1
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Snyder SH, Vignaux PA, Ozalp MK, Gerlach J, Puhl AC, Lane TR, Corbett J, Urbina F, Ekins S. The Goldilocks paradigm: comparing classical machine learning, large language models, and few-shot learning for drug discovery applications. Commun Chem 2024; 7:134. [PMID: 38866916 PMCID: PMC11169557 DOI: 10.1038/s42004-024-01220-4] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/21/2023] [Accepted: 06/04/2024] [Indexed: 06/14/2024] Open
Abstract
Recent advances in machine learning (ML) have led to newer model architectures including transformers (large language models, LLMs) showing state of the art results in text generation and image analysis as well as few-shot learning (FSLC) models which offer predictive power with extremely small datasets. These new architectures may offer promise, yet the 'no-free lunch' theorem suggests that no single model algorithm can outperform at all possible tasks. Here, we explore the capabilities of classical (SVR), FSLC, and transformer models (MolBART) over a range of dataset tasks and show a 'goldilocks zone' for each model type, in which dataset size and feature distribution (i.e. dataset "diversity") determines the optimal algorithm strategy. When datasets are small ( < 50 molecules), FSLC tend to outperform both classical ML and transformers. When datasets are small-to-medium sized (50-240 molecules) and diverse, transformers outperform both classical models and few-shot learning. Finally, when datasets are of larger and of sufficient size, classical models then perform the best, suggesting that the optimal model to choose likely depends on the dataset available, its size and diversity. These findings may help to answer the perennial question of which ML algorithm is to be used when faced with a new dataset.
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Affiliation(s)
- Scott H Snyder
- Collaborations Pharmaceuticals, Inc., 840 Main Campus Drive, Lab 3510, Raleigh, NC, 27606, USA
| | - Patricia A Vignaux
- Collaborations Pharmaceuticals, Inc., 840 Main Campus Drive, Lab 3510, Raleigh, NC, 27606, USA
| | - Mustafa Kemal Ozalp
- 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
| | - Ana C Puhl
- Collaborations Pharmaceuticals, Inc., 840 Main Campus Drive, Lab 3510, Raleigh, NC, 27606, USA
| | - Thomas R Lane
- Collaborations Pharmaceuticals, Inc., 840 Main Campus Drive, Lab 3510, Raleigh, NC, 27606, USA
| | - John Corbett
- 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.
| | - Sean Ekins
- Collaborations Pharmaceuticals, Inc., 840 Main Campus Drive, Lab 3510, Raleigh, NC, 27606, USA.
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2
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Iancu A, Leb I, Prokosch HU, Rödle W. Machine learning in medication prescription: A systematic review. Int J Med Inform 2023; 180:105241. [PMID: 37939541 DOI: 10.1016/j.ijmedinf.2023.105241] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/06/2023] [Revised: 09/17/2023] [Accepted: 09/27/2023] [Indexed: 11/10/2023]
Abstract
BACKGROUND Medication prescription is a complex process that could benefit from current research and development in machine learning through decision support systems. Particularly pediatricians are forced to prescribe medications "off-label" as children are still underrepresented in clinical studies, which leads to a high risk of an incorrect dose and adverse drug effects. METHODS PubMed, IEEE Xplore and PROSPERO were searched for relevant studies that developed and evaluated well-performing machine learning algorithms following the PRISMA statement. Quality assessment was conducted in accordance with the IJMEDI checklist. Identified studies were reviewed in detail, including the required variables for predicting the correct dose, especially of pediatric medication prescription. RESULTS The search identified 656 studies, of which 64 were reviewed in detail and 36 met the inclusion criteria. According to the IJMEDI checklist, five studies were considered to be of high quality. 19 of the 36 studies dealt with the active substance warfarin. Overall, machine learning algorithms based on decision trees or regression methods performed superior regarding their predictive power than algorithms based on neural networks, support vector machines or other methods. The use of ensemble methods like bagging or boosting generally enhanced the accuracy of the dose predictions. The required input and output variables of the algorithms were considerably heterogeneous and differ strongly among the respective substance. CONCLUSIONS By using machine learning algorithms, the prescription process could be simplified and dosing correctness could be enhanced. Despite the heterogenous results among the different substances and cases and the lack of pediatric use cases, the identified approaches and required variables can serve as an excellent starting point for further development of algorithms predicting drug doses, particularly for children. Especially the combination of physiologically-based pharmacokinetic models with machine learning algorithms represents a great opportunity to enhance the predictive power and accuracy of the developed algorithms.
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Affiliation(s)
- Alexa Iancu
- Chair of Medical Informatics, Friedrich-Alexander-Universität Erlangen-Nürnberg (FAU), Wetterkreuz 15, 91058 Erlangen, Germany
| | - Ines Leb
- Chair of Medical Informatics, Friedrich-Alexander-Universität Erlangen-Nürnberg (FAU), Wetterkreuz 15, 91058 Erlangen, Germany
| | - Hans-Ulrich Prokosch
- Chair of Medical Informatics, Friedrich-Alexander-Universität Erlangen-Nürnberg (FAU), Wetterkreuz 15, 91058 Erlangen, Germany
| | - Wolfgang Rödle
- Chair of Medical Informatics, Friedrich-Alexander-Universität Erlangen-Nürnberg (FAU), Wetterkreuz 15, 91058 Erlangen, Germany.
