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Applications of machine learning in drug discovery and development

Published in Nature reviews. Drug discovery (2019-04-11)
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  • Abstract
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TL

TL;DR

The most useful techniques and how machine learning can promote data-driven decision making in drug discovery and development are discussed and major hurdles in the field are highlighted, such as the required data characteristics for applying ML.

Abstract

Authors

J. Vamathevan

1 Paper

Dominic Clark

1 Paper

P. Czodrowski

1 Paper

I. Dunham

1 Paper

Edgardo Ferran

1 Paper

George Lee

1 Paper

Bin Li

1 Paper

A. Madabhushi

1 Paper

Parantu K. Shah

1 Paper

M. Spitzer

1 Paper

Shanrong Zhao

1 Paper

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Research Impact

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Prediction of Druggable Proteins Using Machine Learning and Systems Biology: A Mini-Review

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A systematic approach to identify novel cancer drug targets using machine learning, inhibitor design and high-throughput screening

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113

Why Most Gene Expression Signatures of Tumors Have Not Been Useful in the Clinic

114

Relating protein pharmacology by ligand chemistry

115

This work identifies molecular signatures that are resistant to drug treatments and illustrates a multiomics approach to understanding drug response

116

This article is the first effort to highlight the recent applications of DL in drug discovery research and is an introduction to some popular DL architectures

117

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Bioinformatics — From Genomes to Therapies Ch

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quantification of immune cells from H&E slides and the identification of sub-categories of immune infiltrate as related to therapeutic outcome

Authors

Field of Study

Computer ScienceMedicine

Journal Information

Name

Nature Reviews Drug Discovery

Volume

18

Venue Information

Name

Nature reviews. Drug discovery

Type

journal

URL

https://www.nature.com/nrd/

Alternate Names

  • Nat rev Drug discov
  • Nat Rev Drug Discov
  • Nature Reviews Drug Discovery