Recent advances in antibody optimization based on deep learning methods

Ruofan JIN; Ruhong ZHOU; Dong ZHANG

doi:10.1631/jzus.B2400387

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Review | Updated：2025-05-28

Recent advances in antibody optimization based on deep learning methods
Recent advances in antibody optimization based on deep learning methods
基于深度学习方法的抗体优化研究进展
Journal of Zhejiang University-SCIENCE B (Biomedicine & Biotechnology) Vol. 26, Issue 5, Pages: 409-420(2025)
Affiliations：

1.Institute of Quantitative Biology, College of Life Sciences, Zhejiang University, Hangzhou 310058, China
2.Department of Hepatobiliary and Pancreatic Surgery, The First Affiliated Hospital, School of Medicine, Zhejiang University, Hangzhou 310058, China
Funds：
DOI：10.1631/jzus.B2400387
CLC：
CSTR：
Received：24 July 2024，

Revised：09 November 2024，

Published：28 May 2025
Accepted：

金若凡,周如鸿,张冬.基于深度学习方法的抗体优化研究进展[J].浙江大学学报（英文版）（B辑：生物医学和生物技术）,2025,26(05):409-420.

Ruofan JIN, Ruhong ZHOU, Dong ZHANG. Recent advances in antibody optimization based on deep learning methods[J]. Journal of zhejiang university-science b (biomedicine & biotechnology), 2025, 26(5): 409-420.
金若凡,周如鸿,张冬.基于深度学习方法的抗体优化研究进展[J].浙江大学学报（英文版）（B辑：生物医学和生物技术）,2025,26(05):409-420. DOI： 10.1631/jzus.B2400387.

Ruofan JIN, Ruhong ZHOU, Dong ZHANG. Recent advances in antibody optimization based on deep learning methods[J]. Journal of zhejiang university-science b (biomedicine & biotechnology), 2025, 26(5): 409-420. DOI： 10.1631/jzus.B2400387.

摘要

当前，抗体已成为多种疾病治疗的重要工具，快速高效地优化其理化性质是抗体药物开发过程中的关键环节。随着人工智能技术发展，特别是深度学习方法在生物学领域中取得的显著突破，各类计算方法被广泛应用于抗体优化环节中，以降低开发成本并提升抗体优化的成功率。本文对近年来基于深度学习的抗体优化策略进行综述，重点探讨了深度学习模型构建中关键的数据集资源及算法输入的数据类型；分析了当前深度学习算法在抗体优化应用中所面临的主要挑战；展望了未来通用型深度学习算法在该领域的发展方向及潜在的解决策略。

Abstract

Antibodies currently comprise the predominant treatment modality for a variety of diseases; therefore

optimizing their properties rapidly and efficiently is an indispensable step in antibody-based drug development. Inspired by the great success of artificial intelligence-based algorithms

especially deep learning-based methods in the field of biology

various computational methods have been introduced into antibody optimization to reduce costs and increase the success rate of lead candidate generation and optimization. Herein

we briefly review recent progress in deep learning-based antibody optimization

focusing on the available datasets and algorithm input data types that are crucial for constructing appropriate deep learning models. Furthermore

we discuss the current challenges and potential solutions for the future development of general-purpose deep learning algorithms in antibody optimization.

关键词

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references

Abramson J , Adler J , Dunger J , et al. , 2024 . Accurate structure prediction of biomolecular interactions with AlphaFold 3 . Nature , 630 ( 8016 ): 493 - 500 . https://doi.org/10.1038/s41586-024-07487-w https://doi.org/10.1038/s41586-024-07487-w

Akbar R , Bashour H , Rawat P , et al. , 2022 . Progress and challenges for the machine learning-based design of fit-for-purpose monoclonal antibodies . mAbs , 14 ( 1 ): 2008790 . https://doi.org/10.1080/19420862.2021.2008790 https://doi.org/10.1080/19420862.2021.2008790

Akter J , Khoury DS , Aogo R , et al. , 2019 . Plasmodium -specific antibodies block in vivo parasite growth without clearing infected red blood cells . PLoS Pathog , 15 ( 2 ): e1007599 . https://doi.org/10.1371/journal.ppat.1007599 https://doi.org/10.1371/journal.ppat.1007599

Ansari HR , Flower DR , Raghava GPS , 2010 . AntigenDB: an immunoinformatics database of pathogen antigens . Nucleic Acids Res , 38 ( suppl_1 ): D847 - D853 . https://doi.org/10.1093/nar/gkp830 https://doi.org/10.1093/nar/gkp830

