期刊
JOURNAL OF CHEMICAL INFORMATION AND MODELING
卷 59, 期 8, 页码 3485-3493出版社
AMER CHEMICAL SOC
DOI: 10.1021/acs.jcim.9b00439
关键词
-
类别
资金
- European Union [739649, 675451]
Fast and accurate molecular force field (FF) parameterization is still an unsolved problem. Accurate FF are not generally available for all molecules, like novel druglike molecules. While methods based on quantum mechanics (QM) exist to parameterize them with better accuracy, they are computationally expensive and slow, which limits applicability to a small number of molecules. Here, we present an automated FF parameterization method which can utilize either density functional theory (DFT) calculations or approximate QM energies produced by different neural network potentials (NNPs), to obtain improved parameters for molecules. We demonstrate that for the case of torchani-ANI-1x NNP, we can parameterize small molecules in a fraction of time compared with an equivalent parameterization using DFT QM calculations while producing more accurate parameters than FF (GAFF2). We expect our method to be of critical importance in computational structure-based drug discovery (SBDD). The current version is available at PlayMolecule (www.playmolecule.org) and implemented in HTMD, allowing to parameterize molecules with different QM and NNP options.
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