berne, Freiestrasse 3, 3012 Berne, Switzerland
| | - Jean-Louis Reymond
- Department of Chemistry and Biochemistry, National Center of Competence in Research NCCR Chemical Biology and NCCR TransCure, University of Berne, Freiestrasse 3, 3012 Berne, Switzerland
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Ekins S, Godbole AA, Kéri G, Orfi L, Pato J, Bhat RS, Verma R, Bradley EK, Nagaraja V. Machine learning and docking models for Mycobacterium tuberculosis topoisomerase I. Tuberculosis (Edinb) 2017; 103:52-60. [PMID: 28237034 DOI: 10.1016/j.tube.2017.01.005] [Citation(s) in RCA: 19] [Impact Index Per Article: 2.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/29/2016] [Revised: 01/14/2017] [Accepted: 01/18/2017] [Indexed: 11/30/2022]
Abstract
There is a shortage of compounds that are directed towards new targets apart from those targeted by the FDA approved drugs used against Mycobacterium tuberculosis. Topoisomerase I (Mttopo I) is an essential mycobacterial enzyme and a promising target in this regard. However, it suffers from a shortage of known inhibitors. We have previously used computational approaches such as homology modeling and docking to propose 38 FDA approved drugs for testing and identified several active molecules. To follow on from this, we now describe the in vitro testing of a library of 639 compounds. These data were used to create machine learning models for Mttopo I which were further validated. The combined Mttopo I Bayesian model had a 5 fold cross validation receiver operator characteristic of 0.74 and sensitivity, specificity and concordance values above 0.76 and was used to select commercially available compounds for testing in vitro. The recently described crystal structure of Mttopo I was also compared with the previously described homology model and then used to dock the Mttopo I actives norclomipramine and imipramine. In summary, we describe our efforts to identify small molecule inhibitors of Mttopo I using a combination of machine learning modeling and docking studies in conjunction with screening of the selected molecules for enzyme inhibition. We demonstrate the experimental inhibition of Mttopo I by small molecule inhibitors and show that the enzyme can be readily targeted for lead molecule development.
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Affiliation(s)
- Sean Ekins
- Collaborative Drug Discovery, 1633 Bayshore Highway, Suite 342, Burlingame, CA 94403, USA; Collaborations in Chemistry, 5616 Hilltop Needmore Road, Fuquay-Varina, NC 27526, USA.
| | - Adwait Anand Godbole
- Department of Microbiology and Cell Biology, Indian Institute of Science, Bangalore, 560012, India
| | - György Kéri
- Vichem Chemie Research Ltd., Herman Ottó u. 15, H-1022, Budapest, Hungary; Semmelweis Univ, Dept Med Chem, MTA SE Pathobiochem Res Grp, H-1092, Budapest, Hungary
| | - Lászlo Orfi
- Vichem Chemie Research Ltd., Herman Ottó u. 15, H-1022, Budapest, Hungary; Semmelweis Univ, Dept Med Chem, MTA SE Pathobiochem Res Grp, H-1092, Budapest, Hungary
| | - János Pato
- Vichem Chemie Research Ltd., Herman Ottó u. 15, H-1022, Budapest, Hungary
| | - Rajeshwari Subray Bhat
- Department of Microbiology and Cell Biology, Indian Institute of Science, Bangalore, 560012, India
| | - Rinkee Verma
- Department of Microbiology and Cell Biology, Indian Institute of Science, Bangalore, 560012, India
| | | | - Valakunja Nagaraja
- Department of Microbiology and Cell Biology, Indian Institute of Science, Bangalore, 560012, India; Jawaharlal Nehru Centre for Advanced Scientific Research, Bangalore, 560064, India.
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11
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Collaborative drug discovery for More Medicines for Tuberculosis (MM4TB). Drug Discov Today 2016; 22:555-565. [PMID: 27884746 DOI: 10.1016/j.drudis.2016.10.009] [Citation(s) in RCA: 10] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/13/2016] [Revised: 10/11/2016] [Accepted: 10/21/2016] [Indexed: 01/30/2023]
Abstract
Neglected disease drug discovery is generally poorly funded compared with major diseases and hence there is an increasing focus on collaboration and precompetitive efforts such as public-private partnerships (PPPs). The More Medicines for Tuberculosis (MM4TB) project is one such collaboration funded by the EU with the goal of discovering new drugs for tuberculosis. Collaborative Drug Discovery has provided a commercial web-based platform called CDD Vault which is a hosted collaborative solution for securely sharing diverse chemistry and biology data. Using CDD Vault alongside other commercial and free cheminformatics tools has enabled support of this and other large collaborative projects, aiding drug discovery efforts and fostering collaboration. We will describe CDD's efforts in assisting with the MM4TB project.
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12
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Mikušová K, Ekins S. Learning from the past for TB drug discovery in the future. Drug Discov Today 2016; 22:534-545. [PMID: 27717850 DOI: 10.1016/j.drudis.2016.09.025] [Citation(s) in RCA: 18] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/26/2016] [Revised: 09/25/2016] [Accepted: 09/28/2016] [Indexed: 12/14/2022]
Abstract
Tuberculosis drug discovery has shifted in recent years from a primarily target-based approach to one that uses phenotypic high-throughput screens. As examples of this, through our EU-funded FP7 collaborations, New Medicines for Tuberculosis was target-based and our more-recent More Medicines for Tuberculosis project predominantly used phenotypic screening. From these projects we have examples of success (DprE1) and failure (PimA) going from drug to target and from target to drug, respectively. It is clear that we still have much to learn about the drug targets and the complex effects of the drugs on Mycobacterium tuberculosis. We propose a more integrated approach that learns from earlier drug discovery efforts that could help to move drug discovery forward.
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Affiliation(s)
- Katarína Mikušová
- Department of Biochemistry, Faculty of Natural Sciences, Comenius University in Bratislava, Mlynská dolina, Ilkovičova 6, 84215 Bratislava, Slovakia
| | - Sean Ekins
- Collaborative Drug Discovery, Inc., 1633 Bayshore Highway, Suite 342, Burlingame, CA 94010, USA; Collaborations in Chemistry, 5616 Hilltop Needmore Road, Fuquay Varina, NC 27526, USA.