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3
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Linciano P, Quotadamo A, Luciani R, Santucci M, Zorn KM, Foil DH, Lane TR, Cordeiro da Silva A, Santarem N, B Moraes C, Freitas-Junior L, Wittig U, Mueller W, Tonelli M, Ferrari S, Venturelli A, Gul S, Kuzikov M, Ellinger B, Reinshagen J, Ekins S, Costi MP. High-Throughput Phenotypic Screening and Machine Learning Methods Enabled the Selection of Broad-Spectrum Low-Toxicity Antitrypanosomatidic Agents. J Med Chem 2023; 66:15230-15255. [PMID: 37921561 PMCID: PMC10683024 DOI: 10.1021/acs.jmedchem.3c01322] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/21/2023] [Revised: 10/14/2023] [Accepted: 10/18/2023] [Indexed: 11/04/2023]
Abstract
Broad-spectrum anti-infective chemotherapy agents with activity against Trypanosomes, Leishmania, and Mycobacterium tuberculosis species were identified from a high-throughput phenotypic screening program of the 456 compounds belonging to the Ty-Box, an in-house industry database. Compound characterization using machine learning approaches enabled the identification and synthesis of 44 compounds with broad-spectrum antiparasitic activity and minimal toxicity against Trypanosoma brucei, Leishmania Infantum, and Trypanosoma cruzi. In vitro studies confirmed the predictive models identified in compound 40 which emerged as a new lead, featured by an innovative N-(5-pyrimidinyl)benzenesulfonamide scaffold and promising low micromolar activity against two parasites and low toxicity. Given the volume and complexity of data generated by the diverse high-throughput screening assays performed on the compounds of the Ty-Box library, the chemoinformatic and machine learning tools enabled the selection of compounds eligible for further evaluation of their biological and toxicological activities and aided in the decision-making process toward the design and optimization of the identified lead.
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Affiliation(s)
- Pasquale Linciano
- Department
of Life Sciences, University of Modena and
Reggio Emilia, Via Campi 103, 41125 Modena, Italy
| | - Antonio Quotadamo
- Department
of Life Sciences, University of Modena and
Reggio Emilia, Via Campi 103, 41125 Modena, Italy
| | - Rosaria Luciani
- Department
of Life Sciences, University of Modena and
Reggio Emilia, Via Campi 103, 41125 Modena, Italy
| | - Matteo Santucci
- Department
of Life Sciences, University of Modena and
Reggio Emilia, Via Campi 103, 41125 Modena, Italy
| | - Kimberley M. Zorn
- Collaborations
Pharmaceuticals, Inc., 840 Main Campus Drive, Lab 3510, Raleigh, North Carolina 27606, United States
| | - Daniel H. Foil
- Collaborations
Pharmaceuticals, Inc., 840 Main Campus Drive, Lab 3510, Raleigh, North Carolina 27606, United States
| | - Thomas R. Lane
- Collaborations
Pharmaceuticals, Inc., 840 Main Campus Drive, Lab 3510, Raleigh, North Carolina 27606, United States
| | - Anabela Cordeiro da Silva
- Institute
for Molecular and Cell Biology, 4150-180 Porto, Portugal
- Instituto
de Investigaçao e Inovaçao em Saúde, Universidade do Porto and Institute for Molecular
and Cell Biology, 4150-180 Porto, Portugal
| | - Nuno Santarem
- Institute
for Molecular and Cell Biology, 4150-180 Porto, Portugal
- Instituto
de Investigaçao e Inovaçao em Saúde, Universidade do Porto and Institute for Molecular
and Cell Biology, 4150-180 Porto, Portugal
| | - Carolina B Moraes
- Brazilian
Biosciences National Laboratory (LNBio), Brazilian Center for Research in Energy and Materials (CNPEM), 13083-970 Campinas, São Paulo, Brazil
| | - Lucio Freitas-Junior
- Brazilian
Biosciences National Laboratory (LNBio), Brazilian Center for Research in Energy and Materials (CNPEM), 13083-970 Campinas, São Paulo, Brazil
| | - Ulrike Wittig
- Scientific
Databases and Visualization Group and Molecular and Cellular Modelling
Group, Heidelberg Institute for Theoretical
Studies (HITS), D-69118 Heidelberg, Germany
| | - Wolfgang Mueller
- Scientific
Databases and Visualization Group and Molecular and Cellular Modelling
Group, Heidelberg Institute for Theoretical
Studies (HITS), D-69118 Heidelberg, Germany
| | - Michele Tonelli
- Department
of Pharmacy, University of Genoa, Viale Benedetto XV n.3, 16132 Genoa, Italy
| | - Stefania Ferrari
- Department
of Life Sciences, University of Modena and
Reggio Emilia, Via Campi 103, 41125 Modena, Italy
| | - Alberto Venturelli
- Department
of Life Sciences, University of Modena and
Reggio Emilia, Via Campi 103, 41125 Modena, Italy
- TYDOCK
