{"database":"biostudies-literature","file_versions":[],"scores":null,"additional":{"omics_type":["Unknown"],"volume":["11"],"submitter":["Fei A"],"pubmed_abstract":["<h4>Background</h4>The rapid development of artificial intelligence has permeated many fields, with its application in drug discovery becoming increasingly mature. Machine learning, particularly deep learning, has significantly improved the efficiency of drug discovery. In the core task of predicting drug-target affinity (DTA), deep learning enhances predictive performance by automatically extracting complex features from compounds and proteins.<h4>Methods</h4>Traditional approaches often rely heavily on sequence and two-dimensional structural information, overlooking critical three-dimensional and physicochemical properties. To address this, we propose a novel model-Cross Attention Fusion based on Information Enhancement for Drug-Target Affinity Prediction (CAFIE-DTA)-which incorporates p"],"journal":["PeerJ. Computer science"],"pagination":["e3117"],"full_dataset_link":["https://www.ebi.ac.uk/biostudies/studies/S-EPMC12453779"],"repository":["biostudies-literature"],"pubmed_title":["Enhanced information cross-attention fusion for drug-target binding affinity prediction."],"pmcid":["PMC12453779"],"pubmed_authors":["Fei A","Zhang Y","Yao M","Wang Y","Ruan T","Wang L"],"additional_accession":[]},"is_claimable":false,"name":"Enhanced information cross-attention fusion for drug-target binding affinity prediction.","description":"<h4>Background</h4>The rapid development of artificial intelligence has permeated many fields, with its application in drug discovery becoming increasingly mature. Machine learning, particularly deep learning, has significantly improved the efficiency of drug discovery. In the core task of predicting drug-target affinity (DTA), deep learning enhances predictive performance by automatically extracting complex features from compounds and proteins.<h4>Methods</h4>Traditional approaches often rely heavily on sequence and two-dimensional structural information, overlooking critical three-dimensional and physicochemical properties. To address this, we propose a novel model-Cross Attention Fusion based on Information Enhancement for Drug-Target Affinity Prediction (CAFIE-DTA)-which incorporates p","dates":{"release":"2025-01-01T00:00:00Z","publication":"2025","modification":"2026-04-30T03:22:01.876Z","creation":"2026-04-30T03:11:31.993Z"},"accession":"S-EPMC12453779","cross_references":{"pubmed":["40989310"],"doi":["10.7717/peerj-cs.3117"]}}