This article has just been published in Nature Reviews Drug Discovery, ‘Artificial intelligence in drug discovery — what it is, where we stand and the path forward’, which may be of interest to people here: https://preview.redd.it/71unav5udzhh1.png?width=953&format=png&auto=webp&s=bc6c84671ea3c3ee618dbedadc6a09f4a97d7e62 The URL of the article is as follows: https://www.nature.com/articles/s41573-026-01496-2 and there is free read access available via https://rdcu.be/fyr77 From the abstract: “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.” submitted by /u/Sufficient_Okra_2919
Originally posted by u/Sufficient_Okra_2919 on r/ArtificialInteligence