PHARMA S.r.l., Strada
Gherbella 294/b, 41126 Modena, Italy
| | - Sheraz Gul
- Fraunhofer
Translational Medicine and Pharmacology, Schnackenburgallee 114, D-22525 Hamburg, Germany
- Fraunhofer Cluster of Excellence Immune-Mediated Diseases
CIMD, Schnackenburgallee
114, D-22525 Hamburg, Germany
| | - Maria Kuzikov
- Fraunhofer
Translational Medicine and Pharmacology, Schnackenburgallee 114, D-22525 Hamburg, Germany
- Fraunhofer Cluster of Excellence Immune-Mediated Diseases
CIMD, Schnackenburgallee
114, D-22525 Hamburg, Germany
| | - Bernhard Ellinger
- Fraunhofer
Translational Medicine and Pharmacology, Schnackenburgallee 114, D-22525 Hamburg, Germany
- Fraunhofer Cluster of Excellence Immune-Mediated Diseases
CIMD, Schnackenburgallee
114, D-22525 Hamburg, Germany
| | - Jeanette Reinshagen
- Fraunhofer
Translational Medicine and Pharmacology, Schnackenburgallee 114, D-22525 Hamburg, Germany
- Fraunhofer Cluster of Excellence Immune-Mediated Diseases
CIMD, Schnackenburgallee
114, D-22525 Hamburg, Germany
| | - Sean Ekins
- Collaborations
Pharmaceuticals, Inc., 840 Main Campus Drive, Lab 3510, Raleigh, North Carolina 27606, United States
| | - Maria Paola Costi
- Department
of Life Sciences, University of Modena and
Reggio Emilia, Via Campi 103, 41125 Modena, Italy
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4
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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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5
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Schmalstig AA, Zorn KM, Murci S, Robinson A, Savina S, Komarova E, Makarov V, Braunstein M, Ekins S. Mycobacterium abscessus drug discovery using machine learning. Tuberculosis (Edinb) 2022; 132:102168. [PMID: 35077930 PMCID: PMC8855326 DOI: 10.1016/j.tube.2022.102168] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/16/2021] [Revised: 10/30/2021] [Accepted: 01/14/2022] [Indexed: 01/22/2023]
Abstract
The prevalence of infections by nontuberculous mycobacteria is increasing, having surpassed tuberculosis in the United States and much of the developed world. Nontuberculous mycobacteria occur naturally in the environment and are a significant problem for patients with underlying lung diseases such as bronchiectasis, chronic obstructive pulmonary disease, and cystic fibrosis. Current treatment regimens are lengthy, complicated, toxic and they are often unsuccessful as seen by disease recurrence. Mycobacterium abscessus is one of the most commonly encountered organisms in nontuberculous mycobacteria disease and it is the most difficult to eradicate. There is currently no systematically proven regimen that is effective for treating M. abscessus infections. Our approach to drug discovery integrates machine learning, medicinal chemistry and in vitro testing and has been previously applied to Mycobacterium tuberculosis. We have now identified several novel 1-(phenylsulfonyl)-1H-benzimidazol-2-amines that have weak activity on M. abscessus in vitro but may represent a starting point for future further medicinal chemistry optimization. We also address limitations still to be overcome with the machine learning approach for M. abscessus.
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Affiliation(s)
- Alan A. Schmalstig
- Department of Microbiology and Immunology, School of Medicine, University of North Carolina at Chapel Hill, North Carolina, 27599, USA
| | - Kimberley M. Zorn
- Collaborations Pharmaceuticals, Inc., 840 Main Campus Drive Lab 3510, Raleigh, North Carolina, 27606, USA
| | - Sebastian Murci
- Department of Microbiology and Immunology, School of Medicine, University of North Carolina at Chapel Hill, North Carolina, 27599, USA
| | - Andrew Robinson
- Department of Microbiology and Immunology, School of Medicine, University of North Carolina at Chapel Hill, North Carolina, 27599, USA
| | - Svetlana Savina
- Research Center of Biotechnology RAS, Moscow, 119071, Russia
| | - Elena Komarova
- Research Center of Biotechnology RAS, Moscow, 119071, Russia
| | - Vadim Makarov
- Research Center of Biotechnology RAS, Moscow, 119071, Russia
| | - Miriam Braunstein
- Department of Microbiology and Immunology, School of Medicine, University of North Carolina at Chapel Hill, North Carolina, 27599, USA
| | - Sean Ekins
- Collaborations Pharmaceuticals, Inc., 840 Main Campus Drive Lab 3510, Raleigh, North Carolina, 27606, USA.,Corresponding author: Collaborations Pharmaceuticals, Inc., 840 Main Campus Drive Lab 3510, Raleigh, North Carolina, 27606, USA.
