.png){kind=icon}
.png){kind=icon}
Return
Artificial intelligence in drug discovery — what it is, where we stand and the path forward
.png){kind=icon}
PRE
AI
A
M
J
D
G
J
L
R
K
S
M
M
T
F
I
DOI：10.1038/s41573-026-01496-2.png)
.png){kind=icon}
Original request for help.png){kind=icon}
Share.png){kind=icon}
Save
Abstract
En 中文
Artificial intelligence (AI) in drug discovery has attracted increasing interest over the past decade. It is now time for a critical review of progress in the field: where did we advance — and where are we yet to see impact — when it comes to what matters in drug discovery, which is to deliver safer and more efficacious medicines to patients faster? Although a wide variety of AI methods have been developed, applied and benchmarked, evidence of their clinically relevant impact is, so far, disappointingly limited. In this Perspective we discuss potential reasons, including an insufficient focus on clinical translation during model development, difficulties with applying AI algorithms on conditional life science data, and insufficient problem definitions and the resulting underspecification of computational models for real-world use cases. ‘Technology push’ compared with ‘science pull’ is also likely to be an underlying factor, as well as the substantial time required to operationalize technical capabilities into systems that are sufficiently scaled and accessible for users. We provide recommendations for the development of AI in drug discovery with the aim of increasing its translational relevance. For example, benchmarking studies of AI tools in drug discovery need to move on from model validation and instead focus on their ability to improve decision making. Applications of artificial intelligence (AI) in drug discovery have attracted high interest in recent years, but evidence for clinically relevant impact so far is limited. This Perspective discusses potential reasons, including an insufficient focus on clinical translation during model development and difficulties with applying AI algorithms on conditional life science data, and provides recommendations for the development of AI in drug discovery.
Journal
IF:
101.8
Papers:
5.1K
Citations:
5.3W
Organization
Cited Papers