Apweiler R , Bairoch A , Wu CH , et al. , 2004 . UniProt: the Universal Protein knowledgebase . Nucleic Acids Res , 32 ( suppl_1 ): D115 - D119 . https://doi.org/10.1093/nar/gkh131 https://doi.org/10.1093/nar/gkh131

Bairoch A , Apweiler R , 1997 . The SWISS-PROT protein sequence data bank and its supplement TrEMBL . Nucleic Acids Res , 25 ( 1 ): 31 - 36 . https://doi.org/10.1093/nar/25.1.31 https://doi.org/10.1093/nar/25.1.31

Baldo BA , 2022 . Immune- and non-immune-mediated adverse effects of monoclonal antibody therapy: a survey of 110 approved antibodies . Antibodies , 11 ( 1 ): 17 . https://doi.org/10.3390/antib11010017 https://doi.org/10.3390/antib11010017

Bennett NR , Watson JL , Ragotte RJ , et al. , 2024 . Atomically accurate de novo design of single-domain antibodies . bioRxiv , preprint. https://doi.org/10.1101/2024.03.14.585103 https://doi.org/10.1101/2024.03.14.585103

Berman HM , Westbrook J , Feng ZK , et al. , 2000 . The protein data bank . Nucleic Acids Res , 28 ( 1 ): 235 - 242 . https://doi.org/10.1093/nar/28.1.235 https://doi.org/10.1093/nar/28.1.235

Boniche C , Rossi SA , Kischkel B , et al. , 2020 . Immunotherapy against systemic fungal infections based on monoclonal antibodies . J Fungi , 6 ( 1 ): 31 . https://doi.org/10.3390/jof6010031 https://doi.org/10.3390/jof6010031

Chen JH , Wei GW , 2022 . Mathematical artificial intelligence design of mutation-proof COVID-19 monoclonal antibodies . Commun Inf Syst , 22 ( 3 ): 339 - 361 . https://doi.org/10.4310/CIS.2022.v22.n3.a3 https://doi.org/10.4310/CIS.2022.v22.n3.a3

Chen JM , Sawyer N , Regan L , 2013 . Protein‒protein interactions: general trends in the relationship between binding affinity and interfacial buried surface area . Protein Sci , 22 ( 4 ): 510 - 515 . https://doi.org/10.1002/pro.2230 https://doi.org/10.1002/pro.2230

Crescioli S , Kaplon H , Chenoweth A , et al. , 2024 . Antibodies to watch in 2024 . mAbs , 16 ( 1 ): 2297450 . https://doi.org/10.1080/19420862.2023.2297450 https://doi.org/10.1080/19420862.2023.2297450

do Pazo C , Nawaz K , Webster RM , 2021 . The oncology market for antibody‒drug conjugates . Nat Rev Drug Discov , 20 ( 8 ): 583 - 584 . https://doi.org/10.1038/d41573-021-00054-2 https://doi.org/10.1038/d41573-021-00054-2

Doron I , Mesko M , Li XV , et al. , 2021 . Mycobiota-induced IgA antibodies regulate fungal commensalism in the gut and are dysregulated in Crohn’s disease . Nat Microbiol , 6 ( 12 ): 1493 - 1504 . https://doi.org/10.1038/s41564-021-00983-z https://doi.org/10.1038/s41564-021-00983-z

Douguet D , Chen HC , Tovchigrechko A , et al. , 2006 . Dockground resource for studying protein‒protein interfaces . Bioinformatics , 22 ( 21 ): 2612 - 2618 . https://doi.org/10.1093/bioinformatics/btl447 https://doi.org/10.1093/bioinformatics/btl447

Dunbar J , Krawczyk K , Leem J , et al. , 2014 . SAbDab: the structural antibody database . Nucl Acids Res , 42 ( D1 ): D1140 - D1146 . https://doi.org/10.1093/nar/gkt1043 https://doi.org/10.1093/nar/gkt1043

Evans R , O'Neill M , Pritzel A , et al. , 2021 . Protein complex prediction with AlphaFold-Multimer . bioRxiv , preprint. https://doi.org/10.1101/2021.10.04.463034 https://doi.org/10.1101/2021.10.04.463034

Graves J , Byerly J , Priego E , et al. , 2020 . A review of deep learning methods for antibodies . Antibodies , 9 ( 2 ): 12 . https://doi.org/10.3390/antib9020012 https://doi.org/10.3390/antib9020012

Guo ZL , Yamaguchi R , 2022 . Machine learning methods for protein-protein binding affinity prediction in protein design . Front Bioinform , 2 : 1065703 . https://doi.org/10.3389/fbinf.2022.1065703 https://doi.org/10.3389/fbinf.2022.1065703

Gupta R , Srivastava D , Sahu M , et al. , 2021 . Artificial intelligence to deep learning: machine intelligence approach for drug discovery . Mol Divers , 25 ( 3 ): 1315 - 1360 . https://doi.org/10.1007/s11030-021-10217-3 https://doi.org/10.1007/s11030-021-10217-3

Hernandez I , Bott SW , Patel AS , et al. , 2018 . Pricing of monoclonal antibody therapies: higher if used for cancer? Am J Manag Care , 24 ( 2 ): 109 - 112 .

