Plug-in Models:A Promising Direction for Molecular Generation  

作　　者：Ningfeng Liu Hongwei Jin Liangren Zhang Zhenming Liu 

机构地区：[1]State Key Laboratory of Natural and Biomimetic Drugs,School of Pharmaceutical Sciences,PekingUniversity,100191 Beijing,P.R.China.

出　　处：《Health Data Science》2023年第1期87-89,共3页健康数据科学（英文）

基　　金：the“AI+Health Collaborative Innovation Cultivation”Project(grant number Z2211-00003522022);the National Key Research and Development Program(grant number 2022YFF1203003);the Peking University Medicine-StoneWise Joint Laboratory Project(grant numbers L202107).

摘　　要：Molecular Generation The molecular generation has emerged as a powerful tool for computer-aided drug design in recent years,as it can explore a large and unknown chemical space and discover novel structures or scaffolds.Furthermore,a candidate compound needs to satisfy multiple criteria,such as target affinity,pharmacokinetics,toxicity,synthetic accessibility,etc.,to pass clinical trials and meet industrial standards.Therefore,multi-objective methods have become a focal point of molecular generation and optimization.Several reviews have been published recently to summarize previous works in molecular generation and categorize them(Table).In this article,we propose a classification scheme based on both the model’s architecture and its practical use,namely,entrenched or plug-in models,especially for multi-objective molecular generation models.We argue that plug-in methods have superior flexibility in both model building and practical use,broader application potential,and higher performance boundaries,and they deserve more attention in the future.

关 键 词：optimization TRENCH MOLECULAR 

分 类 号：TP39[自动化与计算机技术—计算机应用技术]