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13
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Ekins S. The Next Era: Deep Learning in Pharmaceutical Research. Pharm Res 2016; 33:2594-603. [PMID: 27599991 DOI: 10.1007/s11095-016-2029-7] [Citation(s) in RCA: 99] [Impact Index Per Article: 12.4] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/10/2016] [Accepted: 08/23/2016] [Indexed: 01/22/2023]
Abstract
Over the past decade we have witnessed the increasing sophistication of machine learning algorithms applied in daily use from internet searches, voice recognition, social network software to machine vision software in cameras, phones, robots and self-driving cars. Pharmaceutical research has also seen its fair share of machine learning developments. For example, applying such methods to mine the growing datasets that are created in drug discovery not only enables us to learn from the past but to predict a molecule's properties and behavior in future. The latest machine learning algorithm garnering significant attention is deep learning, which is an artificial neural network with multiple hidden layers. Publications over the last 3 years suggest that this algorithm may have advantages over previous machine learning methods and offer a slight but discernable edge in predictive performance. The time has come for a balanced review of this technique but also to apply machine learning methods such as deep learning across a wider array of endpoints relevant to pharmaceutical research for which the datasets are growing such as physicochemical property prediction, formulation prediction, absorption, distribution, metabolism, excretion and toxicity (ADME/Tox), target prediction and skin permeation, etc. We also show that there are many potential applications of deep learning beyond cheminformatics. It will be important to perform prospective testing (which has been carried out rarely to date) in order to convince skeptics that there will be benefits from investing in this technique.
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Affiliation(s)
- Sean Ekins
- Collaborations Pharmaceuticals, Inc, 5616 Hilltop Needmore Road, Fuquay-Varina, North Carolina, 27526, USA. .,Collaborative Drug Discovery, 1633 Bayshore Highway, Suite 342, Burlingame, California, 94010, USA.
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14
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Ekins S, Perryman AL, Clark AM, Reynolds RC, Freundlich JS. Machine Learning Model Analysis and Data Visualization with Small Molecules Tested in a Mouse Model of Mycobacterium tuberculosis Infection (2014-2015). J Chem Inf Model 2016; 56:1332-43. [PMID: 27335215 PMCID: PMC4962118 DOI: 10.1021/acs.jcim.6b00004] [Citation(s) in RCA: 20] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/01/2023]
Abstract
![]()
The
renewed urgency to develop new treatments for Mycobacterium
tuberculosis (Mtb)
infection has resulted in large-scale phenotypic screening and thousands
of new active compounds in vitro. The next challenge
is to identify candidates to pursue in a mouse in vivo efficacy model as a step to predicting clinical efficacy. We previously
analyzed over 70 years of this mouse in vivo efficacy
data, which we used to generate and validate machine learning models.
Curation of 60 additional small molecules with in vivo data published in 2014 and 2015 was undertaken to further test these
models. This represents a much larger test set than for the previous
models. Several computational approaches have now been applied to
analyze these molecules and compare their molecular properties beyond
those attempted previously. Our previous machine learning models have
been updated, and a novel aspect has been added in the form of mouse
liver microsomal half-life (MLM t1/2)
and in vitro-based Mtb models incorporating
cytotoxicity data that were used to predict in vivo activity for comparison. Our best Mtbin
vivo models possess fivefold ROC values > 0.7, sensitivity
> 80%, and concordance > 60%, while the best specificity value
is
>40%. Use of an MLM t1/2 Bayesian model
affords comparable results for scoring the 60 compounds tested. Combining
MLM stability and in vitroMtb models
in a novel consensus workflow in the best cases has a positive predicted
value (hit rate) > 77%. Our results indicate that Bayesian models
constructed with literature in vivoMtb data generated by different laboratories in various mouse models
can have predictive value and may be used alongside MLM t1/2 and in vitro-based Mtb models to assist in selecting antitubercular compounds with desirable in vivo efficacy. We demonstrate for the first time that
consensus models of any kind can be used to predict in vivo activity for Mtb. In addition, we describe a new
clustering method for data visualization and apply this to the in vivo training and test data, ultimately making the method
accessible in a mobile app.
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Affiliation(s)
- Sean Ekins
- Collaborative Drug Discovery , 1633 Bayshore Highway, Suite 342, Burlingame, California 94010, United States.,Collaborations in Chemistry , 5616 Hilltop Needmore Road, Fuquay-Varina, North Carolina 27526, United States
| | - Alexander L Perryman
- Department of Pharmacology, Physiology and Neuroscience, Rutgers University-New Jersey Medical School , Newark, New Jersey 07103, United States
| | - Alex M Clark
- Molecular Materials Informatics, Inc. , 1900 St. Jacques #302, Montreal, Quebec H3J 2S1, Canada
| | - Robert C Reynolds
- Division of Hematology and Oncology, Department of Medicine, and Department of Chemistry, College of Arts and Sciences, University of Alabama at Birmingham , 1530 Third Avenue South, Birmingham, Alabama 35294-1240, United States
| | - Joel S Freundlich
- Department of Pharmacology, Physiology and Neuroscience, Rutgers University-New Jersey Medical School , Newark, New Jersey 07103, United States.,Division of Infectious Diseases, Department of Medicine, and the Ruy V. Lourenço Center for the Study of Emerging and Re-emerging Pathogens, Rutgers University-New Jersey Medical School , Newark, New Jersey 07103, United States
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15
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Iribarren SJ, Schnall R, Stone PW, Carballo-Diéguez A. Smartphone Applications to Support Tuberculosis Prevention and Treatment: Review and Evaluation. JMIR Mhealth Uhealth 2016; 4:e25. [PMID: 27177591 PMCID: PMC4884267 DOI: 10.2196/mhealth.5022] [Citation(s) in RCA: 33] [Impact Index Per Article: 4.1] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/11/2015] [Revised: 09/23/2015] [Accepted: 10/31/2015] [Indexed: 12/13/2022] Open
Abstract
BACKGROUND Tuberculosis (TB) remains a major global health problem and is the leading killer due to a single infectious disease. Mobile health (mHealth)-based tools such as smartphone apps have been suggested as tools to support TB control efforts (eg, identification, contact tracing, case management including patient support). OBJECTIVE The purpose of this review was to identify and assess the functionalities of mobile apps focused on prevention and treatment of TB. METHODS We searched 3 online mobile app stores. Apps were included if they were focused on TB and were in English, Spanish, or Portuguese. For each included app, 11 functionalities were assessed (eg, inform, instruct, record), and searches were conducted to identify peer-review publications of rigorous testing of the available apps. RESULTS A total of 1332 potentially relevant apps were identified, with 24 meeting our inclusion criteria. All of the apps were free to download, but 7 required login and password and were developed for specific clinics, regional sites, or research studies. Targeted users were mainly clinicians (n=17); few (n=4) apps were patient focused. Most apps (n=17) had 4 or fewer functions out of 11 (range 1-6). The most common functionalities were inform and record (n=15). Although a number of apps were identified with various functionalities to support TB efforts, some had issues such as incorrect spelling and grammar, inconsistent responses to data entry, problems with crashing, or links to features that had no data. Of more concern, some apps provided potentially harmful information to patients, such as links to natural remedies for TB and natural healers. One-third of the apps (8/24) had not been updated for more than a year and may no longer be supported. Peer-reviewed publications were identified for only two of the included apps. In the gray literature (not found in the app stores), three TB-related apps were identified as in progress, being launched, or tested. CONCLUSIONS Apps identified for TB prevention and treatment had minimal functionality, primarily targeted frontline health care workers, and focused on TB information (eg, general information, guidelines, and news) or data collection (eg, replace paper-based notification or tracking). Few apps were developed for use by patients and none were developed to support TB patient involvement and management in their care (eg, follow-up alerts/reminders, side effects monitoring) or improve interaction with their health care providers, limiting the potential of these apps to facilitate patient-centered care. Our evaluation shows that more refined work is needed to be done in the area of apps to support patients with active TB. Involving TB patients in treatment in the design of these apps is recommended.