Hou WP , Shang XY , Ji ZC , 2023 . Benchmarking large language models for genomic knowledge with GeneTuring . bioRxiv , preprint. https://doi.org/10.1101/2023.03.11.532238 https://doi.org/10.1101/2023.03.11.532238

Jankauskaitė J , Jiménez-García B , Dapkūnas J , et al. , 2019 . SKEMPI 2.0: an updated benchmark of changes in protein‒protein binding energy, kinetics and thermodynamics upon mutation . Bioinformatics , 35 ( 3 ): 462 - 469 . https://doi.org/10.1093/bioinformatics/bty635 https://doi.org/10.1093/bioinformatics/bty635

Jin RF , Ye Q , Wang JK , et al. , 2024 . AttABseq: an attention-based deep learning prediction method for antigen‒antibody binding affinity changes based on protein sequences . Brief Bioinform , 25 ( 4 ): bbae304 . https://doi.org/10.1093/bib/bbae304 https://doi.org/10.1093/bib/bbae304

Jumper J , Evans R , Pritzel A , et al. , 2021 . Highly accurate protein structure prediction with AlphaFold . Nature , 596 ( 7873 ): 583 - 589 . https://doi.org/10.1038/s41586-021-03819-2 https://doi.org/10.1038/s41586-021-03819-2

Kang Y , Leng DW , Guo JJ , et al. , 2022 . Sequence-based deep learning antibody design for in silico antibody affinity maturation . arXiv :2103. 03724 . https://doi.org/10.48550/arXiv.2103.03724 https://doi.org/10.48550/arXiv.2103.03724

Kaplon H , Crescioli S , Chenoweth A , et al. , 2023 . Antibodies to watch in 2023 . mAbs , 15 ( 1 ): 2153410 . https://doi.org/10.1080/19420862.2022.2153410 https://doi.org/10.1080/19420862.2022.2153410

Köhler G , Milstein C , 1975 . Continuous cultures of fused cells secreting antibody of predefined specificity . Nature , 256 ( 5517 ): 495 - 497 . https://doi.org/10.1038/256495a0 https://doi.org/10.1038/256495a0

Lam HYI , Ong XE , Mutwil M , 2024 . Large language models in plant biology . Trends Plant Sci , 29 ( 10 ): 1145 - 1155 . https://doi.org/10.1016/j.tplants.2024.04.013 https://doi.org/10.1016/j.tplants.2024.04.013

Lee M , 2023 . Recent advances in deep learning for protein-protein interaction analysis: a comprehensive review . Molecules , 28 ( 13 ): 5169 . https://doi.org/10.3390/molecules28135169 https://doi.org/10.3390/molecules28135169

Listov D , Goverde CA , Correia BE , et al. , 2024 . Opportunities and challenges in design and optimization of protein function . Nat Rev Mol Cell Biol , 25 ( 8 ): 639 - 653 . https://doi.org/10.1038/s41580-024-00718-y https://doi.org/10.1038/s41580-024-00718-y

Liu G , Zeng HY , Mueller J , et al. , 2020 . Antibody complementarity determining region design using high-capacity machine learning . Bioinformatics , 36 ( 7 ): 2126 - 2133 . https://doi.org/10.1093/bioinformatics/btz895 https://doi.org/10.1093/bioinformatics/btz895

Liu XG , Luo YN , Li PY , et al. , 2021 . Deep geometric representations for modeling effects of mutations on protein-protein binding affinity . PLoS Comput Biol , 17 ( 8 ): e1009284 . https://doi.org/10.1371/journal.pcbi.1009284 https://doi.org/10.1371/journal.pcbi.1009284

Lubiana T , Lopes R , Medeiros P , et al. , 2023 . Ten quick tips for harnessing the power of ChatGPT in computational biology . PLoS Comput Biol , 19 ( 8 ): e1011319 . https://doi.org/10.1371/journal.pcbi.1011319 https://doi.org/10.1371/journal.pcbi.1011319

