{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,1,12]],"date-time":"2026-01-12T11:17:20Z","timestamp":1768216640276,"version":"3.49.0"},"reference-count":49,"publisher":"Frontiers Media SA","license":[{"start":{"date-parts":[[2026,1,12]],"date-time":"2026-01-12T00:00:00Z","timestamp":1768176000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":["frontiersin.org"],"crossmark-restriction":true},"short-container-title":["Front. Bioinform."],"abstract":"<jats:p>\n                    Accurately predicting protein-ligand binding affinity is key in drug discovery. Machine Learning and Deep Learning methods used in the drug discovery process have advanced the prediction of drug\u2013target binding affinities, particularly for G protein\u2013coupled receptors (GPCRs), a pharmacologically significant yet structurally heterogeneous protein family. In this review, binding affinity prediction models are examined and organized according to sequence-based one-dimensional, graph-based two-dimensional, and structure-based three-dimensional frameworks. Sequence-based models utilize convolutional neural networks for high-throughput screening. Recently published models incorporated attention mechanisms and self-supervised learning, enhancing interpretability and reducing dependence on annotated datasets. Graph-based models employ graph neural networks and molecular contact maps to capture topological features, enabling substructure-sensitive predictions. Structure-based approaches integrate spatial and conformational data into high-resolution interaction models. The hybrid use of these three approaches could significantly increase the success rate of\n                    <jats:italic>in silico<\/jats:italic>\n                    models for drug discovery, particularly for GPCRs.\n                  <\/jats:p>","DOI":"10.3389\/fbinf.2025.1712577","type":"journal-article","created":{"date-parts":[[2026,1,12]],"date-time":"2026-01-12T08:13:43Z","timestamp":1768205623000},"update-policy":"https:\/\/doi.org\/10.3389\/crossmark-policy","source":"Crossref","is-referenced-by-count":0,"title":["Recent trends in machine learning and deep learning-based prediction of G-protein coupled receptor-ligand binding affinities"],"prefix":"10.3389","volume":"5","author":[{"given":"Joshua","family":"Stephenson","sequence":"first","affiliation":[]},{"given":"Konda Reddy","family":"Karnati","sequence":"additional","affiliation":[]}],"member":"1965","published-online":{"date-parts":[[2026,1,12]]},"reference":[{"key":"B1","doi-asserted-by":"publisher","first-page":"891","DOI":"10.3390\/ph16060891","article-title":"The role of AI in drug discovery: challenges, opportunities, and strategies","volume":"16","author":"Blanco-Gonz\u00e1lez","year":"2023","journal-title":"Pharmaceuticals"},{"key":"B2","doi-asserted-by":"publisher","first-page":"12","DOI":"10.1186\/s13321-024-00945-7","article-title":"AiGPro: a multi-task model for profiling GPCRs for agonists and antagonists","volume":"17","author":"Brahma","year":"2025","journal-title":"J. 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