Deep learning for low-data drug discovery: Hurdles and opportunities

Deep learning is becoming increasingly relevant in drug discovery, from de novo design to protein structure prediction and synthesis planning. However, it is often challenged by the small data regimes typical of certain drug discovery tasks. In such scenarios, deep learning approaches–which are noto...

Full description

Saved in:
Bibliographic Details
Published inCurrent opinion in structural biology Vol. 86; p. 102818
Main Authors van Tilborg, Derek, Brinkmann, Helena, Criscuolo, Emanuele, Rossen, Luke, Özçelik, Rıza, Grisoni, Francesca
Format Journal Article
LanguageEnglish
Published England Elsevier Ltd 01.06.2024
Subjects
Deep Learning
Drug Discovery - methods
Humans
Proteins - chemistry
Proteins - metabolism
Online AccessGet full text

Cover

More Information
Summary:Deep learning is becoming increasingly relevant in drug discovery, from de novo design to protein structure prediction and synthesis planning. However, it is often challenged by the small data regimes typical of certain drug discovery tasks. In such scenarios, deep learning approaches–which are notoriously ‘data-hungry’–might fail to live up to their promise. Developing novel approaches to leverage the power of deep learning in low-data scenarios is sparking great attention, and future developments are expected to propel the field further. This mini-review provides an overview of recent low-data-learning approaches in drug discovery, analyzing their hurdles and advantages. Finally, we venture to provide a forecast of future research directions in low-data learning for drug discovery.
Bibliography:ObjectType-Article-2
SourceType-Scholarly Journals-1
ObjectType-Feature-3
content type line 23
ObjectType-Review-1
ISSN:0959-440X
1879-033X
DOI:10.1016/j.sbi.2024.102818