ASFP (Artificial Intelligence based Scoring Function Platform): a web server for the development of customized scoring functions

Journal of Cheminformatics

Xueying Guo¹  Xujun Zhang¹  Gaoqi Weng¹  Gaoang Wang¹  Bo Yang¹  Chao Shen¹  Qing Ye¹  Zhe Wang¹  Qiaojun He¹  Tingjun Hou²  Dongsheng Cao³ 

[1] Hangzhou Institute of Innovative Medicine, College of Pharmaceutical Sciences, Zhejiang University, 310058, Hangzhou, Zhejiang, China;Hangzhou Institute of Innovative Medicine, College of Pharmaceutical Sciences, Zhejiang University, 310058, Hangzhou, Zhejiang, China;State Key Lab of CAD&CG, Zhejiang University, 310058, Hangzhou, Zhejiang, China;Xiangya School of Pharmaceutical Sciences, Central South University, 10013, Changsha, Hunan, China;

关键词: Scoring functions;    Descriptors;    Machine learning;    Virtual screening;   

DOI  :  10.1186/s13321-021-00486-3

来源: Springer

【 摘 要 】

Virtual screening (VS) based on molecular docking has emerged as one of the mainstream technologies of drug discovery due to its low cost and high efficiency. However, the scoring functions (SFs) implemented in most docking programs are not always accurate enough and how to improve their prediction accuracy is still a big challenge. Here, we propose an integrated platform called ASFP, a web server for the development of customized SFs for structure-based VS. There are three main modules in ASFP: (1) the descriptor generation module that can generate up to 3437 descriptors for the modelling of protein–ligand interactions; (2) the AI-based SF construction module that can establish target-specific SFs based on the pre-generated descriptors through three machine learning (ML) techniques; (3) the online prediction module that provides some well-constructed target-specific SFs for VS and an additional generic SF for binding affinity prediction. Our methodology has been validated on several benchmark datasets. The target-specific SFs can achieve an average ROC AUC of 0.973 towards 32 targets and the generic SF can achieve the Pearson correlation coefficient of 0.81 on the PDBbind version 2016 core set. To sum up, the ASFP server is a powerful tool for structure-based VS.

【 授权许可】

CC BY