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Affiliation(s)
- Sarah J Iribarren
- Columbia University, School of Nursing, New York, NY, United States.
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16
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Perryman AL, Stratton TP, Ekins S, Freundlich JS. Predicting Mouse Liver Microsomal Stability with "Pruned" Machine Learning Models and Public Data. Pharm Res 2016; 33:433-49. [PMID: 26415647 PMCID: PMC4712113 DOI: 10.1007/s11095-015-1800-5] [Citation(s) in RCA: 36] [Impact Index Per Article: 4.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/30/2015] [Accepted: 09/22/2015] [Indexed: 02/07/2023]
Abstract
PURPOSE Mouse efficacy studies are a critical hurdle to advance translational research of potential therapeutic compounds for many diseases. Although mouse liver microsomal (MLM) stability studies are not a perfect surrogate for in vivo studies of metabolic clearance, they are the initial model system used to assess metabolic stability. Consequently, we explored the development of machine learning models that can enhance the probability of identifying compounds possessing MLM stability. METHODS Published assays on MLM half-life values were identified in PubChem, reformatted, and curated to create a training set with 894 unique small molecules. These data were used to construct machine learning models assessed with internal cross-validation, external tests with a published set of antitubercular compounds, and independent validation with an additional diverse set of 571 compounds (PubChem data on percent metabolism). RESULTS "Pruning" out the moderately unstable / moderately stable compounds from the training set produced models with superior predictive power. Bayesian models displayed the best predictive power for identifying compounds with a half-life ≥1 h. CONCLUSIONS Our results suggest the pruning strategy may be of general benefit to improve test set enrichment and provide machine learning models with enhanced predictive value for the MLM stability of small organic molecules. This study represents the most exhaustive study to date of using machine learning approaches with MLM data from public sources.
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Affiliation(s)
- Alexander L Perryman
- Division of Infectious Disease, Department of Medicine, and the Ruy V. Lourenço Center for the Study of Emerging and Re-emerging Pathogens, Rutgers University-New Jersey Medical School, Newark, New Jersey, 07103, USA
| | - Thomas P Stratton
- Department of Pharmacology & Physiology, Rutgers University-New Jersey Medical School, Medical Sciences Building, I-503, 185 South Orange Ave., Newark, New Jersey, 07103, USA
| | - Sean Ekins
- Collaborations in Chemistry, 5616 Hilltop Needmore Road, Fuquay-Varina, NC, 27526, USA
- Collaborative Drug Discovery, 1633 Bayshore Highway, Suite 342, Burlingame, CA, 94010, USA
| | - Joel S Freundlich
- Division of Infectious Disease, Department of Medicine, and the Ruy V. Lourenço Center for the Study of Emerging and Re-emerging Pathogens, Rutgers University-New Jersey Medical School, Newark, New Jersey, 07103, USA.
- Department of Pharmacology & Physiology, Rutgers University-New Jersey Medical School, Medical Sciences Building, I-503, 185 South Orange Ave., Newark, New Jersey, 07103, USA.
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17
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Ekins S, Freundlich JS, Clark AM, Anantpadma M, Davey RA, Madrid P. Machine learning models identify molecules active against the Ebola virus in vitro. F1000Res 2016; 4:1091. [PMID: 26834994 DOI: 10.12688/f1000research.7217.2] [Citation(s) in RCA: 13] [Impact Index Per Article: 1.6] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Accepted: 12/23/2015] [Indexed: 12/15/2022] Open
Abstract
The search for small molecule inhibitors of Ebola virus (EBOV) has led to several high throughput screens over the past 3 years. These have identified a range of FDA-approved active pharmaceutical ingredients (APIs) with anti-EBOV activity in vitro and several of which are also active in a mouse infection model. There are millions of additional commercially-available molecules that could be screened for potential activities as anti-EBOV compounds. One way to prioritize compounds for testing is to generate computational models based on the high throughput screening data and then virtually screen compound libraries. In the current study, we have generated Bayesian machine learning models with viral pseudotype entry assay and the EBOV replication assay data. We have validated the models internally and externally. We have also used these models to computationally score the MicroSource library of drugs to select those likely to be potential inhibitors. Three of the highest scoring molecules that were not in the model training sets, quinacrine, pyronaridine and tilorone, were tested in vitro and had EC50 values of 350, 420 and 230 nM, respectively. Pyronaridine is a component of a combination therapy for malaria that was recently approved by the European Medicines Agency, which may make it more readily accessible for clinical testing. Like other known antimalarial drugs active against EBOV, it shares the 4-aminoquinoline scaffold. Tilorone, is an investigational antiviral agent that has shown a broad array of biological activities including cell growth inhibition in cancer cells, antifibrotic properties, α7 nicotinic receptor agonist activity, radioprotective activity and activation of hypoxia inducible factor-1. Quinacrine is an antimalarial but also has use as an anthelmintic. Our results suggest data sets with less than 1,000 molecules can produce validated machine learning models that can in turn be utilized to identify novel EBOV inhibitors in vitro.
