The predictive performance of classical scoring functions (SFs) seems to have reached a
plateau. Currently, SFs relying on sophisticated machine learning techniques have shown …

Computer-aided drug discovery and design involve the use of information technologies to
identify and develop, on a rational ground, chemical compounds that align a set of desired …

Abstract Machine learning-based scoring functions (MLSFs) have shown potential for
improving virtual screening capabilities over classical scoring functions (SFs). Due to the …

Abstract Structure-based drug design depends on the detailed knowledge of the three-
dimensional (3D) structures of protein–ligand binding complexes, but accurate prediction of …

The mechanism of transcriptional activation/repression of the nuclear receptors (NRs)
involves two main conformations of the NR protein, namely, the active (agonistic) and …

Prediction of protein–ligand binding affinities is a central issue in structure-based computer-
aided drug design. In recent years, much effort has been devoted to the prediction of the …

There is an urgent need to identify new therapies that prevent SARS-CoV-2 infection and
improve the outcome of COVID-19 patients. This pandemic has thus spurred intensive …

Cracking the entangling code of protein–ligand interaction (PLI) is of great importance to
structure-based drug design and discovery. Different physical and biochemical …

본 리뷰 논문은 2010 년부터 2022 년까지의 인공지능을 활용한 신약개발 관련 연구동향을
분석하여 정리하였다. 이러한 분석을 통해 2,421 개 연구의 초록을 코퍼스로 구성하고 …

This review analyzes research trends related to new drug development using artificial
intelligence from 2010 to 2022. This analysis organized the abstracts of 2,421 studies into a …