{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,17]],"date-time":"2026-04-17T18:33:45Z","timestamp":1776450825807,"version":"3.51.2"},"reference-count":41,"publisher":"MDPI AG","issue":"5","license":[{"start":{"date-parts":[[2025,2,27]],"date-time":"2025-02-27T00:00:00Z","timestamp":1740614400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"National Science Centre in Poland","award":["2020\/39\/B\/NZ2\/00584"],"award-info":[{"award-number":["2020\/39\/B\/NZ2\/00584"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["IJMS"],"abstract":"<jats:p>G protein-coupled receptors (GPCRs) constitute the largest and most frequently used family of molecular drug targets. The simplicity of GPCR drug design results from their common seven-transmembrane-helix topology and well-understood signaling pathways. GPCRs are extremely sensitive to slight changes in the chemical structure of compounds, which allows for the reliable design of highly selective and specific drugs. Only recently has the number of GPCR structures, both in their active and inactive conformations, together with their active ligands, become sufficient to comprehensively apply machine learning in decision support systems to predict compound activity in drug design. Here, we describe GPCRVS, an efficient machine learning system for the online assessment of the compound activity against several GPCR targets, including peptide- and protein-binding GPCRs, which are the most difficult for virtual screening tasks. As a decision support system, GPCRVS evaluates compounds in terms of their activity range, the pharmacological effect they exert on the receptor, and the binding mode they could demonstrate for different types and subtypes of GPCRs. GPCRVS allows for the evaluation of compounds ranging from small molecules to short peptides provided in common chemical file formats. The results of the activity class assignment and the binding affinity prediction are provided in comparison with predictions for known active ligands of each included GPCR. Multiclass classification in GPCRVS, handling incomplete and fuzzy biological data, was validated on ChEMBL and Google Patents-retrieved data sets for class B GPCRs and chemokine CC and CXC receptors.<\/jats:p>","DOI":"10.3390\/ijms26052160","type":"journal-article","created":{"date-parts":[[2025,2,28]],"date-time":"2025-02-28T02:48:37Z","timestamp":1740710917000},"page":"2160","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":8,"title":["GPCRVS - AI-driven Decision Support System for GPCR Virtual Screening"],"prefix":"10.3390","volume":"26","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-0429-0637","authenticated-orcid":false,"given":"Dorota","family":"Latek","sequence":"first","affiliation":[{"name":"University of Warsaw, Faculty of Chemistry, 1 Pasteur St, 02-093 Warsaw, Poland"}]},{"given":"Khushil","family":"Prajapati","sequence":"additional","affiliation":[{"name":"University of Warsaw, Faculty of Chemistry, 1 Pasteur St, 02-093 Warsaw, Poland"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-0654-5322","authenticated-orcid":false,"given":"Paulina","family":"Dragan","sequence":"additional","affiliation":[{"name":"University of Warsaw, Faculty of Chemistry, 1 Pasteur St, 02-093 Warsaw, Poland"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-1844-6997","authenticated-orcid":false,"given":"Matthew","family":"Merski","sequence":"additional","affiliation":[{"name":"University of Warsaw, Faculty of Chemistry, 1 Pasteur St, 02-093 Warsaw, Poland"}]},{"given":"Przemys\u0142aw","family":"Osial","sequence":"additional","affiliation":[{"name":"University of Warsaw, Faculty of Chemistry, 1 Pasteur St, 02-093 Warsaw, Poland"}]}],"member":"1968","published-online":{"date-parts":[[2025,2,27]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"19","DOI":"10.1038\/nrd.2016.230","article-title":"A Comprehensive Map of Molecular Drug Targets","volume":"16","author":"Santos","year":"2017","journal-title":"Nat. Rev. Drug Discov."