{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,1]],"date-time":"2026-04-01T18:01:43Z","timestamp":1775066503143,"version":"3.50.1"},"reference-count":44,"publisher":"Oxford University Press (OUP)","issue":"4","license":[{"start":{"date-parts":[[2022,7,11]],"date-time":"2022-07-11T00:00:00Z","timestamp":1657497600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by-nc\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2022,7,18]]},"abstract":"<jats:title>Abstract<\/jats:title>\n                  <jats:p>In this study, we introduce an interpretable graph-based deep learning prediction model, AttentionSiteDTI, which utilizes protein binding sites along with a self-attention mechanism to address the problem of drug\u2013target interaction prediction. Our proposed model is inspired by sentence classification models in the field of Natural Language Processing, where the drug\u2013target complex is treated as a sentence with relational meaning between its biochemical entities a.k.a. protein pockets and drug molecule. AttentionSiteDTI enables interpretability by identifying the protein binding sites that contribute the most toward the drug\u2013target interaction. Results on three benchmark datasets show improved performance compared with the current state-of-the-art models. More significantly, unlike previous studies, our model shows superior performance, when tested on new proteins (i.e. high generalizability). Through multidisciplinary collaboration, we further experimentally evaluate the practical potential of our proposed approach. To achieve this, we first computationally predict the binding interactions between some candidate compounds and a target protein, then experimentally validate the binding interactions for these pairs in the laboratory. The high agreement between the computationally predicted and experimentally observed (measured) drug\u2013target interactions illustrates the potential of our method as an effective pre-screening tool in drug repurposing applications.<\/jats:p>","DOI":"10.1093\/bib\/bbac272","type":"journal-article","created":{"date-parts":[[2022,7,11]],"date-time":"2022-07-11T19:17:06Z","timestamp":1657567026000},"source":"Crossref","is-referenced-by-count":102,"title":["AttentionSiteDTI: an interpretable graph-based model for drug-target interaction prediction using NLP sentence-level relation classification"],"prefix":"10.1093","volume":"23","author":[{"given":"Mehdi","family":"Yazdani-Jahromi","sequence":"first","affiliation":[{"name":"Industrial Engineering and Management Systems, University of Central Florida , Street, 32816, 4000 Central Florida Blvd. Orlando, USA"}]},{"given":"Niloofar","family":"Yousefi","sequence":"additional","affiliation":[{"name":"Industrial Engineering and Management Systems, University of Central Florida , Street, 32816, 4000 Central Florida Blvd. Orlando, USA"}]},{"given":"Aida","family":"Tayebi","sequence":"additional","affiliation":[{"name":"Industrial Engineering and Management Systems, University of Central Florida , Street, 32816, 4000 Central Florida Blvd. Orlando, USA"}]},{"given":"Elayaraja","family":"Kolanthai","sequence":"additional","affiliation":[{"name":"College of Medicine , Bionix Cluster, , 4000 Central Florida Blvd. Orlando, 32816, Florida, USA"},{"name":"University of Central Florida , Bionix Cluster, , 4000 Central Florida Blvd. Orlando, 32816, Florida, USA"}]},{"given":"Craig J","family":"Neal","sequence":"additional","affiliation":[{"name":"College of Medicine , Bionix Cluster, , 4000 Central Florida Blvd. Orlando, 32816, Florida, USA"},{"name":"University of Central Florida , Bionix Cluster, , 4000 Central Florida Blvd. Orlando, 32816, Florida, USA"}]},{"given":"Sudipta","family":"Seal","sequence":"additional","affiliation":[{"name":"College of Medicine , Bionix Cluster, , 4000 Central Florida Blvd. Orlando, 32816, Florida, USA"},{"name":"University of Central Florida , Bionix Cluster, , 4000 Central Florida Blvd. Orlando, 32816, Florida, USA"},{"name":"Advanced Materials Processing and Analysis Center , Dept. of Materials Science and Engineering, , 4000 Central Florida Blvd. Orlando, 32816, Florida, USA"},{"name":"University of Central Florida , Dept. of Materials Science and Engineering, , 4000 Central Florida Blvd. Orlando, 32816, Florida, USA"}]},{"given":"Ozlem Ozmen","family":"Garibay","sequence":"additional","affiliation":[{"name":"Industrial Engineering and Management Systems, University of Central Florida , Street, 32816, 4000 Central Florida Blvd. Orlando, 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