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6
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Batra K, Zorn KM, Foil DH, Minerali E, Gawriljuk VO, Lane TR, Ekins S. Quantum Machine Learning Algorithms for Drug Discovery Applications. J Chem Inf Model 2021; 61:2641-2647. [PMID: 34032436 PMCID: PMC8254374 DOI: 10.1021/acs.jcim.1c00166] [Citation(s) in RCA: 12] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/23/2022]
Abstract
The growing quantity of public and private data sets focused on small molecules screened against biological targets or whole organisms provides a wealth of drug discovery relevant data. This is matched by the availability of machine learning algorithms such as Support Vector Machines (SVM) and Deep Neural Networks (DNN) that are computationally expensive to perform on very large data sets with thousands of molecular descriptors. Quantum computer (QC) algorithms have been proposed to offer an approach to accelerate quantum machine learning over classical computer (CC) algorithms, however with significant limitations. In the case of cheminformatics, which is widely used in drug discovery, one of the challenges to overcome is the need for compression of large numbers of molecular descriptors for use on a QC. Here, we show how to achieve compression with data sets using hundreds of molecules (SARS-CoV-2) to hundreds of thousands of molecules (whole cell screening data sets for plague and M. tuberculosis) with SVM and the data reuploading classifier (a DNN equivalent algorithm) on a QC benchmarked against CC and hybrid approaches. This study illustrates the steps needed in order to be "quantum computer ready" in order to apply quantum computing to drug discovery and to provide the foundation on which to build this field.
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Affiliation(s)
- Kushal Batra
- Computer Science, NC State University, Raleigh, NC 27606, USA
| | - Kimberley M. Zorn
- Collaborations Pharmaceuticals, Inc., 840 Main Campus Drive, Lab 3510, Raleigh, NC 27606, USA
| | - Daniel H. Foil
- Collaborations Pharmaceuticals, Inc., 840 Main Campus Drive, Lab 3510, Raleigh, NC 27606, USA
| | - Eni Minerali
- Collaborations Pharmaceuticals, Inc., 840 Main Campus Drive, Lab 3510, Raleigh, NC 27606, USA
| | - Victor O. Gawriljuk
- São Carlos Institute of Physics, University of São Paulo, Av. João Dagnone, 1100 - Santa Angelina, São Carlos - SP, 13563-120, Brazil
| | - Thomas R. Lane
- Collaborations Pharmaceuticals, Inc., 840 Main Campus Drive, Lab 3510, Raleigh, NC 27606, USA
| | - Sean Ekins
- Collaborations Pharmaceuticals, Inc., 840 Main Campus Drive, Lab 3510, Raleigh, NC 27606, USA
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7
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Winkler DA. Use of Artificial Intelligence and Machine Learning for Discovery of Drugs for Neglected Tropical Diseases. Front Chem 2021; 9:614073. [PMID: 33791277 PMCID: PMC8005575 DOI: 10.3389/fchem.2021.614073] [Citation(s) in RCA: 19] [Impact Index Per Article: 6.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/05/2020] [Accepted: 01/18/2021] [Indexed: 12/11/2022] Open
Abstract
Neglected tropical diseases continue to create high levels of morbidity and mortality in a sizeable fraction of the world’s population, despite ongoing research into new treatments. Some of the most important technological developments that have accelerated drug discovery for diseases of affluent countries have not flowed down to neglected tropical disease drug discovery. Pharmaceutical development business models, cost of developing new drug treatments and subsequent costs to patients, and accessibility of technologies to scientists in most of the affected countries are some of the reasons for this low uptake and slow development relative to that for common diseases in developed countries. Computational methods are starting to make significant inroads into discovery of drugs for neglected tropical diseases due to the increasing availability of large databases that can be used to train ML models, increasing accuracy of these methods, lower entry barrier for researchers, and widespread availability of public domain machine learning codes. Here, the application of artificial intelligence, largely the subset called machine learning, to modelling and prediction of biological activities and discovery of new drugs for neglected tropical diseases is summarized. The pathways for the development of machine learning methods in the short to medium term and the use of other artificial intelligence methods for drug discovery is discussed. The current roadblocks to, and likely impacts of, synergistic new technological developments on the use of ML methods for neglected tropical disease drug discovery in the future are also discussed.
