RBVS: Database of the Receptor-Based Virtual Screening
Senbiao Fang, Huimin Zhu, Yongfan Ming, Kunying Niu, Baoying Zhao, Min Li · 2024
In pharmaceutical arena, various computational methods especially the receptor-based virtual screening has played an important role in improving the success ratio of drug discovery. Numerous researches have been reported from 1990 to 2024. Comprehensively understanding of the influence of receptor-based virtual screening on drug discovery would greatly expedite the drug development process. Integrative information encompassing all related works can make significant help for researchers in pharmaceutical, biological, medicine, and computational biology fields. However, it is difficult to obtain the diverse data needed for integrative research from thousands of existing works. In this research, by extracting information from 1319 literature publications across 114 journals, we comprehensively charted the landscape of all existing receptor-based virtual screening works, encompassing 947 targets covering 283 diseases along with 9991 ligands. One complete network map including target proteins, "disease-target" nodes association, ligands, docking software and molecular characteristics, etc., were compiled to the database Receptor-Based Virtual Screening (RBVS, https://www.csuligroup.com/protein). Based on the RBVS database, researchers can (1) obtain one integrated 'disease-target' network map to timely identify the hottest targets associating with studied disease; (2) concentrate on more valuable and comprehensive information for target protein from different researching perspectives, and conduct more accurate and effective virtual screening strategies to reduce the time and experiment costs; (3) understand the researching trends of hot drug targets to identify more potential drug candidates; (4) improve the success ratio of yielding more effective drugs with more structural diversity and mother nuclear structure; (5) obtain more effective guidance for optimization of lead compounds to make useful guidance for subsequent pre-clinical experiments; (6) the accumulated data can serve as core fundamental data, facilitating researches in the artificial intelligence (AI) field.