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Affiliation(s)
- Sean Ekins
- Collaborations in Chemistry, Fuquay-Varina, NC, 27526, USA
- Collaborations Pharmaceuticals Inc, Fuquay-Varina, NC, 27526, USA
- Collaborative Drug Discovery, Burlingame, CA, 94010, USA
| | - Joel S Freundlich
- Departments of Pharmacology & Physiology and Medicine, Center for Emerging and Reemerging Pathogens, UMDNJ, New Jersey Medical School, Newark, NJ, 07103, USA
| | - Alex M Clark
- Molecular Materials Informatics, Inc., Montreal, 94025, Canada
| | - Manu Anantpadma
- Texas Biomedical Research Institute, San Antonio, TX, 78227, USA
| | - Robert A Davey
- Texas Biomedical Research Institute, San Antonio, TX, 78227, USA
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18
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Ekins S, Madrid PB, Sarker M, Li SG, Mittal N, Kumar P, Wang X, Stratton TP, Zimmerman M, Talcott C, Bourbon P, Travers M, Yadav M, Freundlich JS. Combining Metabolite-Based Pharmacophores with Bayesian Machine Learning Models for Mycobacterium tuberculosis Drug Discovery. PLoS One 2015; 10:e0141076. [PMID: 26517557 PMCID: PMC4627656 DOI: 10.1371/journal.pone.0141076] [Citation(s) in RCA: 9] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/30/2015] [Accepted: 10/05/2015] [Indexed: 12/15/2022] Open
Abstract
Integrated computational approaches for Mycobacterium tuberculosis (Mtb) are useful to identify new molecules that could lead to future tuberculosis (TB) drugs. Our approach uses information derived from the TBCyc pathway and genome database, the Collaborative Drug Discovery TB database combined with 3D pharmacophores and dual event Bayesian models of whole-cell activity and lack of cytotoxicity. We have prioritized a large number of molecules that may act as mimics of substrates and metabolites in the TB metabolome. We computationally searched over 200,000 commercial molecules using 66 pharmacophores based on substrates and metabolites from Mtb and further filtering with Bayesian models. We ultimately tested 110 compounds in vitro that resulted in two compounds of interest, BAS 04912643 and BAS 00623753 (MIC of 2.5 and 5 μg/mL, respectively). These molecules were used as a starting point for hit-to-lead optimization. The most promising class proved to be the quinoxaline di-N-oxides, evidenced by transcriptional profiling to induce mRNA level perturbations most closely resembling known protonophores. One of these, SRI58 exhibited an MIC = 1.25 μg/mL versus Mtb and a CC50 in Vero cells of >40 μg/mL, while featuring fair Caco-2 A-B permeability (2.3 x 10−6 cm/s), kinetic solubility (125 μM at pH 7.4 in PBS) and mouse metabolic stability (63.6% remaining after 1 h incubation with mouse liver microsomes). Despite demonstration of how a combined bioinformatics/cheminformatics approach afforded a small molecule with promising in vitro profiles, we found that SRI58 did not exhibit quantifiable blood levels in mice.
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Affiliation(s)
- Sean Ekins
- Collaborative Drug Discovery Inc., 1633 Bayshore Highway, Suite 342, Burlingame, CA, 94010, United States of America
- Collaborations in Chemistry, 5616 Hilltop Needmore Road, Fuquay-Varina, NC, 27526, United States of America
- * E-mail: (SE); (PBM); (JSF)
| | - Peter B. Madrid
- SRI International, 333 Ravenswood Avenue, Menlo Park, CA, 94025, United States of America
- * E-mail: (SE); (PBM); (JSF)
| | - Malabika Sarker
- SRI International, 333 Ravenswood Avenue, Menlo Park, CA, 94025, United States of America
| | - Shao-Gang Li
- Departments of Pharmacology & Physiology and Medicine, Center for Emerging and Reemerging Pathogens, Rutgers University–New Jersey Medical School, 185 South Orange Avenue, Newark, NJ, 07103, United States of America
| | - Nisha Mittal
- Departments of Pharmacology & Physiology and Medicine, Center for Emerging and Reemerging Pathogens, Rutgers University–New Jersey Medical School, 185 South Orange Avenue, Newark, NJ, 07103, United States of America
| | - Pradeep Kumar
- Department of Medicine, Center for Emerging and Reemerging Pathogens, Rutgers University–New Jersey Medical School, 185 South Orange Avenue, Newark, NJ, 07103, United States of America
| | - Xin Wang
- Departments of Pharmacology & Physiology and Medicine, Center for Emerging and Reemerging Pathogens, Rutgers University–New Jersey Medical School, 185 South Orange Avenue, Newark, NJ, 07103, United States of America
| | - Thomas P. Stratton
- Departments of Pharmacology & Physiology and Medicine, Center for Emerging and Reemerging Pathogens, Rutgers University–New Jersey Medical School, 185 South Orange Avenue, Newark, NJ, 07103, United States of America
| | - Matthew Zimmerman
- Public Health Research Institute, Rutgers University–New Jersey Medical School, Newark, NJ, 07103, United States of America
| | - Carolyn Talcott
- SRI International, 333 Ravenswood Avenue, Menlo Park, CA, 94025, United States of America
| | - Pauline Bourbon
- SRI International, 333 Ravenswood Avenue, Menlo Park, CA, 94025, United States of America
| | - Mike Travers
- Collaborative Drug Discovery Inc., 1633 Bayshore Highway, Suite 342, Burlingame, CA, 94010, United States of America
| | - Maneesh Yadav
- SRI International, 333 Ravenswood Avenue, Menlo Park, CA, 94025, United States of America
| | - Joel S. Freundlich
- Departments of Pharmacology & Physiology and Medicine, Center for Emerging and Reemerging Pathogens, Rutgers University–New Jersey Medical School, 185 South Orange Avenue, Newark, NJ, 07103, United States of America
- * E-mail: (SE); (PBM); (JSF)
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19
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Ekins S, Freundlich JS, Clark AM, Anantpadma M, Davey RA, Madrid P. Machine learning models identify molecules active against the Ebola virus in vitro. F1000Res 2015; 4:1091. [PMID: 26834994 DOI: 10.12688/f1000research.7217.1] [Citation(s) in RCA: 49] [Impact Index Per Article: 5.4] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Accepted: 10/15/2015] [Indexed: 12/23/2022] Open
Abstract