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Affiliation(s)
- David A Winkler
- Monash Institute of Pharmaceutical Sciences, Monash University, Parkville, VIC, Australia.,Latrobe Institute for Molecular Science, La Trobe University, Bundoora, VIC, Australia.,School of Pharmacy, University of Nottingham, Nottingham, United Kingdom.,CSIRO Data61, Pullenvale, QLD, Australia
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8
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Lane TR, Foil DH, Minerali E, Urbina F, Zorn KM, Ekins S. Bioactivity Comparison across Multiple Machine Learning Algorithms Using over 5000 Datasets for Drug Discovery. Mol Pharm 2020; 18:403-415. [PMID: 33325717 DOI: 10.1021/acs.molpharmaceut.0c01013] [Citation(s) in RCA: 21] [Impact Index Per Article: 5.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/14/2022]
Abstract
Machine learning methods are attracting considerable attention from the pharmaceutical industry for use in drug discovery and applications beyond. In recent studies, we and others have applied multiple machine learning algorithms and modeling metrics and, in some cases, compared molecular descriptors to build models for individual targets or properties on a relatively small scale. Several research groups have used large numbers of datasets from public databases such as ChEMBL in order to evaluate machine learning methods of interest to them. The largest of these types of studies used on the order of 1400 datasets. We have now extracted well over 5000 datasets from CHEMBL for use with the ECFP6 fingerprint and in comparison of our proprietary software Assay Central with random forest, k-nearest neighbors, support vector classification, naïve Bayesian, AdaBoosted decision trees, and deep neural networks (three layers). Model performance was assessed using an array of fivefold cross-validation metrics including area-under-the-curve, F1 score, Cohen's kappa, and Matthews correlation coefficient. Based on ranked normalized scores for the metrics or datasets, all methods appeared comparable, while the distance from the top indicated that Assay Central and support vector classification were comparable. Unlike prior studies which have placed considerable emphasis on deep neural networks (deep learning), no advantage was seen in this case. If anything, Assay Central may have been at a slight advantage as the activity cutoff for each of the over 5000 datasets representing over 570,000 unique compounds was based on Assay Central performance, although support vector classification seems to be a strong competitor. We also applied Assay Central to perform prospective predictions for the toxicity targets PXR and hERG to further validate these models. This work appears to be the largest scale comparison of these machine learning algorithms to date. Future studies will likely evaluate additional databases, descriptors, and machine learning algorithms and further refine the methods for evaluating and comparing such models.
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Affiliation(s)
- Thomas R Lane
- Collaborations Pharmaceuticals, Inc., 840 Main Campus Drive, Lab 3510, Raleigh, North Carolina 27606, United States
| | - Daniel H Foil
- Collaborations Pharmaceuticals, Inc., 840 Main Campus Drive, Lab 3510, Raleigh, North Carolina 27606, United States
| | - Eni Minerali
- Collaborations Pharmaceuticals, Inc., 840 Main Campus Drive, Lab 3510, Raleigh, North Carolina 27606, United States
| | - Fabio Urbina
- Department of Cell Biology and Physiology, University of North Carolina at Chapel Hill, Chapel Hill, North Carolina 27599-7545, United States
| | - Kimberley M Zorn
- Collaborations Pharmaceuticals, Inc., 840 Main Campus Drive, Lab 3510, Raleigh, North Carolina 27606, United States
| | - Sean Ekins
- Collaborations Pharmaceuticals, Inc., 840 Main Campus Drive, Lab 3510, Raleigh, North Carolina 27606, United States
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9
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Makarov V, Salina E, Reynolds RC, Kyaw Zin PP, Ekins S. Molecule Property Analyses of Active Compounds for Mycobacterium tuberculosis. J Med Chem 2020; 63:8917-8955. [PMID: 32259446 DOI: 10.1021/acs.jmedchem.9b02075] [Citation(s) in RCA: 16] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/12/2022]
Abstract
Tuberculosis (TB) continues to claim the lives of around 1.7 million people per year. Most concerning are the reports of multidrug drug resistance. Paradoxically, this global health pandemic is demanding new therapies when resources and interest are waning. However, continued tuberculosis drug discovery is critical to address the global health need and burgeoning multidrug resistance. Many diverse classes of antitubercular compounds have been identified with activity in vitro and in vivo. Our analyses of over 100 active leads are representative of thousands of active compounds generated over the past decade, suggests that they come from few chemical classes or natural product sources. We are therefore repeatedly identifying compounds that are similar to those that preceded them. Our molecule-centered cheminformatics analyses point to the need to dramatically increase the diversity of chemical libraries tested and get outside of the historic Mtb property space if we are to generate novel improved antitubercular leads.
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Affiliation(s)
- Vadim Makarov
- FRC Fundamentals of Biotechnology, Russian Academy of Science, Moscow 119071, Russia
| | - Elena Salina
- FRC Fundamentals of Biotechnology, Russian Academy of Science, Moscow 119071, Russia
| | - Robert C Reynolds
- Department of Medicine, Division of Hematology and Oncology, University of Alabama at Birmingham, NP 2540 J, 1720 Second Avenue South, Birmingham, Alabama 35294-3300, United States
| | - Phyo Phyo Kyaw Zin
- Department of Chemistry, North Carolina State University, Raleigh, North Carolina 27695, United States.,Bioinformatics Research Center, North Carolina State University, Raleigh, North Carolina 27695, United States
| | - Sean Ekins
- Collaborations Pharmaceuticals, Inc., 840 Main Campus Drive, Lab 3510 Raleigh, North Carolina 27606, United States
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10
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Teixeira C, Ventura C, Gomes JRB, Gomes P, Martins F. Cinnamic Derivatives as Antitubercular Agents: Characterization by Quantitative Structure-Activity Relationship Studies. Molecules 2020; 25:molecules25030456. [PMID: 31973244 PMCID: PMC7037561 DOI: 10.3390/molecules25030456] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/19/2019] [Revised: 01/15/2020] [Accepted: 01/17/2020] [Indexed: 11/22/2022] Open
Abstract
Tuberculosis, caused by Mycobacterium tuberculosis (Mtb), remains one of the top ten causes of death worldwide and the main cause of mortality from a single infectious agent. The upsurge of multi- and extensively-drug resistant tuberculosis cases calls for an urgent need to develop new and more effective antitubercular drugs. As the cinnamoyl scaffold is a privileged and important pharmacophore in medicinal chemistry, some studies were conducted to find novel cinnamic acid derivatives (CAD) potentially active against tuberculosis. In this context, we have engaged in the setting up of a quantitative structure–activity relationships (QSAR) strategy to: (i) derive through multiple linear regression analysis a statistically significant model to describe the antitubercular activity of CAD towards wild-type Mtb; and (ii) identify the most relevant properties with an impact on the antitubercular behavior of those derivatives. The best-found model involved only geometrical and electronic CAD related properties and was successfully challenged through strict internal and external validation procedures. The physicochemical information encoded by the identified descriptors can be used to propose specific structural modifications to design better CAD antitubercular compounds.
