AI-driven antibody design with generative diffusion models: current insights and future directions
文献类型：期刊论文
| 作者 | He, Xin-heng3,4,5; Li, Jun-rui4,5; Xu, James2; Shan, Hong4,5; Shen, Shi-yi3,4,5; Gao, Si-han1; Xu, H. Eric2,3,4,5
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| 刊名 | ACTA PHARMACOLOGICA SINICA
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| 出版日期 | 2024-09-30 |
| 页码 | 10 |
| 关键词 | antibodies generative model diffusion de novo antibody design CDR optimization model evaluation |
| ISSN号 | 1671-4083 |
| DOI | 10.1038/s41401-024-01380-y |
| 通讯作者 | Xu, H. Eric(eric.xu@simm.ac.cn) |
| 英文摘要 | 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. |
| WOS关键词 | SIMILARITY |
| 资助项目 | Lingang Laboratory[LG-GG-202204-01] ; National Natural Science Foundation[82121005] ; National Natural Science Foundation[32130022] ; CAS Strategic Priority Research Program[XDB37030103] ; Shanghai Municipal Science and Technology Major Project[2019SHZDZX02] |
| WOS研究方向 | Chemistry ; Pharmacology & Pharmacy |
| 语种 | 英语 |
| WOS记录号 | WOS:001325456200001 |
| 出版者 | NATURE PUBL GROUP |
| 源URL | [http://119.78.100.183/handle/2S10ELR8/313624]  |
| 专题 | 新药研究国家重点实验室 |
| 通讯作者 | Xu, H. Eric |
| 作者单位 | 1.Fudan Univ, Sch Pharm, Shanghai 201203, Peoples R China 2.Cascade Pharm, Shanghai 201318, Peoples R China 3.Univ Chinese Acad Sci, Beijing 100049, Peoples R China 4.Chinese Acad Sci, Shanghai Inst Mat Med, CAS Key Lab Receptor Res, Shanghai 201203, Peoples R China 5.Chinese Acad Sci, State Key Lab Drug Res, Shanghai Inst Mat Med, Shanghai 201203, Peoples R China |
| 推荐引用方式 GB/T 7714 | He, Xin-heng,Li, Jun-rui,Xu, James,et al. AI-driven antibody design with generative diffusion models: current insights and future directions[J]. ACTA PHARMACOLOGICA SINICA,2024:10. |
| APA | He, Xin-heng.,Li, Jun-rui.,Xu, James.,Shan, Hong.,Shen, Shi-yi.,...&Xu, H. Eric.(2024).AI-driven antibody design with generative diffusion models: current insights and future directions.ACTA PHARMACOLOGICA SINICA,10. |
| MLA | He, Xin-heng,et al."AI-driven antibody design with generative diffusion models: current insights and future directions".ACTA PHARMACOLOGICA SINICA (2024):10. |
入库方式： OAI收割
来源：上海药物研究所
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