},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"247","DOI":"10.1016\/j.sbi.2016.11.005","article-title":"Mechanistic Insights into GPCR\u2013G Protein Interactions","volume":"41","author":"Mahoney","year":"2016","journal-title":"Curr. Opin. Struct. Biol."},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"9501","DOI":"10.1073\/pnas.0811437106","article-title":"The Effect of Ligand Efficacy on the Formation and Stability of a GPCR-G Protein Complex","volume":"106","author":"Yao","year":"2009","journal-title":"Proc. Natl. Acad. Sci. USA"},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"3018","DOI":"10.1111\/bph.13278","article-title":"Superagonism at G Protein-coupled Receptors and Beyond","volume":"173","author":"Schrage","year":"2016","journal-title":"Br. J. Pharmacol."},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"225","DOI":"10.1007\/978-981-13-8719-7_10","article-title":"GPCR Allosteric Modulator Discovery","volume":"Volume 1163","author":"Zhang","year":"2019","journal-title":"Protein Allostery in Drug Discovery"},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"107","DOI":"10.1080\/10799890600567570","article-title":"Ago-Allosteric Modulation and Other Types of Allostery in Dimeric 7TM Receptors","volume":"26","author":"Schwartz","year":"2006","journal-title":"J. Recept. Signal Transduct."},{"key":"ref_7","doi-asserted-by":"crossref","unstructured":"Belle, V., and Papantonis, I. (2021). Principles and Practice of Explainable Machine Learning. Front. Big Data, 4.","DOI":"10.3389\/fdata.2021.688969"},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"6169","DOI":"10.1021\/acs.jcim.3c00685","article-title":"CODD-Pred: A Web Server for Efficient Target Identification and Bioactivity Prediction of Small Molecules","volume":"63","author":"Yin","year":"2023","journal-title":"J. Chem. Inf. Model."},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"W726","DOI":"10.1093\/nar\/gkac297","article-title":"SuperPred 3.0: Drug Classification and Target Prediction-a Machine Learning Approach","volume":"50","author":"Gallo","year":"2022","journal-title":"Nucleic Acids Res."},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"W357","DOI":"10.1093\/nar\/gkz382","article-title":"SwissTargetPrediction: Updated Data and New Features for Efficient Prediction of Protein Targets of Small Molecules","volume":"47","author":"Daina","year":"2019","journal-title":"Nucleic Acids Res."},{"key":"ref_11","unstructured":"Chollet, F., and Keras: The Python Deep Learning Library (2025, February 23). Astrophysics Source Code Library 2018; ascl:1806.022. Available online: https:\/\/ascl.net\/1806.022."},{"key":"ref_12","unstructured":"Abadi, M., Barham, P., Chen, J., Chen, Z., Davis, A., Dean, J., Devin, M., Ghemawat, S., Irving, G., and Isard, M. (2016, January 2). TensorFlow: A System for Large-Scale Machine Learning. Proceedings of the 12th USENIX Symposium on Operating Systems Design and Implementation (OSDI\u201916), USENIX Association, Savannah, GA, USA."},{"key":"ref_13","unstructured":"Ke, G., Meng, Q., Finley, T., Wang, T., Chen, W., Ma, W., Ye, Q., and Liu, T.-Y. (2017, January 4\u20139). LightGBM: A Highly Efficient Gradient Boosting Decision Tree. Proceedings of the Advances in Neural Information Processing Systems, Long Beach, CA, USA."},{"key":"ref_14","unstructured":"Kingma, D.P., and Ba, J. (2015, January 7\u20139). Adam: A Method for Stochastic Optimization. Proceedings of the 3rd International Conference for Learning Representations (ICLR\u201915), San Diego, CA, USA."},{"key":"ref_15","unstructured":"Gordon-Rodriguez, E., Loaiza-Ganem, G., Pleiss, G., and Cunningham, J.P. (2020). Uses and Abuses of the Cross-Entropy Loss: Case Studies in Modern Deep Learning. arXiv."