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Affiliation(s)
- Cátia Teixeira
- LAQV-REQUIMTE, Departamento de Química e Bioquímica da Faculdade de Ciências da Universidade do Porto, P-4169-007 Porto, Portugal
- Correspondence: (C.T.); (F.M.)
| | - Cristina Ventura
- Instituto Superior de Educação e Ciências, P-1750-142 Lisboa, Portugal
| | - José R. B. Gomes
- CICECO, Departamento de Química, Universidade de Aveiro, P-3810-193 Aveiro, Portugal
| | - Paula Gomes
- LAQV-REQUIMTE, Departamento de Química e Bioquímica da Faculdade de Ciências da Universidade do Porto, P-4169-007 Porto, Portugal
| | - Filomena Martins
- Centro de Química e Bioquímica (CQB), Centro de Química Estrutural (CQE), Faculdade de Ciências da Universidade de Lisboa, P-1749-016 Lisboa, Portugal
- Correspondence: (C.T.); (F.M.)
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11
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Wang X, Perryman AL, Li SG, Paget SD, Stratton TP, Lemenze A, Olson AJ, Ekins S, Kumar P, Freundlich JS. Intrabacterial Metabolism Obscures the Successful Prediction of an InhA Inhibitor of Mycobacterium tuberculosis. ACS Infect Dis 2019; 5:2148-2163. [PMID: 31625383 DOI: 10.1021/acsinfecdis.9b00295] [Citation(s) in RCA: 9] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/29/2022]
Abstract
Tuberculosis, caused by Mycobacterium tuberculosis (M. tuberculosis), kills 1.6 million people annually. To bridge the gap between structure- and cell-based drug discovery strategies, we are pioneering a computer-aided discovery paradigm that merges structure-based virtual screening with ligand-based, machine learning methods trained with cell-based data. This approach successfully identified N-(3-methoxyphenyl)-7-nitrobenzo[c][1,2,5]oxadiazol-4-amine (JSF-2164) as an inhibitor of purified InhA with whole-cell efficacy versus in vitro cultured M. tuberculosis. When the intrabacterial drug metabolism (IBDM) platform was leveraged, mechanistic studies demonstrated that JSF-2164 underwent a rapid F420H2-dependent biotransformation within M. tuberculosis to afford intrabacterial nitric oxide and two amines, identified as JSF-3616 and JSF-3617. Thus, metabolism of JSF-2164 obscured the InhA inhibition phenotype within cultured M. tuberculosis. This study demonstrates a new docking/Bayesian computational strategy to combine cell- and target-based drug screening and the need to probe intrabacterial metabolism when clarifying the antitubercular mechanism of action.
