{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,16]],"date-time":"2026-04-16T22:46:39Z","timestamp":1776379599867,"version":"3.51.2"},"reference-count":39,"publisher":"Oxford University Press (OUP)","issue":"9","license":[{"start":{"date-parts":[[2022,3,11]],"date-time":"2022-03-11T00:00:00Z","timestamp":1646956800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by-nc\/4.0\/"}],"funder":[{"name":"National Institute of General Medical Sciences of National Institute of Health","award":["R01GM122845"],"award-info":[{"award-number":["R01GM122845"]}]},{"name":"National Institute on Aging of the National Institute of Health","award":["R01AD057555"],"award-info":[{"award-number":["R01AD057555"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2022,4,28]]},"abstract":"<jats:title>Abstract<\/jats:title><jats:sec><jats:title>Motivation<\/jats:title><jats:p>Drug discovery has witnessed intensive exploration of predictive modeling of drug\u2013target physical interactions over two decades. However, a critical knowledge gap needs to be filled for correlating drug\u2013target interactions with clinical outcomes: predicting genome-wide receptor activities or function selectivity, especially agonist versus antagonist, induced by novel chemicals. Two major obstacles compound the difficulty on this task: known data of receptor activity is far too scarce to train a robust model in light of genome-scale applications, and real-world applications need to deploy a model on data from various shifted distributions.<\/jats:p><\/jats:sec><jats:sec><jats:title>Results<\/jats:title><jats:p>To address these challenges, we have developed an end-to-end deep learning framework, DeepREAL, for multi-scale modeling of genome-wide ligand-induced receptor activities. DeepREAL utilizes self-supervised learning on tens of millions of protein sequences and pre-trained binary interaction classification to solve the data distribution shift and data scarcity problems. Extensive benchmark studies on G-protein coupled receptors (GPCRs), which simulate real-world scenarios, demonstrate that DeepREAL achieves state-of-the-art performances in out-of-distribution settings. DeepREAL can be extended to other gene families beyond GPCRs.<\/jats:p><\/jats:sec><jats:sec><jats:title>Availability and implementation<\/jats:title><jats:p>All data used are downloaded from Pfam (Mistry et al., 2020), GLASS (Chan et al., 2015) and IUPHAR\/BPS and the data from reference (Sakamuru et al., 2021). Readers are directed to their official website for original data. Code is available on GitHub https:\/\/github.com\/XieResearchGroup\/DeepREAL.<\/jats:p><\/jats:sec><jats:sec><jats:title>Supplementary information<\/jats:title><jats:p>Supplementary data are available at Bioinformatics online.<\/jats:p><\/jats:sec>","DOI":"10.1093\/bioinformatics\/btac154","type":"journal-article","created":{"date-parts":[[2022,3,10]],"date-time":"2022-03-10T20:11:29Z","timestamp":1646943089000},"page":"2561-2570","source":"Crossref","is-referenced-by-count":20,"title":["DeepREAL: a deep learning powered multi-scale modeling framework for predicting out-of-distribution ligand-induced GPCR activity"],"prefix":"10.1093","volume":"38","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-9516-6489","authenticated-orcid":false,"given":"Tian","family":"Cai","sequence":"first","affiliation":[{"name":"Ph.D. Program in Computer Science, The Graduate Center, The City University of New York , New York, NY 10016, USA"}]},{"given":"Kyra Alyssa","family":"Abbu","sequence":"additional","affiliation":[{"name":"Department of Computer Science, Hunter College, The City University of New York , New York, NY 10065, USA"}]},{"given":"Yang","family":"Liu","sequence":"additional","affiliation":[{"name":"Department of Computer Science, Hunter College, The City University of New York , New York, NY 10065, USA"}]},{"given":"Lei","family":"Xie","sequence":"additional","affiliation":[{"name":"Ph.D. Program in Computer Science, The Graduate Center, The City University of New York , New York, NY 10016, USA"},{"name":"Department of Computer Science, Hunter College, The City University of New York , New York, NY 10065, USA"},{"name":"Helen and Robert Appel Alzheimer\u2019s Disease Research Institute, Feil Family Brain & Mind Research Institute, Weill Cornell Medicine, Cornell University , New York, NY 10021, USA"}]}],"member":"286","published-online":{"date-parts":[[2022,3,11]]},"reference":[{"key":"2023041402564495500_","first-page":"D1006","article-title":"The IUPHAR\/BPS Guide to PHARMACOLOGY in 2020: extending immunopharmacology content and introducing the IUPHAR\/MMV Guide to MALARIA PHARMACOLOGY","volume":"48","author":"Armstrong","year":"2019","journal-title":"Nucleic Acids Res"},{"key":"2023041402564495500_","first-page":"217","volume-title":"Annual Reports in Computational Chemistry","author":"Bolton","year":"2008"},{"key":"2023041402564495500_","doi-asserted-by":"crossref","first-page":"1570","DOI":"10.1021\/acs.jcim.0c01285","article-title":"MSA-regularized protein sequence transformer toward predicting genome-wide chemical-protein interactions: application to GPCRome deorphanization","volume":"61","author":"Cai","year":"2021","journal-title":"J. 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