Anesthetic drug discovery with computer-aided drug design and machine learning

Abstract Computer-aided drug design (CADD) has emerged as a highly effective and indispensable tool for streamlining the drug discovery process, leading to significant reductions in cost and time. The integration of CADD with machine learning (ML) and deep learning (DL) technologies further enhances its potential and promises novel advancements in the field. In this article, we provide a review of the computational methods employed in the development of novel anesthetics, outlining their respective advantages and limitations. These techniques have demonstrated their utility across various stages of drug discovery, encompassing the exploration of target-ligand interactions, identification and validation of new binding sites, de novo drug design, evaluation and optimization of absorption, distribution, metabolism, excretion, and toxicity (ADMET) properties in lead compounds, as well as prediction of adverse effects. Through an in-depth exploration of computational approaches and their applications, this article aims to help relevant researchers develop safer and more effective anesthetic drugs.