The search for small molecule inhibitors of Ebola virus (EBOV) has led to several high throughput screens over the past 3 years. These have identified a range of FDA-approved active pharmaceutical ingredients (APIs) with anti-EBOV activity in vitro and several of which are also active in a mouse infection model. There are millions of additional commercially-available molecules that could be screened for potential activities as anti-EBOV compounds. One way to prioritize compounds for testing is to generate computational models based on the high throughput screening data and then virtually screen compound libraries. In the current study, we have generated Bayesian machine learning models with viral pseudotype entry assay and the EBOV replication assay data. We have validated the models internally and externally. We have also used these models to computationally score the MicroSource library of drugs to select those likely to be potential inhibitors. Three of the highest scoring molecules that were not in the model training sets, quinacrine, pyronaridine and tilorone, were tested in vitro and had EC 50 values of 350, 420 and 230 nM, respectively. Pyronaridine is a component of a combination therapy for malaria that was recently approved by the European Medicines Agency, which may make it more readily accessible for clinical testing. Like other known antimalarial drugs active against EBOV, it shares the 4-aminoquinoline scaffold. Tilorone, is an investigational antiviral agent that has shown a broad array of biological activities including cell growth inhibition in cancer cells, antifibrotic properties, α7 nicotinic receptor agonist activity, radioprotective activity and activation of hypoxia inducible factor-1. Quinacrine is an antimalarial but also has use as an anthelmintic. Our results suggest data sets with less than 1,000 molecules can produce validated machine learning models that can in turn be utilized to identify novel EBOV inhibitors in vitro.
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Affiliation(s)
- Sean Ekins
- Collaborations in Chemistry, Fuquay-Varina, NC, 27526, USA.,Collaborations Pharmaceuticals Inc, Fuquay-Varina, NC, 27526, USA.,Collaborative Drug Discovery, Burlingame, CA, 94010, USA
| | - Joel S Freundlich
- Departments of Pharmacology & Physiology and Medicine, Center for Emerging and Reemerging Pathogens, UMDNJ, New Jersey Medical School, Newark, NJ, 07103, USA
| | - Alex M Clark
- Molecular Materials Informatics, Inc., Montreal, 94025, Canada
| | - Manu Anantpadma
- Texas Biomedical Research Institute, San Antonio, TX, 78227, USA
| | - Robert A Davey
- Texas Biomedical Research Institute, San Antonio, TX, 78227, USA
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20
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Ekins S, Freundlich JS, Clark AM, Anantpadma M, Davey RA, Madrid P. Machine learning models identify molecules active against the Ebola virus in vitro. F1000Res 2015; 4:1091. [PMID: 26834994 PMCID: PMC4706063 DOI: 10.12688/f1000research.7217.3] [Citation(s) in RCA: 44] [Impact Index Per Article: 4.9] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Accepted: 01/17/2017] [Indexed: 12/21/2022] Open
Abstract
The search for small molecule inhibitors of Ebola virus (EBOV) has led to several high throughput screens over the past 3 years. These have identified a range of FDA-approved active pharmaceutical ingredients (APIs) with anti-EBOV activity
in vitro and several of which are also active in a mouse infection model. There are millions of additional commercially-available molecules that could be screened for potential activities as anti-EBOV compounds. One way to prioritize compounds for testing is to generate computational models based on the high throughput screening data and then virtually screen compound libraries. In the current study, we have generated Bayesian machine learning models with viral pseudotype entry assay and the EBOV replication assay data. We have validated the models internally and externally. We have also used these models to computationally score the MicroSource library of drugs to select those likely to be potential inhibitors. Three of the highest scoring molecules that were not in the model training sets, quinacrine, pyronaridine and tilorone, were tested
in vitro and had EC
50 values of 350, 420 and 230 nM, respectively. Pyronaridine is a component of a combination therapy for malaria that was recently approved by the European Medicines Agency, which may make it more readily accessible for clinical testing. Like other known antimalarial drugs active against EBOV, it shares the 4-aminoquinoline scaffold. Tilorone, is an investigational antiviral agent that has shown a broad array of biological activities including cell growth inhibition in cancer cells, antifibrotic properties, α7 nicotinic receptor agonist activity, radioprotective activity and activation of hypoxia inducible factor-1. Quinacrine is an antimalarial but also has use as an anthelmintic. Our results suggest data sets with less than 1,000 molecules can produce validated machine learning models that can in turn be utilized to identify novel EBOV inhibitors
in vitro.
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Affiliation(s)
- Sean Ekins
- Collaborations in Chemistry, Fuquay-Varina, NC, 27526, USA.,Collaborations Pharmaceuticals Inc, Fuquay-Varina, NC, 27526, USA.,Collaborative Drug Discovery, Burlingame, CA, 94010, USA
| | - Joel S Freundlich
- Departments of Pharmacology & Physiology and Medicine, Center for Emerging and Reemerging Pathogens, UMDNJ, New Jersey Medical School, Newark, NJ, 07103, USA
| | - Alex M Clark
- Molecular Materials Informatics, Inc., Montreal, 94025, Canada
| | - Manu Anantpadma
- Texas Biomedical Research Institute, San Antonio, TX, 78227, USA
| | - Robert A Davey
- Texas Biomedical Research Institute, San Antonio, TX, 78227, USA
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21
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Ekins S, Clark AM, Wright SH. Making Transporter Models for Drug-Drug Interaction Prediction Mobile. Drug Metab Dispos 2015. [PMID: 26199424 DOI: 10.1124/dmd.115.064956] [Citation(s) in RCA: 12] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/22/2022] Open
Abstract
The past decade has seen increased numbers of studies publishing ligand-based computational models for drug transporters. Although they generally use small experimental data sets, these models can provide insights into structure-activity relationships for the transporter. In addition, such models have helped to identify new compounds as substrates or inhibitors of transporters of interest. We recently proposed that many transporters are promiscuous and may require profiling of new chemical entities against multiple substrates for a specific transporter. Furthermore, it should be noted that virtually all of the published ligand-based transporter models are only accessible to those involved in creating them and, consequently, are rarely shared effectively. One way to surmount this is to make models shareable or more accessible. The development of mobile apps that can access such models is highlighted here. These apps can be used to predict ligand interactions with transporters using Bayesian algorithms. We used recently published transporter data sets (MATE1, MATE2K, OCT2, OCTN2, ASBT, and NTCP) to build preliminary models in a commercial tool and in open software that can deliver the model in a mobile app. In addition, several transporter data sets extracted from the ChEMBL database were used to illustrate how such public data and models can be shared. Predicting drug-drug interactions for various transporters using computational models is potentially within reach of anyone with an iPhone or iPad. Such tools could help prioritize which substrates should be used for in vivo drug-drug interaction testing and enable open sharing of models.
