Transforming Computational Drug Discovery with Machine Learning and AI

被引：58

作者：

Smith, Justin S. ^{[1
,2
,3
]}

Roitberg, Adrian E. ^{[1
]}

Isayev, Olexandr ^{[4
]}

机构：

[1] Univ Florida, Dept Chem, Gainesville, FL 32611 USA

[2] Los Alamos Natl Lab, Ctr Nonlinear Studies, Los Alamos, NM 87545 USA

[3] Los Alamos Natl Lab, Theoret Div, Los Alamos, NM 87545 USA

[4] Univ N Carolina, UNC Eshelman Sch Pharm, Chapel Hill, NC 27599 USA

来源：

ACS MEDICINAL CHEMISTRY LETTERS | 2018年 / 9卷 / 11期

基金：

美国国家科学基金会;

关键词：

Deep learning; artificial intelligence; drug discovery; machine learning; molecular potentials; force field; neural network;

D O I：

10.1021/acsmedchemlett.8b00437

中图分类号：

R914 [药物化学];

学科分类号：

100701 ;

摘要：

In this Viewpoint, we discuss the current progress in applications of machine learning (ML) and artificial intelligence (AI) to meet the challenges of computational drug discovery. We identify several areas where existing methods have the potential to accelerate pharmaceutical research and disrupt more traditional approaches.

引用

页码：1065 / 1069

页数：9