虚拟筛选
对接(动物)
计算生物学
计算机科学
分数
堆积
机器学习
药物发现
生物信息学
人工智能
化学
生物
医学
护理部
有机化学
作者
Xiaolin Pan,Hao Wang,Yueqing Zhang,Xingyu Wang,Cuiyu Li,Changge Ji,John Z. H. Zhang
标识
DOI:10.1021/acs.jcim.1c01537
摘要
The protein–ligand scoring function plays an important role in computer-aided drug discovery and is heavily used in virtual screening and lead optimization. In this study, we developed a new empirical protein–ligand scoring function with amino acid-specific interaction components for hydrogen bond, van der Waals, and electrostatic interactions. In addition, hydrophobic, π-stacking, π-cation, and metal–ligand interactions are also included in the new scoring function. To better evaluate the performance of the AA-Score, we generated several new test sets for evaluation of scoring, ranking, and docking performances, respectively. Extensive tests show that AA-Score performs well on scoring, docking, and ranking as compared to other widely used traditional scoring functions. The performance improvement of AA-Score benefits from the decomposition of individual interaction into amino acid-specific types. To facilitate applications, we developed an easy-to-use tool to analyze protein–ligand interaction fingerprint and predict binding affinity using the AA-Score. The source code and associated running examples can be found at https://github.com/xundrug/AA-Score-Tool.
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