Makowski EK , Kinnunen PC , Huang J , et al. , 2022 . Co-optimization of therapeutic antibody affinity and specificity using machine learning models that generalize to novel mutational space . Nat Commun , 13 : 3788 . https://doi.org/10.1038/s41467-022-31457-3 https://doi.org/10.1038/s41467-022-31457-3

Mason DM , Friedensohn S , Weber CR , et al. , 2021 . Optimization of therapeutic antibodies by predicting antigen specificity from antibody sequence via deep learning . Nat Biomed Eng , 5 ( 6 ): 600 - 612 . https://doi.org/10.1038/s41551-021-00699-9 https://doi.org/10.1038/s41551-021-00699-9

Mastropietro A , Pasculli G , Bajorath J , 2023 . Learning characteristics of graph neural networks predicting protein‒ligand affinities . Nat Mach Intell , 5 ( 12 ): 1427 - 1436 . https://doi.org/10.1038/s42256-023-00756-9 https://doi.org/10.1038/s42256-023-00756-9

Meli R , Morris GM , Biggin PC , 2022 . Scoring functions for protein-ligand binding affinity prediction using structure-based deep learning: a review . Front Bioinform , 2 : 885983 . https://doi.org/10.3389/fbinf.2022.885983 https://doi.org/10.3389/fbinf.2022.885983

Mullard A , 2021 . FDA approves 100th monoclonal antibody product . Nat Rev Drug Discov , 20 ( 7 ): 491 - 495 . https://doi.org/10.1038/d41573-021-00079-7 https://doi.org/10.1038/d41573-021-00079-7

Notin P , Rollins N , Gal Y , et al. , 2024 . Machine learning for functional protein design . Nat Biotechnol , 42 ( 2 ): 216 - 228 . https://doi.org/10.1038/s41587-024-02127-0 https://doi.org/10.1038/s41587-024-02127-0

O'Shea K , Nash R , 2015 . An introduction to convolutional neural networks . arXiv :1511. 08458 . https://doi.org/10.48550/arXiv.1511.08458 https://doi.org/10.48550/arXiv.1511.08458

Pires DEV , Ascher DB , 2016 . mCSM-AB: a web server for predicting antibody‒antigen affinity changes upon mutation with graph-based signatures . Nucleic Acids Res , 44 ( W1 ): W469 - W473 . https://doi.org/10.1093/nar/gkw458 https://doi.org/10.1093/nar/gkw458

Réau M , Renaud N , Xue LC , et al. , 2023 . DeepRank-GNN: a graph neural network framework to learn patterns in protein‒protein interfaces . Bioinformatics , 39 ( 1 ): btac759 . https://doi.org/10.1093/bioinformatics/btac759 https://doi.org/10.1093/bioinformatics/btac759

Shan SS , Luo ST , Yang ZQ , et al. , 2022 . Deep learning guided optimization of human antibody against SARS-CoV-2 variants with broad neutralization . Proc Natl Acad Sci USA , 119 ( 11 ): e2122954119 . https://doi.org/10.1073/pnas.2122954119 https://doi.org/10.1073/pnas.2122954119

Silver D , Huang A , Maddison CJ , et al. , 2016 . Mastering the game of Go with deep neural networks and tree search . Nature , 529 ( 7587 ): 484 - 489 . https://doi.org/10.1038/nature16961 https://doi.org/10.1038/nature16961

Sirin S , Apgar JR , Bennett EM , et al. , 2016 . AB‐bind: antibody binding mutational database for computational affinity predictions . Protein Sci , 25 ( 2 ): 393 - 409 . https://doi.org/10.1002/pro.2829 https://doi.org/10.1002/pro.2829

Soleymani F , Paquet E , Viktor H , et al. , 2022 . Protein‒protein interaction prediction with deep learning: a comprehensive review . Comput Struct Biotechnol J , 20 : 5316 - 5341 . https://doi.org/10.1016/j.csbj.2022.08.070 https://doi.org/10.1016/j.csbj.2022.08.070

Soleymani F , Paquet E , Viktor HL , et al. , 2023 . ProtInteract: a deep learning framework for predicting protein‒protein interactions . Comput Struct Biotechnol J , 21 : 1324 - 1348 . https://doi.org/10.1016/j.csbj.2023.01.028 https://doi.org/10.1016/j.csbj.2023.01.028

Sun XY , Yi CY , Zhu YF , et al. , 2022 . Neutralization mechanism of a human antibody with pan-coronavirus reactivity including SARS-CoV-2 . Nat Microbiol , 7 ( 7 ): 1063 - 1074 . https://doi.org/10.1038/s41564-022-01155-3 https://doi.org/10.1038/s41564-022-01155-3

