AI-driven antibody design with generative diffusion models:current insights and future directions  

作　　者：Xin-heng He Jun-rui Li James Xu Hong Shan Shi-yi Shen Si-han Gao H.Eric Xu 

机构地区：[1]State Key Laboratory of Drug Research and CAS Key Laboratory of Receptor Research,Shanghai Institute of Materia Medica,Chinese Academy of Sciences,Shanghai,201203,China [2]University of Chinese Academy of Sciences,Beijing,100049,China [3]Cascade Pharma,Shanghai,201318,China [4]School of Pharmacy,Fudan University,Shanghai,201203,China

出　　处：《Acta Pharmacologica Sinica》2025年第3期565-574,共10页中国药理学报(英文版)

基　　金：supported by the Lingang Laboratory,Grant No.LG-GG-202204-01;the National Natural Science Foundation(82121005,32130022);CAS Strategic Priority Research Program(XDB37030103);Shanghai Municipal Science and Technology Major Project(2019SHZDZX02).

摘　　要：Therapeutic antibodies are at the forefront of biotherapeutics,valued for their high target specificity and binding affinity.Despite their potential,optimizing antibodies for superior efficacy presents significant challenges in both monetary and time costs.Recent strides in computational and artificial intelligence(AI),especially generative diffusion models,have begun to address these challenges,offering novel approaches for antibody design.This review delves into specific diffusion-based generative methodologies tailored for antibody design tasks,de novo antibody design,and optimization of complementarity-determining region(CDR)loops,along with their evaluation metrics.We aim to provide an exhaustive overview of this burgeoning field,making it an essential resource for leveraging diffusion-based generative models in antibody design endeavors.

关 键 词：ANTIBODIES generative model diffusion de novo antibody design CDR optimization model evaluation 

分 类 号：TP18[自动化与计算机技术—控制理论与控制工程] R392[自动化与计算机技术—控制科学与工程]