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Affiliation(s)
- Sean Ekins
- Collaborations Pharmaceuticals, Inc., and Collaborations in Chemistry, Fuquay-Varina, North Carolina (S.E.); Collaborative Drug Discovery, Burlingame, California (S.E.); Molecular Materials Informatics, Inc., Montreal, Quebec, Canada (A.M.C.); and Department of Physiology, University of Arizona, Tucson, Arizona (S.H.W.)
| | - Alex M Clark
- Collaborations Pharmaceuticals, Inc., and Collaborations in Chemistry, Fuquay-Varina, North Carolina (S.E.); Collaborative Drug Discovery, Burlingame, California (S.E.); Molecular Materials Informatics, Inc., Montreal, Quebec, Canada (A.M.C.); and Department of Physiology, University of Arizona, Tucson, Arizona (S.H.W.)
| | - Stephen H Wright
- Collaborations Pharmaceuticals, Inc., and Collaborations in Chemistry, Fuquay-Varina, North Carolina (S.E.); Collaborative Drug Discovery, Burlingame, California (S.E.); Molecular Materials Informatics, Inc., Montreal, Quebec, Canada (A.M.C.); and Department of Physiology, University of Arizona, Tucson, Arizona (S.H.W.)
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22
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Ekins S, Lage de Siqueira-Neto J, McCall LI, Sarker M, Yadav M, Ponder EL, Kallel EA, Kellar D, Chen S, Arkin M, Bunin BA, McKerrow JH, Talcott C. Machine Learning Models and Pathway Genome Data Base for Trypanosoma cruzi Drug Discovery. PLoS Negl Trop Dis 2015; 9:e0003878. [PMID: 26114876 PMCID: PMC4482694 DOI: 10.1371/journal.pntd.0003878] [Citation(s) in RCA: 63] [Impact Index Per Article: 7.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/27/2015] [Accepted: 06/05/2015] [Indexed: 12/21/2022] Open
Abstract
Background Chagas disease is a neglected tropical disease (NTD) caused by the eukaryotic parasite Trypanosoma cruzi. The current clinical and preclinical pipeline for T. cruzi is extremely sparse and lacks drug target diversity. Methodology/Principal Findings In the present study we developed a computational approach that utilized data from several public whole-cell, phenotypic high throughput screens that have been completed for T. cruzi by the Broad Institute, including a single screen of over 300,000 molecules in the search for chemical probes as part of the NIH Molecular Libraries program. We have also compiled and curated relevant biological and chemical compound screening data including (i) compounds and biological activity data from the literature, (ii) high throughput screening datasets, and (iii) predicted metabolites of T. cruzi metabolic pathways. This information was used to help us identify compounds and their potential targets. We have constructed a Pathway Genome Data Base for T. cruzi. In addition, we have developed Bayesian machine learning models that were used to virtually screen libraries of compounds. Ninety-seven compounds were selected for in vitro testing, and 11 of these were found to have EC50 < 10μM. We progressed five compounds to an in vivo mouse efficacy model of Chagas disease and validated that the machine learning model could identify in vitro active compounds not in the training set, as well as known positive controls. The antimalarial pyronaridine possessed 85.2% efficacy in the acute Chagas mouse model. We have also proposed potential targets (for future verification) for this compound based on structural similarity to known compounds with targets in T. cruzi. Conclusions/ Significance We have demonstrated how combining chemoinformatics and bioinformatics for T. cruzi drug discovery can bring interesting in vivo active molecules to light that may have been overlooked. The approach we have taken is broadly applicable to other NTDs. Chagas disease is a neglected tropical disease (NTD) caused by the eukaryotic parasite Trypanosoma cruzi. The disease is endemic to Latin America but is increasingly found in North America and Europe, primarily through immigration, and the spread of this disease is bringing new attention to the need for novel, safe, and effective therapeutics to treat T. cruzi infection. We have used data from a phenotypic screen to build Bayesian models to predict anti-parasitic activity against T. cruzi in vitro. These models were used to score various small libraries of molecules. We selected less than 100 compounds for testing and found in vitro actives, some of which were tested in an in vivo efficacy model. We identified the antimalarial pyronaridine as having in vivo efficacy and provides us with a new starting point for further investigation and optimization.
