Advances in machine intelligence‐driven virtual screening approaches for big‐data

Abstract

Virtual screening (VS) is an integral and ever‐evolving domain of drug discovery framework. The VS is traditionally classified into ligand‐based (LB) and structure‐based (SB) approaches. Machine intelligence or artificial intelligence has wide applications in the drug discovery domain to reduce time and resource consumption. In combination with machine intelligence algorithms, VS has emerged into revolutionarily progressive technology that learns within robust decision orders for data curation and hit molecule screening from large VS libraries in minutes or hours. The exponential growth of chemical and biological data has evolved as “big‐data” in the public domain demands modern and advanced machine intelligence‐driven VS approaches to screen hit molecules from ultra‐large VS libraries. VS has evolved from an individual approach (LB and SB) to integrated LB and SB techniques to explore various ligand and target protein aspects for the enhanced rate of appropriate hit molecule prediction. Current trends demand advanced and intelligent solutions to handle enormous data in drug discovery domain for screening and optimizing hits or lead with fewer or no false positive hits. Following the big‐data drift and tremendous growth in computational architecture, we presented this review. Here, the article categorized and emphasized individual VS techniques, detailed literature presented for machine learning implementation, modern machine intelligence approaches, and limitations and deliberated the future prospects.

The recent advances in the approach of artificial intelligence (AI) towards drug discovery

“…These computational hits are then subjected to in vitro testing. Within the realm of AI drug discovery, virtual screening falls into two primary categories: ligand-based virtual screening (LBVS) (Oliveira et al, 2023) and structure-based virtual screening (SBVS) (Kumar and Acharya, 2023). LBVS entails the analysis of biological data to differentiate inactive compounds from active ones (Dragan et al, 2023).…”