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Affiliation(s)
- Xin Wang
- Department of Pharmacology, Physiology, and Neuroscience, Rutgers University−New Jersey Medical School, Medical Sciences Building, 185 South Orange Avenue, Newark, New Jersey 07103, United States
| | - Alexander L. Perryman
- Department of Pharmacology, Physiology, and Neuroscience, Rutgers University−New Jersey Medical School, Medical Sciences Building, 185 South Orange Avenue, Newark, New Jersey 07103, United States
| | - Shao-Gang Li
- Department of Pharmacology, Physiology, and Neuroscience, Rutgers University−New Jersey Medical School, Medical Sciences Building, 185 South Orange Avenue, Newark, New Jersey 07103, United States
| | - Steve D. Paget
- Department of Pharmacology, Physiology, and Neuroscience, Rutgers University−New Jersey Medical School, Medical Sciences Building, 185 South Orange Avenue, Newark, New Jersey 07103, United States
| | - Thomas P. Stratton
- Department of Pharmacology, Physiology, and Neuroscience, Rutgers University−New Jersey Medical School, Medical Sciences Building, 185 South Orange Avenue, Newark, New Jersey 07103, United States
| | - Alex Lemenze
- Division of Infectious Disease, Department of Medicine, and the Ruy V. Lourenço Center for the Study of Emerging and Reemerging Pathogens, Rutgers University−New Jersey Medical School, Medical Sciences Building, 185 South Orange Avenue, Newark, New Jersey 07103, United States
| | - Arthur J. Olson
- Department of Integrative Structural and Computational Biology, The Scripps Research Institute, Room MB112/Mail Drop MB5, 10550 North Torrey Pines Road, La Jolla, California 92037, United States
| | - Sean Ekins
- Collaborations in Chemistry, 5616 Hilltop Needmore Road, Fuquay-Varina, North Carolina 27526, United States
| | - Pradeep Kumar
- Division of Infectious Disease, Department of Medicine, and the Ruy V. Lourenço Center for the Study of Emerging and Reemerging Pathogens, Rutgers University−New Jersey Medical School, Medical Sciences Building, 185 South Orange Avenue, Newark, New Jersey 07103, United States
| | - Joel S. Freundlich
- Department of Pharmacology, Physiology, and Neuroscience, Rutgers University−New Jersey Medical School, Medical Sciences Building, 185 South Orange Avenue, Newark, New Jersey 07103, United States
- Division of Infectious Disease, Department of Medicine, and the Ruy V. Lourenço Center for the Study of Emerging and Reemerging Pathogens, Rutgers University−New Jersey Medical School, Medical Sciences Building, 185 South Orange Avenue, Newark, New Jersey 07103, United States
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12
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Yang X, Wang Y, Byrne R, Schneider G, Yang S. Concepts of Artificial Intelligence for Computer-Assisted Drug Discovery. Chem Rev 2019; 119:10520-10594. [PMID: 31294972 DOI: 10.1021/acs.chemrev.8b00728] [Citation(s) in RCA: 346] [Impact Index Per Article: 69.2] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/05/2023]
Abstract
Artificial intelligence (AI), and, in particular, deep learning as a subcategory of AI, provides opportunities for the discovery and development of innovative drugs. Various machine learning approaches have recently (re)emerged, some of which may be considered instances of domain-specific AI which have been successfully employed for drug discovery and design. This review provides a comprehensive portrayal of these machine learning techniques and of their applications in medicinal chemistry. After introducing the basic principles, alongside some application notes, of the various machine learning algorithms, the current state-of-the art of AI-assisted pharmaceutical discovery is discussed, including applications in structure- and ligand-based virtual screening, de novo drug design, physicochemical and pharmacokinetic property prediction, drug repurposing, and related aspects. Finally, several challenges and limitations of the current methods are summarized, with a view to potential future directions for AI-assisted drug discovery and design.
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Affiliation(s)
- Xin Yang
- State Key Laboratory of Biotherapy and Cancer Center, West China Hospital , Sichuan University , Chengdu , Sichuan 610041 , China
| | - Yifei Wang
- State Key Laboratory of Biotherapy and Cancer Center, West China Hospital , Sichuan University , Chengdu , Sichuan 610041 , China
| | - Ryan Byrne
- ETH Zurich , Department of Chemistry and Applied Biosciences , Vladimir-Prelog-Weg 4 , CH-8093 Zurich , Switzerland
| | - Gisbert Schneider
- ETH Zurich , Department of Chemistry and Applied Biosciences , Vladimir-Prelog-Weg 4 , CH-8093 Zurich , Switzerland
| | - Shengyong Yang
- State Key Laboratory of Biotherapy and Cancer Center, West China Hospital , Sichuan University , Chengdu , Sichuan 610041 , China
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13
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Stephenson N, Shane E, Chase J, Rowland J, Ries D, Justice N, Zhang J, Chan L, Cao R. Survey of Machine Learning Techniques in Drug Discovery. Curr Drug Metab 2019; 20:185-193. [DOI: 10.2174/1389200219666180820112457] [Citation(s) in RCA: 111] [Impact Index Per Article: 22.2] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/06/2017] [Revised: 01/01/2018] [Accepted: 03/19/2018] [Indexed: 12/19/2022]
Abstract
Background:Drug discovery, which is the process of discovering new candidate medications, is very important for pharmaceutical industries. At its current stage, discovering new drugs is still a very expensive and time-consuming process, requiring Phases I, II and III for clinical trials. Recently, machine learning techniques in Artificial Intelligence (AI), especially the deep learning techniques which allow a computational model to generate multiple layers, have been widely applied and achieved state-of-the-art performance in different fields, such as speech recognition, image classification, bioinformatics, etc. One very important application of these AI techniques is in the field of drug discovery.Methods:We did a large-scale literature search on existing scientific websites (e.g, ScienceDirect, Arxiv) and startup companies to understand current status of machine learning techniques in drug discovery.Results:Our experiments demonstrated that there are different patterns in machine learning fields and drug discovery fields. For example, keywords like prediction, brain, discovery, and treatment are usually in drug discovery fields. Also, the total number of papers published in drug discovery fields with machine learning techniques is increasing every year.Conclusion:The main focus of this survey is to understand the current status of machine learning techniques in the drug discovery field within both academic and industrial settings, and discuss its potential future applications. Several interesting patterns for machine learning techniques in drug discovery fields are discussed in this survey.