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Affiliation(s)
- Sean Ekins
- Collaborative Drug Discovery, Burlingame, California, United States of America
- Collaborations in Chemistry, Fuquay-Varina, North Carolina, United States of America
- * E-mail:
| | - Jair Lage de Siqueira-Neto
- Skaggs School of Pharmacy and Pharmaceutical Sciences, University of California, San Diego, San Diego, California, United States of America
| | - Laura-Isobel McCall
- Skaggs School of Pharmacy and Pharmaceutical Sciences, University of California, San Diego, San Diego, California, United States of America
| | - Malabika Sarker
- SRI International, Menlo Park, California, United States of America
| | - Maneesh Yadav
- SRI International, Menlo Park, California, United States of America
| | - Elizabeth L. Ponder
- Chemistry, Engineering & Medicine for Human Health (ChEM-H), Stanford, California, United States of America
| | - E. Adam Kallel
- Collaborative Drug Discovery, Burlingame, California, United States of America
| | - Danielle Kellar
- Department of Pathology, University of California, San Francisco, San Francisco, California, United States of America
| | - Steven Chen
- Small Molecule Discovery Center and Department of Pharmaceutical Chemistry, University of California, San Francisco, San Francisco, California, United States of America
| | - Michelle Arkin
- Small Molecule Discovery Center and Department of Pharmaceutical Chemistry, University of California, San Francisco, San Francisco, California, United States of America
| | - Barry A. Bunin
- Collaborative Drug Discovery, Burlingame, California, United States of America
| | - James H. McKerrow
- Skaggs School of Pharmacy and Pharmaceutical Sciences, University of California, San Diego, San Diego, California, United States of America
| | - Carolyn Talcott
- SRI International, Menlo Park, California, United States of America
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23
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Clark AM, Dole K, Coulon-Spektor A, McNutt A, Grass G, Freundlich JS, Reynolds RC, Ekins S. Open Source Bayesian Models. 1. Application to ADME/Tox and Drug Discovery Datasets. J Chem Inf Model 2015; 55:1231-45. [PMID: 25994950 PMCID: PMC4478615 DOI: 10.1021/acs.jcim.5b00143] [Citation(s) in RCA: 84] [Impact Index Per Article: 9.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/07/2023]
Abstract
![]()
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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Affiliation(s)
- Alex M Clark
- †Molecular Materials Informatics, Inc., 1900 St. Jacques No. 302, Montreal H3J 2S1, Quebec, Canada
| | - Krishna Dole
- ‡Collaborative Drug Discovery, 1633 Bayshore Highway, Suite 342, Burlingame, California 94010, United States
| | - Anna Coulon-Spektor
- ‡Collaborative Drug Discovery, 1633 Bayshore Highway, Suite 342, Burlingame, California 94010, United States
| | - Andrew McNutt
- ‡Collaborative Drug Discovery, 1633 Bayshore Highway, Suite 342, Burlingame, California 94010, United States
| | - George Grass
- §G2 Research, Inc., P.O. Box 1242, Tahoe City, California 96145, United States
| | | | - Robert C Reynolds
- #Department of Chemistry, College of Arts and Sciences, University of Alabama at Birmingham, , 1530 Third Avenue South, Birmingham, Alabama 35294-1240, United States
| | - Sean Ekins
- ‡Collaborative Drug Discovery, 1633 Bayshore Highway, Suite 342, Burlingame, California 94010, United States.,∇Collaborations in Chemistry, 5616 Hilltop Needmore Road, Fuquay-Varina, North Carolina 27526, United States
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24
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Clark AM, Ekins S. Open Source Bayesian Models. 2. Mining a "Big Dataset" To Create and Validate Models with ChEMBL. J Chem Inf Model 2015; 55:1246-60. [PMID: 25995041 DOI: 10.1021/acs.jcim.5b00144] [Citation(s) in RCA: 67] [Impact Index Per Article: 7.4] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/05/2023]
Abstract
In an associated paper, we have described a reference implementation of Laplacian-corrected naïve Bayesian model building using extended connectivity (ECFP)- and molecular function class fingerprints of maximum diameter 6 (FCFP)-type fingerprints. As a follow-up, we have now undertaken a large-scale validation study in order to ensure that the technique generalizes to a broad variety of drug discovery datasets. To achieve this, we have used the ChEMBL (version 20) database and split it into more than 2000 separate datasets, each of which consists of compounds and measurements with the same target and activity measurement. In order to test these datasets with the two-state Bayesian classification, we developed an automated algorithm for detecting a suitable threshold for active/inactive designation, which we applied to all collections. With these datasets, we were able to establish that our Bayesian model implementation is effective for the large majority of cases, and we were able to quantify the impact of fingerprint folding on the receiver operator curve cross-validation metrics. We were also able to study the impact that the choice of training/testing set partitioning has on the resulting recall rates. The datasets have been made publicly available to be downloaded, along with the corresponding model data files, which can be used in conjunction with the CDK and several mobile apps. We have also explored some novel visualization methods which leverage the structural origins of the ECFP/FCFP fingerprints to attribute regions of a molecule responsible for positive and negative contributions to activity. The ability to score molecules across thousands of relevant datasets across organisms also may help to access desirable and undesirable off-target effects as well as suggest potential targets for compounds derived from phenotypic screens.
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Affiliation(s)
- Alex M Clark
- †Molecular Materials Informatics, Inc., 1900 St. Jacques No. 302, Montreal H3J 2S1, Quebec, Canada
| | - Sean Ekins
- ‡Collaborations Pharmaceuticals, Inc., 5616 Hilltop Needmore Road, Fuquay-Varina, North Carolina 27526, United States.,§Collaborations in Chemistry, 5616 Hilltop Needmore Road, Fuquay-Varina, North Carolina 27526, United States.,∥Collaborative Drug Discovery, 1633 Bayshore Highway, Suite 342, Burlingame, California 94010, United States
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25
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Perryman AL, Yu W, Wang X, Ekins S, Forli S, Li SG, Freundlich JS, Tonge PJ, Olson AJ. A virtual screen discovers novel, fragment-sized inhibitors of Mycobacterium tuberculosis InhA. J Chem Inf Model 2015; 55:645-59. [PMID: 25636146 DOI: 10.1021/ci500672v] [Citation(s) in RCA: 31] [Impact Index Per Article: 3.4] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/03/2023]
Abstract
Isoniazid (INH) is usually administered to treat latent Mycobacterium tuberculosis (Mtb) infections and is used in combination therapy to treat active tuberculosis (TB). Unfortunately, resistance to this drug is hampering its clinical effectiveness. INH is a prodrug that must be activated by Mtb catalase-peroxidase (KatG) before it can inhibit InhA (Mtb enoyl-acyl-carrier-protein reductase). Isoniazid-resistant cases of TB found in clinical settings usually involve mutations in or deletion of katG, which abrogate INH activation. Compounds that inhibit InhA without requiring prior activation by KatG would not be affected by this resistance mechanism and hence would display continued potency against these drug-resistant isolates of Mtb. Virtual screening experiments versus InhA in the GO Fight Against Malaria (GO FAM) project were designed to discover new scaffolds that display base-stacking interactions with the NAD cofactor. GO FAM experiments included targets from other pathogens, including Mtb, when they had structural similarity to a malaria target. Eight of the 16 soluble compounds identified by docking against InhA plus visual inspection were modest inhibitors and did not require prior activation by KatG. The best two inhibitors discovered are both fragment-sized compounds and displayed Ki values of 54 and 59 μM, respectively. Importantly, the novel inhibitors discovered have low structural similarity to known InhA inhibitors and thus help expand the number of chemotypes on which future medicinal chemistry efforts can be focused. These new fragment hits could eventually help advance the fight against INH-resistant Mtb strains, which pose a significant global health threat.
