{"database":"biostudies-literature","file_versions":[],"scores":null,"additional":{"omics_type":["Unknown"],"volume":["150(5)"],"submitter":["Zhang X"],"pubmed_abstract":["<h4>Purpose</h4>Bispecific antibodies (BsAbs), capable of targeting two antigens simultaneously, represent a significant advancement by employing dual mechanisms of action for tumor suppression. However, how to pair targets to develop effective and safe bispecific drugs is a major challenge for pharmaceutical companies.<h4>Methods</h4>Using machine learning models, we refined the biological characteristics of currently approved or in clinical development BsAbs and analyzed hundreds of membrane proteins as bispecific targets to predict the likelihood of successful drug development for various target combinations. Moreover, to enhance the interpretability of prediction results in bispecific target combination, we combined machine learning models with Large Language Models (LLMs). Through a R"],"journal":["Journal of cancer research and clinical oncology"],"pagination":["237"],"full_dataset_link":["https://www.ebi.ac.uk/biostudies/studies/S-EPMC11076393"],"repository":["biostudies-literature"],"pubmed_title":["BiSpec Pairwise AI: guiding the selection of bispecific antibody target combinations with pairwise learning and GPT augmentation."],"pmcid":["PMC11076393"],"pubmed_authors":["Zhang X","Sun C","Wang H"],"additional_accession":[]},"is_claimable":false,"name":"BiSpec Pairwise AI: guiding the selection of bispecific antibody target combinations with pairwise learning and GPT augmentation.","description":"<h4>Purpose</h4>Bispecific antibodies (BsAbs), capable of targeting two antigens simultaneously, represent a significant advancement by employing dual mechanisms of action for tumor suppression. However, how to pair targets to develop effective and safe bispecific drugs is a major challenge for pharmaceutical companies.<h4>Methods</h4>Using machine learning models, we refined the biological characteristics of currently approved or in clinical development BsAbs and analyzed hundreds of membrane proteins as bispecific targets to predict the likelihood of successful drug development for various target combinations. Moreover, to enhance the interpretability of prediction results in bispecific target combination, we combined machine learning models with Large Language Models (LLMs). Through a R","dates":{"release":"2024-01-01T00:00:00Z","publication":"2024 May","modification":"2026-06-01T21:18:24.087Z","creation":"2026-05-21T03:08:06.14Z"},"accession":"S-EPMC11076393","cross_references":{"pubmed":["38713378"],"doi":["10.1007/s00432-024-05740-3"]}}