期刊
EXPERT OPINION ON DRUG DISCOVERY
卷 17, 期 1, 页码 71-78出版社
TAYLOR & FRANCIS LTD
DOI: 10.1080/17460441.2021.1979514
关键词
Artificial intelligence; drug discovery; structure-based virtual screening; molecular docking; machine learning
The implementation of AI methodologies in drug discovery is increasing, particularly in structure-based virtual screening. AI methods provide alternative frameworks for ligand identification and de novo ligand design. Future trends include more structure-based generative models and standard guidelines for validating generated structures.
Introduction The implementation of Artificial Intelligence (AI) methodologies to drug discovery (DD) are on the rise. Several applications have been developed for structure-based DD, where AI methods provide an alternative framework for the identification of ligands for validated therapeutic targets, as well as the de novo design of ligands through generative models. Areas covered Herein, the authors review the contributions between the 2019 to present period regarding the application of AI methods to structure-based virtual screening (SBVS) which encompasses mainly molecular docking applications - binding pose prediction and binary classification for ligand or hit identification-, as well as de novo drug design driven by machine learning (ML) generative models, and the validation of AI models in structure-based screening. Studies are reviewed in terms of their main objective, used databases, implemented methodology, input and output, and key results . Expert opinion More profound analyses regarding the validity and applicability of AI methods in DD have begun to appear. In the near future, we expect to see more structure-based generative models- which are scarce in comparison to ligand-based generative models-, the implementation of standard guidelines for validating the generated structures, and more analyses regarding the validation of AI methods in structure-based DD.
作者
我是这篇论文的作者
点击您的名字以认领此论文并将其添加到您的个人资料中。
推荐
暂无数据