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Affiliation(s)
- Alexander L Perryman
- †Department of Integrative Structural and Computational Biology, The Scripps Research Institute, La Jolla, California 92037, United States
| | | | | | - Sean Ekins
- ⊥Collaborations in Chemistry, 5616 Hilltop Needmore Road, Fuquay-Varina, North Carolina 27526, United States.,#Collaborative Drug Discovery, 1633 Bayshore Highway, Suite 342, Burlingame, California 94010, United States
| | - Stefano Forli
- †Department of Integrative Structural and Computational Biology, The Scripps Research Institute, La Jolla, California 92037, United States
| | | | | | | | - Arthur J Olson
- †Department of Integrative Structural and Computational Biology, The Scripps Research Institute, La Jolla, California 92037, United States
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26
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Warr WA. App-etite for change. J Comput Aided Mol Des 2014; 29:297-303. [PMID: 25515639 DOI: 10.1007/s10822-014-9824-1] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/07/2014] [Accepted: 12/10/2014] [Indexed: 10/24/2022]
Affiliation(s)
- Wendy A Warr
- Wendy Warr & Associates, Holmes Chapel, Cheshire, UK,
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Sharma A, Dutta P, Sharma M, Rajput NK, Dodiya B, Georrge JJ, Kholia T, Bhardwaj A. BioPhytMol: a drug discovery community resource on anti-mycobacterial phytomolecules and plant extracts. J Cheminform 2014; 6:46. [PMID: 25360160 PMCID: PMC4206768 DOI: 10.1186/s13321-014-0046-2] [Citation(s) in RCA: 25] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/07/2014] [Accepted: 09/23/2014] [Indexed: 11/25/2022] Open
Abstract
Background Tuberculosis (TB) is the second leading cause of death from a single infectious organism, demanding attention towards discovery of novel anti-tubercular compounds. Natural products or their derivatives have provided more than 50% of all existing drugs, offering a chemically diverse space for discovery of novel drugs. Description BioPhytMol has been designed to systematically curate and analyze the anti-mycobacterial natural product chemical space. BioPhytMol is developed as a drug-discovery community resource with anti-mycobacterial phytomolecules and plant extracts. Currently, it holds 2582 entries including 188 plant families (692 genera and 808 species) from global flora, manually curated from literature. In total, there are 633 phytomolecules (with structures) curated against 25 target mycobacteria. Multiple analysis approaches have been used to prioritize the library for drug-like compounds, for both whole cell screening and target-based approaches. In order to represent the multidimensional data on chemical diversity, physiochemical properties and biological activity data of the compound library, novel approaches such as the use of circular graphs have been employed. Conclusion BioPhytMol has been designed to systematically represent and search for anti-mycobacterial phytochemical information. Extensive compound analyses can also be performed through web-application for prioritizing drug-like compounds. The resource is freely available online at http://ab-openlab.csir.res.in/biophytmol/. BioPhytMol: a drug discovery community resource on anti-mycobacterial phytomolecules and plant extracts generated using Crowdsourcing. The platform comprises of manually curated data on antimycobacterial natural products along with tools to perform structure similarity and visualization. The platform allows for prioritization of drug like natural products for antimycobacterial drug discovery. ![]()
Electronic supplementary material The online version of this article (doi:10.1186/s13321-014-0046-2) contains supplementary material, which is available to authorized users.
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Affiliation(s)
- Arun Sharma
- Open Source Drug Discovery (OSDD) Unit, Council of Scientific and Industrial Research, New Delhi, India
| | - Prasun Dutta
- Open Source Drug Discovery (OSDD) Unit, Council of Scientific and Industrial Research, New Delhi, India
| | - Maneesh Sharma
- St. Stephens College, University of Delhi, New Delhi, India
| | - Neeraj Kumar Rajput
- Open Source Drug Discovery (OSDD) Unit, Council of Scientific and Industrial Research, New Delhi, India
| | - Bhavna Dodiya
- Department of Applied Mathematics and Bioinformatics, Faculty of Technology and Engineering, The Maharaja Sayajirao University of Baroda, Vadodara, Gujarat India
| | - John J Georrge
- Department of Bioinformatics, Christ College, Rajkot, Gujarat India ; Department of Biochemistry and Molecular Biology, University Clinic of Bonn (UKB), University of Bonn, Bonn, Germany
| | - Trupti Kholia
- Department of Bioinformatics, Christ College, Rajkot, Gujarat India
| | | | - Anshu Bhardwaj
- Open Source Drug Discovery (OSDD) Unit, Council of Scientific and Industrial Research, New Delhi, India
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28
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Ekins S, Clark AM, Swamidass SJ, Litterman N, Williams AJ. Bigger data, collaborative tools and the future of predictive drug discovery. J Comput Aided Mol Des 2014; 28:997-1008. [PMID: 24943138 PMCID: PMC4198464 DOI: 10.1007/s10822-014-9762-y] [Citation(s) in RCA: 19] [Impact Index Per Article: 1.9] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/29/2014] [Accepted: 06/09/2014] [Indexed: 12/31/2022]
Abstract
Over the past decade we have seen a growth in the provision of chemistry data and cheminformatics tools as either free websites or software as a service commercial offerings. These have transformed how we find molecule-related data and use such tools in our research. There have also been efforts to improve collaboration between researchers either openly or through secure transactions using commercial tools. A major challenge in the future will be how such databases and software approaches handle larger amounts of data as it accumulates from high throughput screening and enables the user to draw insights, enable predictions and move projects forward. We now discuss how information from some drug discovery datasets can be made more accessible and how privacy of data should not overwhelm the desire to share it at an appropriate time with collaborators. We also discuss additional software tools that could be made available and provide our thoughts on the future of predictive drug discovery in this age of big data. We use some examples from our own research on neglected diseases, collaborations, mobile apps and algorithm development to illustrate these ideas.
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Affiliation(s)
- Sean Ekins
- Collaborations in Chemistry, 5616 Hilltop Needmore Road, Fuquay-Varina, NC, 27526, USA,
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