},{"key":"ref_16","unstructured":"O\u2019Malley, T., Bursztein, E., Long, J., Chollet, F., Jin, H., and Invernizzi, L. (2025, February 23). KerasTuner 2019. Available online: https:\/\/github.com\/keras-team\/keras-tuner."},{"key":"ref_17","unstructured":"Ranganathan, S., Gribskov, M., Nakai, K., and Sch\u00f6nbach, C. (2019). Cross-Validation. Encyclopedia of Bioinformatics and Computational Biology, Academic Press."},{"key":"ref_18","doi-asserted-by":"crossref","first-page":"45","DOI":"10.1037\/h0072400","article-title":"The Shrinkage of the Coefficient of Multiple Correlation","volume":"22","author":"Larson","year":"1931","journal-title":"J. Educ. Psychol."},{"key":"ref_19","doi-asserted-by":"crossref","unstructured":"Dragan, P., Merski, M., Wi\u015bniewski, S., Sanmukh, S.G., and Latek, D. (2023). Chemokine Receptors\u2014Structure-Based Virtual Screening Assisted by Machine Learning. Pharmaceutics, 15.","DOI":"10.3390\/pharmaceutics15020516"},{"key":"ref_20","doi-asserted-by":"crossref","unstructured":"Akiba, T., Sano, S., Yanase, T., Ohta, T., and Koyama, M. (2019, January 25). Optuna: A Next-Generation Hyperparameter Optimization Framework. Proceedings of the 25th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining, ACM, Anchorage AK, USA.","DOI":"10.1145\/3292500.3330701"},{"key":"ref_21","doi-asserted-by":"crossref","first-page":"3891","DOI":"10.1021\/acs.jcim.1c00203","article-title":"AutoDock Vina 1.2.0: New Docking Methods, Expanded Force Field, and Python Bindings","volume":"61","author":"Eberhardt","year":"2021","journal-title":"J. Chem. Inf. Model."},{"key":"ref_22","doi-asserted-by":"crossref","unstructured":"Latek, D., Rutkowska, E., Niewieczerzal, S., and Cielecka-Piontek, J. (2019). Drug-Induced Diabetes Type 2: In Silico Study Involving Class B GPCRs. PLoS ONE, 14.","DOI":"10.1371\/journal.pone.0208892"},{"key":"ref_23","doi-asserted-by":"crossref","unstructured":"Pasznik, P., Rutkowska, E., Niewieczerzal, S., Cielecka-Piontek, J., and Latek, D. (2019). Potential Off-Target Effects of Beta-Blockers on Gut Hormone Receptors: In Silico Study Including GUT-DOCK\u2014A Web Service for Small-Molecule Docking. PLoS ONE, 14.","DOI":"10.1371\/journal.pone.0210705"},{"key":"ref_24","doi-asserted-by":"crossref","first-page":"1120","DOI":"10.1016\/j.str.2014.06.012","article-title":"Advances in GPCR Modeling Evaluated by the GPCR Dock 2013 Assessment: Meeting New Challenges","volume":"22","author":"Kufareva","year":"2014","journal-title":"Structure"},{"key":"ref_25","unstructured":"(2025, February 23). Schr\u00f6dinger Release 2023-1: Maestro; Schr\u00f6dinger, LLC: New York, NY, USA, 2021. Available online: https:\/\/www.schrodinger.com\/products\/maestro."},{"key":"ref_26","doi-asserted-by":"crossref","first-page":"W387","DOI":"10.1093\/nar\/gky429","article-title":"GPCRM: A Homology Modeling Web Service with Triple Membrane-Fitted Quality Assessment of GPCR Models","volume":"46","author":"Miszta","year":"2018","journal-title":"Nucleic Acids Res."},{"key":"ref_27","doi-asserted-by":"crossref","unstructured":"Latek, D., Pasznik, P., Carlomagno, T., and Filipek, S. (2013). Towards Improved Quality of GPCR Models by Usage of Multiple Templates and Profile-Profile Comparison. PLoS ONE, 8.","DOI":"10.1371\/journal.pone.0056742"},{"key":"ref_28","doi-asserted-by":"crossref","first-page":"263","DOI":"10.1021\/acsptsci.9b00080","article-title":"Structure and Dynamics of Adrenomedullin Receptors AM 1 and AM 2 Reveal Key Mechanisms in the Control of Receptor Phenotype by Receptor Activity-Modifying Proteins","volume":"3","author":"Liang","year":"2020","journal-title":"ACS Pharmacol. Transl. Sci."},{"key":"ref_29","doi-asserted-by":"crossref","first-page":"656","DOI":"10.1016\/j.molcel.2020.01.012","article-title":"Toward a Structural Understanding of Class B GPCR Peptide Binding and Activation","volume":"77","author":"Liang","year":"2020","journal-title":"Mol. Cell"},{"key":"ref_30","doi-asserted-by":"crossref","first-page":"8918","DOI":"10.1038\/ncomms9918","article-title":"Autocrine Selection of a GLP-1R G-Protein Biased Agonist with Potent Antidiabetic Effects","volume":"6","author":"Zhang","year":"2015","journal-title":"Nat. Commun."