Applications of machine learning in drug discovery and development

Autoren

Jessica Vamathevan
Dominic Clark
Paul Czodrowski
Ian Dunham
Edgardo Ferran
George Lee
Bin Li
Anant Madabhushi
Parantu Shah
Michaela Spitzer
Shanrong Zhao

DOI

10.1038/s41573-019-0024-5

eISSN

1474-1784

ISSN

1474-1776

Ausgabe der Veröffentlichung

6

Zeitschrift

Nature Reviews Drug Discovery

Sprache

en

Online publication date

2019

Paginierung

463 - 477

Datum der Veröffentlichung

2019

Status

Published

Herausgeber

Springer Science and Business Media LLC

Herausgeber URL

http://dx.doi.org/10.1038/s41573-019-0024-5

Datum der Datenerfassung

2023

Titel

Applications of machine learning in drug discovery and development

Ausgabe der Zeitschrift

18

Datenquelle: Crossref

Abstract

Drug discovery and development pipelines are long, complex and depend on numerous factors. Machine learning (ML) approaches provide a set of tools that can improve discovery and decision making for well-specified questions with abundant, high-quality data. Opportunities to apply ML occur in all stages of drug discovery. Examples include target validation, identification of prognostic biomarkers and analysis of digital pathology data in clinical trials. Applications have ranged in context and methodology, with some approaches yielding accurate predictions and insights. The challenges of applying ML lie primarily with the lack of interpretability and repeatability of ML-generated results, which may limit their application. In all areas, systematic and comprehensive high-dimensional data still need to be generated. With ongoing efforts to tackle these issues, as well as increasing awareness of the factors needed to validate ML approaches, the application of ML can promote data-driven decision making and has the potential to speed up the process and reduce failure rates in drug discovery and development.

Addresses

European Molecular Biology Laboratory, European Bioinformatics Institute, Cambridge, UK. jessicav@ebi.ac.uk.

Autoren

Jessica Vamathevan
Dominic Clark
Paul Czodrowski
Ian Dunham
Edgardo Ferran
George Lee
Bin Li
Anant Madabhushi
Parantu Shah
Michaela Spitzer
Shanrong Zhao

DOI

10.1038/s41573-019-0024-5

eISSN

1474-1784

Externe Identifier

PubMed Identifier: 30976107
PubMed Central ID: PMC6552674

Funding acknowledgements

NCI NIH HHS: R01 CA216579
NCI NIH HHS: U24 CA199374
NCI NIH HHS: R01 CA202752
NCI NIH HHS: R01 CA220581
BLRD VA: I01 BX004121
NCI NIH HHS: R01 CA208236

Open access

false

ISSN

1474-1776

Ausgabe der Veröffentlichung

6

Zeitschrift

Nature reviews. Drug discovery

Schlüsselwörter

Animals
Humans
Drug Design
Drug Discovery
Machine Learning
Neural Networks, Computer

Sprache

eng

Medium

Print

Paginierung

463 - 477

Datum der Veröffentlichung

2019

Status

Published

Datum der Datenerfassung

2019

Titel

Applications of machine learning in drug discovery and development.

Sub types

research-article
Review
Journal Article

Ausgabe der Zeitschrift

18

Files

https://europepmc.org/articles/pmc6552674?pdf=render

Datenquelle: Europe PubMed Central

Abstract

Drug discovery and development pipelines are long, complex and depend on numerous factors. Machine learning (ML) approaches provide a set of tools that can improve discovery and decision making for well-specified questions with abundant, high-quality data. Opportunities to apply ML occur in all stages of drug discovery. Examples include target validation, identification of prognostic biomarkers and analysis of digital pathology data in clinical trials. Applications have ranged in context and methodology, with some approaches yielding accurate predictions and insights. The challenges of applying ML lie primarily with the lack of interpretability and repeatability of ML-generated results, which may limit their application. In all areas, systematic and comprehensive high-dimensional data still need to be generated. With ongoing efforts to tackle these issues, as well as increasing awareness of the factors needed to validate ML approaches, the application of ML can promote data-driven decision making and has the potential to speed up the process and reduce failure rates in drug discovery and development.

Autoren

Jessica Vamathevan
Dominic Clark
Paul Czodrowski
Ian Dunham
Edgardo Ferran
George Lee
Bin Li
Anant Madabhushi
Parantu Shah
Michaela Spitzer
Shanrong Zhao

Autoren-URL

https://www.ncbi.nlm.nih.gov/pubmed/30976107

DOI

10.1038/s41573-019-0024-5

eISSN

1474-1784

Externe Identifier

NIH Manuscript Submission ID: NIHMS1029624
PubMed Central ID: PMC6552674

Funding acknowledgements

NCI NIH HHS: R01 CA216579
NCI NIH HHS: R01 CA202752
NCI NIH HHS: R01 CA208236
NCI NIH HHS: U24 CA199374
BLRD VA: I01 BX004121
NCI NIH HHS: R01 CA220581

Ausgabe der Veröffentlichung

6

Zeitschrift

Nat Rev Drug Discov

Schlüsselwörter

Animals
Drug Design
Drug Discovery
Humans
Machine Learning
Neural Networks, Computer

Sprache

eng

Country

England

Paginierung

463 - 477

PII

10.1038/s41573-019-0024-5

Datum der Veröffentlichung

2019

Status

Published

Datum, an dem der Datensatz öffentlich gemacht wurde

2019

Titel

Applications of machine learning in drug discovery and development.

Sub types

Journal Article
Review

Ausgabe der Zeitschrift

18

Datenquelle: PubMed

Applications of machine learning in drug discovery and development

Files

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