Combined usage of ligand- and structure-based virtual screening in the artificial intelligence era

Drug design has always been pursuing techniques with time- and cost-benefits. Virtual screening, generally classified as ligand-based (LBVS) and structure-based (SBVS) approaches, could identify active compounds in the large chemical library to reduce time and cost. Owing to the intrinsic flaws and complementary nature of both approaches, continued efforts have been made to combine them to mitigate limitations. Meanwhile, the emergence of machine learning (ML) endows them with opportunities to leverage vast amounts of data to improve their defects. However, few discussions on how to merge ML-improved LBVS and SBVS have been conducted. Therefore, this review provides insights into combined usage of ML-improved LBVS and SBVS to enlighten medicinal chemists to utilize these joint strategies to lift the screening efficiency as well as AI professionals to design novel techniques.