{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,23]],"date-time":"2026-04-23T22:24:33Z","timestamp":1776983073860,"version":"3.51.4"},"reference-count":49,"publisher":"Oxford University Press (OUP)","issue":"4","license":[{"start":{"date-parts":[[2025,7,8]],"date-time":"2025-07-08T00:00:00Z","timestamp":1751932800000},"content-version":"vor","delay-in-days":7,"URL":"https:\/\/creativecommons.org\/licenses\/by-nc\/4.0\/"}],"funder":[{"name":"MHRD-UAY Project","award":["IITB_016 (2017)"],"award-info":[{"award-number":["IITB_016 (2017)"]}]},{"name":"MASSFIITB"},{"DOI":"10.13039\/501100001407","name":"Department of Biotechnology","doi-asserted-by":"publisher","award":["BT\/PR13114\/INF\/22\/206\/2015"],"award-info":[{"award-number":["BT\/PR13114\/INF\/22\/206\/2015"]}],"id":[{"id":"10.13039\/501100001407","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100004541","name":"Ministry of Education, India","doi-asserted-by":"publisher","id":[{"id":"10.13039\/501100004541","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2025,7,2]]},"abstract":"<jats:title>Abstract<\/jats:title>\n                  <jats:p>Drug design and development are central to clinical research, yet 90% of drugs fail to reach the clinic, often due to inappropriate selection of drug targets. Conventional methods for target identification lack precision and sensitivity. While various computational tools have been developed to predict the druggability of proteins, they often focus on limited subsets of the human proteome or rely solely on amino acid properties. Our study presents DrugProtAI, a tool developed by implementing a partitioning-based method and trained on the entire human protein set using both sequence- and non\u2013sequence-derived properties. The partitioned method was evaluated using popular machine learning algorithms, of which Random Forest and XGBoost performed the best. A comprehensive analysis of 183 features, encompassing biophysical, sequence-, and non\u2013sequence-derived properties, achieved a median Area Under Precision-Recall Curve (AUC) of 0.87 in target prediction. The model was further tested on a blinded validation set comprising recently approved drug targets. The key predictors were also identified, which we believe will help users in selecting appropriate drug targets. We believe that these insights are poised to significantly advance drug development. This version of the tool provides the probability of druggability for human proteins. The tool is freely accessible at https:\/\/drugprotai.pythonanywhere.com\/.<\/jats:p>","DOI":"10.1093\/bib\/bbaf330","type":"journal-article","created":{"date-parts":[[2025,6,20]],"date-time":"2025-06-20T08:00:13Z","timestamp":1750406413000},"source":"Crossref","is-referenced-by-count":5,"title":["DrugProtAI: A machine learning\u2013driven approach for predicting protein druggability through feature engineering and robust partition-based ensemble methods"],"prefix":"10.1093","volume":"26","author":[{"given":"Ankit","family":"Halder","sequence":"first","affiliation":[{"name":"Department of Biosciences and Bioengineering, Indian Institute of Technology Bombay , Powai, Mumbai 400076, Maharashtra ,","place":["India"]}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Sabyasachi","family":"Samantaray","sequence":"additional","affiliation":[{"name":"Department of Computer Science and Engineering, Indian Institute of Technology Bombay , Powai, Mumbai 400076, Maharashtra ,","place":["India"]}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Sahil","family":"Barbade","sequence":"additional","affiliation":[{"name":"Department of Civil Engineering, Indian Institute of Technology Bombay , Powai, Mumbai 400076, Maharashtra ,","place":["India"]}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Aditya","family":"Gupta","sequence":"additional","affiliation":[{"name":"Department of Mechanical Engineering, Indian Institute of Technology Bombay , Powai, Mumbai 400076, Maharashtra ,","place":["India"]}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-5159-6834","authenticated-orcid":false,"given":"Sanjeeva","family":"Srivastava","sequence":"additional","affiliation":[{"name":"Department of Biosciences and Bioengineering, Indian Institute of Technology Bombay , Powai, Mumbai 400076, Maharashtra ,","place":["India"]}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"286","published-online":{"date-parts":[[2025,7,8]]},"reference":[{"key":"2025070813005788000_ref1","doi-asserted-by":"publisher","first-page":"3049","DOI":"10.1016\/j.apsb.2022.02.002","article-title":"Why 90% of clinical drug development fails and how to improve