Szklarczyk D , Kirsch R , Koutrouli M , et al. , 2023 . The STRING database in 2023: protein‒protein association networks and functional enrichment analyses for any sequenced genome of interest . Nucleic Acids Res , 51 ( D1 ): D638 - D646 . https://doi.org/10.1093/nar/gkac1000 https://doi.org/10.1093/nar/gkac1000

Thirumalai D , Visaga Ambi S , Vieira-Pires RS , et al. , 2019 . Chicken egg yolk antibody (IgY) as diagnostics and therapeutics in parasitic infections‒a review . Int J Biol Macromol , 136 : 755 - 763 . https://doi.org/10.1016/j.ijbiomac.2019.06.118 https://doi.org/10.1016/j.ijbiomac.2019.06.118

Toseland CP , Clayton DJ , McSparron H , et al. , 2005 . AntiJen: a quantitative immunology database integrating functional, thermodynamic, kinetic, biophysical, and cellular data . Immunome Res , 1 : 4 . https://doi.org/10.1186/1745-7580-1-4 https://doi.org/10.1186/1745-7580-1-4

Tovchigrechko A , Vakser IA , 2006 . GRAMM-X public web server for protein‒protein docking . Nucleic Acids Res , 34 ( suppl_2 ): W310 - W314 . https://doi.org/10.1093/nar/gkl206 https://doi.org/10.1093/nar/gkl206

Vita R , Mahajan S , Overton JA , et al. , 2019 . The Immune Epitope Database (IEDB): 2018 update . Nucleic Acids Res , 47 ( D1 ): D339 - D343 . https://doi.org/10.1093/nar/gky1006 https://doi.org/10.1093/nar/gky1006

Wang GY , Liu XH , Wang K , et al. , 2023 . Deep-learning-enabled protein‒protein interaction analysis for prediction of SARS-CoV-2 infectivity and variant evolution . Nat Med , 29 ( 8 ): 2007 - 2018 . https://doi.org/10.1038/s41591-023-02483-5 https://doi.org/10.1038/s41591-023-02483-5

Wang ML , Cang ZX , Wei GW , 2020 . A topology-based network tree for the prediction of protein‒protein binding affinity changes following mutation . Nat Mach Intell , 2 ( 2 ): 116 - 123 . https://doi.org/10.1038/s42256-020-0149-6 https://doi.org/10.1038/s42256-020-0149-6

Wang RX , Fang XL , Lu YP , et al. , 2004 . The PDBbind database: collection of binding affinities for protein‒ligand complexes with known three-dimensional structures . J Med Chem , 47 ( 12 ): 2977 - 2980 . https://doi.org/10.1021/jm030580l https://doi.org/10.1021/jm030580l

Wilman W , Wróbel S , Bielska W , et al. , 2022 . Machine-designed biotherapeutics: opportunities, feasibility and advantages of deep learning in computational antibody discovery . Brief Bioinform , 23 ( 4 ): bbac267 . https://doi.org/10.1093/bib/bbac267 https://doi.org/10.1093/bib/bbac267

Wu ZH , Pan SR , Chen FW , et al. , 2021 . A comprehensive survey on graph neural networks . IEEE Trans Neural Netw Learn Syst , 32 ( 1 ): 4 - 24 . https://doi.org/10.1109/TNNLS.2020.2978386 https://doi.org/10.1109/TNNLS.2020.2978386

Young C , Lau AWY , Burnett DL , 2022 . B cells in the balance: offsetting self-reactivity avoidance with protection against foreign . Front Immunol , 13 : 951385 . https://doi.org/10.3389/fimmu.2022.951385 https://doi.org/10.3389/fimmu.2022.951385

Zhao Y , He B , Xu F , et al. , 2023 . DeepAIR: a deep learning framework for effective integration of sequence and 3D structure to enable adaptive immune receptor analysis . Sci Adv , 9 ( 32 ): eabo5128 . https://doi.org/10.1126/sciadv.abo5128 https://doi.org/10.1126/sciadv.abo5128

Zheng W , Zhao WJ , Wu M , et al. , 2020 . Microbiota-targeted maternal antibodies protect neonates from enteric infection . Nature , 577 ( 7791 ): 543 - 548 . https://doi.org/10.1038/s41586-019-1898-4 https://doi.org/10.1038/s41586-019-1898-4

Zurawski DV , McLendon MK , 2020 . Monoclonal antibodies as an antibacterial approach against bacterial pathogens . Antibiotics , 9 ( 4 ): 155 . https://doi.org/10.3390/antibiotics9040155 https://doi.org/10.3390/antibiotics9040155

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