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Affiliation(s)
- Natalie Stephenson
- Department of Computer Science, Pacific Lutheran University, Tacoma, WA 98447, United States
| | - Emily Shane
- Department of Computer Science, Pacific Lutheran University, Tacoma, WA 98447, United States
| | - Jessica Chase
- Department of Computer Science, Pacific Lutheran University, Tacoma, WA 98447, United States
| | - Jason Rowland
- Department of Computer Science, Pacific Lutheran University, Tacoma, WA 98447, United States
| | - David Ries
- Department of Computer Science, Pacific Lutheran University, Tacoma, WA 98447, United States
| | - Nicola Justice
- Department of Mathematics, Pacific Lutheran University, Tacoma, WA 98447, United States
| | - Jie Zhang
- Key Laboratory of Hebei Province for Plant Physiology and Molecular Pathology, College of Life Sciences, Hebei Agricultural University, Baoding, China
| | - Leong Chan
- School of Business, Pacific Lutheran University, Tacoma, WA 98447, United States
| | - Renzhi Cao
- Department of Computer Science, Pacific Lutheran University, Tacoma, WA 98447, United States
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14
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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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15
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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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16
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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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17
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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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18
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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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19
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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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20
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Clark AM, Dole K, Ekins S. Open Source Bayesian Models. 3. Composite Models for Prediction of Binned Responses. J Chem Inf Model 2016; 56:275-85. [PMID: 26750305 PMCID: PMC4764945 DOI: 10.1021/acs.jcim.5b00555] [Citation(s) in RCA: 13] [Impact Index Per Article: 1.6] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/07/2023]
Abstract
![]()
Bayesian models constructed from
structure-derived fingerprints
have been a popular and useful method for drug discovery research
when applied to bioactivity measurements that can be effectively classified
as active or inactive. The results can be used to rank candidate structures
according to their probability of activity, and this ranking benefits
from the high degree of interpretability when structure-based fingerprints
are used, making the results chemically intuitive. Besides selecting
an activity threshold, building a Bayesian model is fast and requires
few or no parameters or user intervention. The method also does not
suffer from such acute overtraining problems as quantitative structure–activity
relationships or quantitative structure–property relationships
(QSAR/QSPR). This makes it an approach highly suitable for automated
workflows that are independent of user expertise or prior knowledge
of the training data. We now describe a new method for creating a
composite group of Bayesian models to extend the method to work with
multiple states, rather than just binary. Incoming activities are
divided into bins, each covering a mutually exclusive range of activities.
For each of these bins, a Bayesian model is created to model whether
or not the compound belongs in the bin. Analyzing putative molecules
using the composite model involves making a prediction for each bin
and examining the relative likelihood for each assignment, for example,
highest value wins. The method has been evaluated on a collection
of hundreds of data sets extracted from ChEMBL v20 and validated data
sets for ADME/Tox and bioactivity.
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Affiliation(s)
- Alex M Clark
- Molecular Materials Informatics, Inc. , 1900 St. Jacques #302, Montreal H3J 2S1, Quebec, Canada
| | - Krishna Dole
- Collaborative Drug Discovery, Inc. , 1633 Bayshore Highway, Suite 342, Burlingame, California 94010, United States
| | - Sean Ekins
- Collaborative Drug Discovery, Inc. , 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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21
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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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22
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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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23
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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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24
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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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25
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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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26
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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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27
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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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Abstract
The current Ebola virus epidemic may provide some suggestions of how we can better prepare for the next pathogen outbreak. We propose several cost effective steps that could be taken that would impact the discovery and use of small molecule therapeutics including: 1. text mine the literature, 2. patent assignees and/or inventors should openly declare their relevant filings, 3. reagents and assays could be commoditized, 4. using manual curation to enhance database links, 5. engage database and curation teams, 6. consider open science approaches, 7. adapt the "box" model for shareable reference compounds, and 8. involve the physician's perspective.
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Affiliation(s)
- 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
| | - Christopher Southan
- IUPHAR/BPS Guide to PHARMACOLOGY, Centre for Integrative Physiology, University of Edinburgh, Hugh Robson Building, Edinburgh, EH8 9XD, UK
| | - Megan Coffee
- Center for Infectious Diseases and Emergency Readiness, University of California at Berkeley, 1918 University Ave, Berkeley, CA, 94704, USA
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Abstract
The current Ebola virus epidemic may provide some suggestions of how we can better prepare for the next pathogen outbreak. We propose several cost effective steps that could be taken that would impact the discovery and use of small molecule therapeutics including: 1. text mine the literature, 2. patent assignees and/or inventors should openly declare their relevant filings, 3. reagents and assays could be commoditized, 4. using manual curation to enhance database links, 5. engage database and curation teams, 6. consider open science approaches, 7. adapt the "box" model for shareable reference compounds, and 8. involve the physician's perspective.
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Affiliation(s)
- 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
| | - Christopher Southan
- IUPHAR/BPS Guide to PHARMACOLOGY, Centre for Integrative Physiology, University of Edinburgh, Hugh Robson Building, Edinburgh, EH8 9XD, UK
| | - Megan Coffee
- Center for Infectious Diseases and Emergency Readiness, University of California at Berkeley, 1918 University Ave, Berkeley, CA, 94704, USA
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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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