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Artificial intelligence-driven computational methods for antibody design and optimization.

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Title: Artificial intelligence-driven computational methods for antibody design and optimization.
Authors: Vecchietti LF; Max Planck Institute for Security and Privacy (MPI-SP), Universitätsstraße 140, Bochum, Germany., Wijaya BN; School of Computing, Korea Advanced Institute of Science and Technology (KAIST), Daejeon, Republic of Korea., Armanuly A; Graduate School of Medical Science and Engineering, Korea Advanced Institute of Science and Technology (KAIST), Daejeon, Republic of Korea., Hangeldiyev B; Robotics Program, Korea Advanced Institute of Science and Technology (KAIST), Daejeon, Republic of Korea., Jung H; School of Computing, Korea Advanced Institute of Science and Technology (KAIST), Daejeon, Republic of Korea., Lee S; Department of Biological Sciences, Korea Advanced Institute of Science and Technology (KAIST), Daejeon, Republic of Korea., Cha M; Max Planck Institute for Security and Privacy (MPI-SP), Universitätsstraße 140, Bochum, Germany.; School of Computing, Korea Advanced Institute of Science and Technology (KAIST), Daejeon, Republic of Korea., Kim HM; Department of Biological Sciences, Korea Advanced Institute of Science and Technology (KAIST), Daejeon, Republic of Korea.; Center for Biomolecular and Cellular Structure, Institute for Basic Science (IBS), Daejeon, Republic of Korea.
Source: MAbs [MAbs] 2025 Dec; Vol. 17 (1), pp. 2528902. Date of Electronic Publication: 2025 Jul 18.
Publication Type: Journal Article; Review; Research Support, Non-U.S. Gov't
Journal Info: Publisher: Taylor & Francis Country of Publication: United States NLM ID: 101479829 Publication Model: Print-Electronic Cited Medium: Internet ISSN: 1942-0870 (Electronic) Linking ISSN: 19420862 NLM ISO Abbreviation: MAbs Subsets: MEDLINE
Database: MEDLINE Ultimate
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ISSN:1942-0870
DOI:10.1080/19420862.2025.2528902