Improved Method of Structure-Based Virtual Screening via Interaction-Energy-Based Learning
Improved Method of Structure-Based Virtual Screening via Interaction-Energy-Based Learning
复制标题
DOI：
10.1021/acs.jcim.8b00673
复制
发表时间：
2019-02
影响因子：
5.6
通讯作者：
Nobuaki Yasuo;M. Sekijima
中科院分区：
文献类型：
--
作者：
Nobuaki Yasuo;M. Sekijima
在药物开发中，虚拟筛选是一种很有前途的获得新的热门化合物的方法。它的目标是从一个大型化学库中丰富潜在的活性化合物，为进一步的生物实验做准备。然而，目前的虚拟筛选方法的准确性还不够。在这项研究中，我们提出了一种新的虚拟筛选方法--相互作用能量向量相似度得分(Sieve-Score)，其中提取蛋白质-配体相互作用能量来表示机器学习的对接姿势。与其他最先进的虚拟筛选方法相比，Sieve-Score提供了实质性的改进，即其他基于机器学习的评分功能、交互指纹和对接软件，使有用诱饵目录上的结果丰富了1%，增强了(DUD-E)。筛选结果也是人类可解释的，以区分活性和非活性化合物的重要相互作用的形式。源代码可以在https://github.com/sekijima-lab/SIEVE-Score上找到。
Virtual screening is a promising method for obtaining novel hit compounds in drug discovery. It aims to enrich potentially active compounds from a large chemical library for further biological experiments. However, the accuracy of current virtual screening methods is insufficient. In this study, we develop a new virtual screening method named Similarity of Interaction Energy VEctor Score (SIEVE-Score), in which protein-ligand interaction energies are extracted to represent docking poses for machine learning. SIEVE-Score offers substantial improvements compared to other state-of-the-art virtual screening methods, namely, other machine-learning-based scoring functions, interaction fingerprints, and docking software, for the enrichment factor 1% results on the Directory of Useful Decoys, Enhanced (DUD-E). The screening results are also human-interpretable in the form of important interactions for distinguishing between active and inactive compounds. The source code is available at https://github.com/sekijima-lab/SIEVE-Score .