Graduate School of Pharmaceutical Sciences, Osaka University.
Chem Pharm Bull (Tokyo). 2024;72(9):781-786. doi: 10.1248/cpb.c23-00839.
Owing to the increasing use of computers, computer-aided drug design (CADD) has become an essential component of drug discovery research. In structure-based drug design (SBDD), including inhibitor design and in silico screening of drug target molecules, concordance with wet experimental data is important to provide insights on unique perspectives derived from calculations. Fragment molecular orbital (FMO) method is a quantum chemical method that facilitates precise energy calculations. Fragmentation method makes it possible to apply the quantum chemical method to biological macromolecules for energy calculation based on the electron behavior. Furthermore, interaction energies calculated on a residue-by-residue basis via fragmentation aid in the analysis of interactions between the target and ligand molecule residues and molecular design. In this review, we outline the recent developments in SBDD and FMO methods and highlight the prospects of developing machine learning approaches for large computational data using the FMO method.
由于计算机的广泛应用,计算机辅助药物设计(CADD)已成为药物发现研究的重要组成部分。在基于结构的药物设计(SBDD)中,包括抑制剂设计和药物靶标分子的计算机筛选,与湿实验数据的一致性对于提供从计算中得出的独特视角的见解很重要。片段分子轨道(FMO)方法是一种量子化学方法,可促进精确的能量计算。片段化方法使得可以基于电子行为将量子化学方法应用于生物大分子以进行能量计算。此外,通过片段化逐残基计算的相互作用能有助于分析靶标和配体分子残基之间的相互作用以及分子设计。在这篇综述中,我们概述了 SBDD 和 FMO 方法的最新进展,并强调了使用 FMO 方法开发机器学习方法来处理大型计算数据的前景。