},{"key":"ref_31","doi-asserted-by":"crossref","first-page":"2726","DOI":"10.1016\/j.jmb.2017.06.022","article-title":"Structure and Function of Peptide-Binding G Protein-Coupled Receptors","volume":"429","author":"Wu","year":"2017","journal-title":"J. Mol. Biol."},{"key":"ref_32","doi-asserted-by":"crossref","first-page":"15119","DOI":"10.1074\/jbc.M116.726620","article-title":"Differential Requirement of the Extracellular Domain in Activation of Class B G Protein-Coupled Receptors","volume":"291","author":"Zhao","year":"2016","journal-title":"J. Biol. Chem."},{"key":"ref_33","doi-asserted-by":"crossref","first-page":"550","DOI":"10.1124\/mol.119.115915","article-title":"Understanding Peptide Binding in Class A G Protein-Coupled Receptors","volume":"96","author":"Tikhonova","year":"2019","journal-title":"Mol. Pharmacol."},{"key":"ref_34","doi-asserted-by":"crossref","unstructured":"Krumm, B.E., and Grisshammer, R. (2015). Peptide Ligand Recognition by G Protein-Coupled Receptors. Front. Pharmacol., 6.","DOI":"10.3389\/fphar.2015.00048"},{"key":"ref_35","doi-asserted-by":"crossref","first-page":"742","DOI":"10.1021\/ci100050t","article-title":"Extended-Connectivity Fingerprints","volume":"50","author":"Rogers","year":"2010","journal-title":"J. Chem. Inf. Model."},{"key":"ref_36","unstructured":"Landrum, G., Tosco, P., Kelley, B., Cosgrove, D., Vianello, R., and Kawashima, E. (2025, February 23). Rdkit\/Rdkit: 2023_03_2 (Q1 2023) Release 2023. Available online: https:\/\/zenodo.org\/records\/8053810."},{"key":"ref_37","doi-asserted-by":"crossref","first-page":"204","DOI":"10.1021\/acs.jcim.9b00778","article-title":"Autodock Vina Adopts More Accurate Binding Poses but Autodock4 Forms Better Binding Affinity","volume":"60","author":"Nguyen","year":"2020","journal-title":"J. Chem. Inf. Model."},{"key":"ref_38","doi-asserted-by":"crossref","unstructured":"Mizera, M., and Latek, D. (2021). Ligand-Receptor Interactions and Machine Learning in GCGR and GLP-1R Drug Discovery. Int. J. Mol. Sci., 22.","DOI":"10.3390\/ijms22084060"},{"key":"ref_39","doi-asserted-by":"crossref","first-page":"677","DOI":"10.1021\/acschemneuro.8b00489","article-title":"Reliability of Docking-Based Virtual Screening for GPCR Ligands with Homology Modeled Structures: A Case Study of the Angiotensin II Type I Receptor","volume":"10","author":"Chen","year":"2019","journal-title":"ACS Chem. Neurosci."},{"key":"ref_40","doi-asserted-by":"crossref","first-page":"210","DOI":"10.1016\/j.copbio.2020.06.004","article-title":"Advances in G Protein-Coupled Receptor High-Throughput Screening","volume":"64","author":"Yasi","year":"2020","journal-title":"Curr. Opin. Biotechnol."},{"key":"ref_41","doi-asserted-by":"crossref","first-page":"C583","DOI":"10.1152\/ajpcell.00464.2021","article-title":"Recent Progress in Assays for GPCR Drug Discovery","volume":"323","author":"Guo","year":"2022","journal-title":"Am. J. Physiol.-Cell Physiol."}],"container-title":["International Journal of Molecular Sciences"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/1422-0067\/26\/5\/2160\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,9]],"date-time":"2025-10-09T16:44:03Z","timestamp":1760028243000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/1422-0067\/26\/5\/2160"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025,2,27]]},"references-count":41,"journal-issue":{"issue":"5","published-online":{"date-parts":[[2025,3]]}},"alternative-id":["ijms26052160"],"URL":"https:\/\/doi.org\/10.3390\/ijms26052160","relation":{"has-preprint":[{"id-type":"doi","id":"10.1101\/2024.10.28.620626","asserted-by":"object"}]},"ISSN":["1422-0067"],"issn-type":[{"value":"1422-0067","type":"electronic"}],"subject":[],"published":{"date-parts":[[2025,2,27]]}}}