Kavousipour, Soudabeh and Barazesh, Mahdi and Mohammadi, Shiva (2025) Artificial intelligence in antibody design and development: harnessing the power of computational approaches. Med Biol Eng Comput.
Full text not available from this repository.Abstract
Antibodies are a key therapeutic class in pharma, enabling precise targeting of disease agents. Traditional methods for their design are slow, costly, and limited. Advances in high-throughput data and artificial intelligence (AI) including machine learning, deep learning, and reinforcement learning have revolutionized antibody sequence design, 3D structure prediction, and optimization of affinity and specificity. Computational approaches enable rapid library generation and efficient screening, reduce experimental sampling, and support rational design with improved immune response. Combining AI with experimental methods allows for de novo, multifunctional antibody development. AI also accelerates the discovery process, target identification, and candidate prioritization by analyzing large datasets, predicting interactions, and guiding modifications to enhance efficacy and safety. Despite challenges, ongoing research continues to expand the potential of AI and transform antibody development and the pharmaceutical industry. Antibodies are a key therapeutic class in pharma, enabling precise targeting of disease agents. Traditional methods for their design are slow, costly, and limited. Advances in high-throughput data and artificial intelligence (AI) including machine learning, deep learning, and reinforcement learning have revolutionized antibody sequence design, 3D structure prediction, and optimization of affinity and specificity. Computational approaches enable rapid library generation and efficient screening, reduce experimental sampling, and support rational design with improved immune response. Combining AI with experimental methods allows for de novo, multifunctional antibody development. AI also accelerates the discovery process, target identification, and candidate prioritization by analyzing large datasets, predicting interactions, and guiding modifications to enhance efficacy and safety. Despite challenges, ongoing research continues to expand the potential of AI and transform antibody development and the pharmaceutical industry
| Item Type: | Article |
|---|---|
| Subjects: | R Medicine > RZ Other systems of medicine |
| Divisions: | Faculty of Medicine, Health and Life Sciences > School of Medicine |
| Depositing User: | lorestan university |
| Date Deposited: | 02 Sep 2025 03:57 |
| Last Modified: | 02 Sep 2025 03:57 |
| URI: | http://eprints.lums.ac.ir/id/eprint/5205 |
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