The rise of deep learning in drug discovery

Ola Engkvist; Thomas Blaschke; Hongming Chen; Marcus Olivecrona; Yinhai Wang

doi:10.1016/j.drudis.2018.01.039

The rise of deep learning in drug discovery

Расцвет глубокого обучения в разработке лекарственных средств

Ola Engkvist, Thomas Blaschke, Hongming Chen, Marcus Olivecrona, Yinhai Wang

2018-01-31

SCID: 54.1/7ypk8rz9 Discuss with AI

bioactivity predictionde novo molecular designdeep learningdrug discoverysynthesis prediction

Abstract (AI)

Over the past decade, deep learning has achieved remarkable success in various artificial intelligence research areas. Evolved from the previous research on artificial neural networks, this technology has shown superior performance to other machine learning algorithms in areas such as image and voice recognition, natural language processing, among others. The first wave of applications of deep learning in pharmaceutical research has emerged in recent years, and its utility has gone beyond bioactivity predictions and has shown promise in addressing diverse problems in drug discovery. Examples will be discussed covering bioactivity prediction, de novo molecular design, synthesis prediction and biological image analysis.

Key Findings

Deep learning has demonstrated utility in biological image analysis, broadening its role across pharmaceutical research.

Deep learning has emerged as a major approach in drug discovery during the past decade, extending beyond traditional artificial intelligence applications.

Deep learning is being applied to synthesis prediction, supporting the planning or evaluation of chemical routes.

Deep learning shows promise for de novo molecular design, enabling computational approaches to generating novel drug candidates.

Early pharmaceutical applications of deep learning have progressed beyond bioactivity prediction to address diverse drug-discovery challenges.

Research Object

deep learning applications in drug discovery

Research Subject

the utility and performance of deep learning for bioactivity prediction, de novo molecular design, synthesis prediction, and biological image analysis

Publication Details

DOI

10.1016/j.drudis.2018.01.039

Publication Date

2018-01-31

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Cited by

1855

Open access PDF

https://www.sciencedirect.com/science/article/p…

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Author Information

Authors

Ola Engkvist

Thomas Blaschke

Hongming Chen

Marcus Olivecrona

Yinhai Wang

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References available in scid.ai4

XGBoost2016 Detecting Functionality-Specific Vulnerabilities via Retrieving Individual Functionality-Equivalent APIs in Open-Source Repositories2025 Semi-Supervised Classification with Graph Convolutional Networks2016 Neural Message Passing for Quantum Chemistry2017

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