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Exploiting machine learning for end-to-end drug discovery and development

Nature materials, 2019-05, Vol.18 (5), p.435-441

2019

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Assessment of Substrate-Dependent Ligand Interactions at the Organic Cation Transporter OCT2 Using Six Model Substrates

Molecular pharmacology, 2018-09, Vol.94 (3), p.1057-1068

2018

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Ebola Virus Bayesian Machine Learning Models Enable New in Vitro Leads

ACS omega, 2019-01, Vol.4 (1), p.2353-2361

2019

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Open Source Bayesian Models. 2. Mining a “Big Dataset” To Create and Validate Models with ChEMBL

Journal of chemical information and modeling, 2015-06, Vol.55 (6), p.1246-1260

2015

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Capturing mixture composition: an open machine-readable format for representing mixed substances

Journal of cheminformatics, 2019-05, Vol.11 (1), p.33-17, Article 33

2019

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Machine learning models identify molecules active against the Ebola virus in vitro [version 2; peer review: 2 approved]

F1000 research, 2016, Vol.4, p.1091

2016

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Bigger data, collaborative tools and the future of predictive drug discovery

Journal of computer-aided molecular design, 2014-10, Vol.28 (10), p.997-1008

2014

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Comparing Multiple Machine Learning Algorithms and Metrics for Estrogen Receptor Binding Prediction

Molecular pharmaceutics, 2018-10, Vol.15 (10), p.4361-4370

2018

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Machine learning models identify molecules active against the Ebola virus in vitro [version 3; peer review: 2 approved]

F1000 research, 2017, Vol.4, p.1091-1091

2017

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Mobile apps for chemistry in the world of drug discovery

Drug discovery today, 2011-11, Vol.16 (21), p.928-939

2011

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Using Machine Learning to Parse Chemical Mixture Descriptions

ACS omega, 2021-08, Vol.6 (34), p.22400-22409

2021

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The Next Frontier of Environmental Unknowns: Substances of Unknown or Variable Composition, Complex Reaction Products, or Biological Materials (UVCBs)

Environmental science & technology, 2022-06, Vol.56 (12), p.7448-7466

2022

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Open Drug Discovery Teams: A Chemistry Mobile App for Collaboration

Molecular informatics, 2012-08, Vol.31 (8), p.585-597

2012

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Multiple Machine Learning Comparisons of HIV Cell-based and Reverse Transcriptase Data Sets

Molecular pharmaceutics, 2019-04, Vol.16 (4), p.1620-1632

2019

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CATMoS: Collaborative Acute Toxicity Modeling Suite

Environmental health perspectives, 2021-04, Vol.129 (4), p.47013

2021

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2D Depiction of Protein−Ligand Complexes

Journal of chemical information and modeling, 2007-09, Vol.47 (5), p.1933-1944

2007

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Comparing and Validating Machine Learning Models for Mycobacterium tuberculosis Drug Discovery

Molecular pharmaceutics, 2018-10, Vol.15 (10), p.4346-4360

2018

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Open Source Bayesian Models. 3. Composite Models for Prediction of Binned Responses

Journal of chemical information and modeling, 2016-02, Vol.56 (2), p.275-285

2016

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Basic primitives for molecular diagram sketching

Journal of cheminformatics, 2010-10, Vol.2 (1), p.8-8, Article 8

Clark, Alex M

2010

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Open Source Bayesian Models. 1. Application to ADME/Tox and Drug Discovery Datasets

Journal of chemical information and modeling, 2015-06, Vol.55 (6), p.1231-1245

2015

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Understanding the patient experience of health care–associated infection: A qualitative systematic review

American journal of infection control, 2018-08, Vol.46 (8), p.936-942

2018

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Accurate Specification of Molecular Structures: The Case for Zero-Order Bonds and Explicit Hydrogen Counting

Journal of chemical information and modeling, 2011-12, Vol.51 (12), p.3149-3157

Clark, Alex M

2011

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High-throughput screening and Bayesian machine learning for copper-dependent inhibitors of Staphylococcus aureus

Metallomics, 2019-03, Vol.11 (3), p.696

2019

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Collaborative drug discovery for More Medicines for Tuberculosis (MM4TB)

Drug discovery today, 2017-03, Vol.22 (3), p.555-565

2017

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Detection and Assignment of Common Scaffolds in Project Databases of Lead Molecules

Journal of medicinal chemistry, 2009-01, Vol.52 (2), p.469-483

2009

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Exploiting machine learning for end-to-end drug discovery and development

Assessment of Substrate-Dependent Ligand Interactions at the Organic Cation Transporter OCT2 Using Six Model Substrates

Ebola Virus Bayesian Machine Learning Models Enable New in Vitro Leads

Open Source Bayesian Models. 2. Mining a “Big Dataset” To Create and Validate Models with ChEMBL

Capturing mixture composition: an open machine-readable format for representing mixed substances

Machine learning models identify molecules active against the Ebola virus in vitro [version 2; peer review: 2 approved]

Bigger data, collaborative tools and the future of predictive drug discovery

Comparing Multiple Machine Learning Algorithms and Metrics for Estrogen Receptor Binding Prediction

Machine learning models identify molecules active against the Ebola virus in vitro [version 3; peer review: 2 approved]

Mobile apps for chemistry in the world of drug discovery

Using Machine Learning to Parse Chemical Mixture Descriptions

The Next Frontier of Environmental Unknowns: Substances of Unknown or Variable Composition, Complex Reaction Products, or Biological Materials (UVCBs)

Open Drug Discovery Teams: A Chemistry Mobile App for Collaboration

Multiple Machine Learning Comparisons of HIV Cell-based and Reverse Transcriptase Data Sets

CATMoS: Collaborative Acute Toxicity Modeling Suite

2D Depiction of Protein−Ligand Complexes

Comparing and Validating Machine Learning Models for Mycobacterium tuberculosis Drug Discovery

Open Source Bayesian Models. 3. Composite Models for Prediction of Binned Responses

Basic primitives for molecular diagram sketching

Open Source Bayesian Models. 1. Application to ADME/Tox and Drug Discovery Datasets

Understanding the patient experience of health care–associated infection: A qualitative systematic review

Accurate Specification of Molecular Structures: The Case for Zero-Order Bonds and Explicit Hydrogen Counting

High-throughput screening and Bayesian machine learning for copper-dependent inhibitors of Staphylococcus aureus

Collaborative drug discovery for More Medicines for Tuberculosis (MM4TB)

Detection and Assignment of Common Scaffolds in Project Databases of Lead Molecules

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