it?","volume":"12","author":"Sun","year":"2022","journal-title":"Acta Pharmaceutica Sinica B"},{"key":"2025070813005788000_ref2","doi-asserted-by":"publisher","first-page":"1675","DOI":"10.1016\/S1359-6446(05)03624-X","article-title":"Predicting protein druggability","volume":"10","author":"Hajduk","year":"2005","journal-title":"Drug Discov Today"},{"key":"2025070813005788000_ref3","doi-asserted-by":"publisher","first-page":"2783","DOI":"10.3390\/ijms20112783","article-title":"A structure-based drug discovery paradigm","volume":"20","author":"Batool","year":"2019","journal-title":"IJMS"},{"key":"2025070813005788000_ref4","doi-asserted-by":"publisher","first-page":"583","DOI":"10.1038\/s41586-021-03819-2","article-title":"Highly accurate protein structure prediction with AlphaFold","volume":"596","author":"Jumper","year":"2021","journal-title":"Nature"},{"key":"2025070813005788000_ref5","doi-asserted-by":"publisher","first-page":"493","DOI":"10.1038\/s41586-024-07487-w","article-title":"Accurate structure prediction of biomolecular interactions with AlphaFold 3","volume":"630","author":"Abramson","year":"2024","journal-title":"Nature"},{"key":"2025070813005788000_ref6","doi-asserted-by":"publisher","first-page":"1156","DOI":"10.1016\/j.biocel.2007.02.018","article-title":"Druggability of human disease genes","volume":"39","author":"Sakharkar","year":"2007","journal-title":"Int J Biochem Cell Biol"},{"key":"2025070813005788000_ref7","doi-asserted-by":"publisher","first-page":"1291","DOI":"10.1038\/s42003-022-04245-4","article-title":"DrugnomeAI is an ensemble machine-learning framework for predicting druggability of candidate drug targets","volume":"5","author":"Raies","year":"2022","journal-title":"Commun Biol"},{"key":"2025070813005788000_ref8","doi-asserted-by":"publisher","first-page":"e0117955","DOI":"10.1371\/journal.pone.0117955","article-title":"Properties of protein drug target classes","volume":"10","author":"Bull","year":"2015","journal-title":"PloS One"},{"key":"2025070813005788000_ref9","doi-asserted-by":"publisher","first-page":"451","DOI":"10.1093\/bioinformatics\/btp002","article-title":"Properties and identification of human protein drug targets","volume":"25","author":"Bakheet","year":"2009","journal-title":"Bioinformatics"},{"key":"2025070813005788000_ref10","doi-asserted-by":"publisher","first-page":"eaag1166","DOI":"10.1126\/scitranslmed.aag1166","article-title":"The druggable genome and support for target identification and validation in drug development","volume":"9","author":"Finan","year":"2017","journal-title":"Sci Transl Med"},{"key":"2025070813005788000_ref11","doi-asserted-by":"publisher","first-page":"915","DOI":"10.17179\/excli2023-6410","article-title":"Empirical comparison and analysis of machine learning-based approaches for druggable protein identification","volume":"22","author":"Shoombuatong","year":"2023","journal-title":"EXCLI J"},{"key":"2025070813005788000_ref12","doi-asserted-by":"publisher","first-page":"104883","DOI":"10.1016\/j.isci.2022.104883","article-title":"Computational prediction and interpretation of druggable proteins using a stacked ensemble-learning framework","volume":"25","author":"Charoenkwan","year":"2022","journal-title":"iScience"},{"key":"2025070813005788000_ref13","doi-asserted-by":"publisher","first-page":"35","DOI":"10.1016\/j.artmed.2019.07.005","article-title":"Accurate prediction of potential druggable proteins based on genetic algorithm and bagging-SVM ensemble classifier","volume":"98","author":"Lin","year":"2019","journal-title":"Artif Intell Med"},{"key":"2025070813005788000_ref14","doi-asserted-by":"publisher","DOI":"10.1093\/bioinformatics\/btaf360","article-title":"DrugTar Improves Druggability Prediction by Integrating Large Language Models and Gene Ontologies","volume-title":"Bioinformatics","author":"Borhani"},{"key":"2025070813005788000_ref15","doi-asserted-by":"publisher","first-page":"69","DOI":"10.1016\/j.ddtec.2021.06.007","article-title":"Recent advances in mass-spectrometry based proteomics software, tools and databases","volume":"39","author":"Halder","year":"2021","journal-title":"Drug Discov Today Technol"},{"key":"2025070813005788000_ref16","doi-asserted-by":"publisher","first-page":"115D","DOI":"10.1093\/nar\/gkh131","article-title":"UniProt: the universal protein knowledgebase","volume":"32","author":"Apweiler","year":"2004","journal-title":"Nucleic Acids Res"},{"key":"2025070813005788000_ref17","doi-asserted-by":"publisher","first-page":"D1265","DOI":"10.1093\/nar\/gkad976","article-title":"DrugBank 6.0: the DrugBank Knowledgebase for 2024","volume":"52","author":"Knox","year":"2024","journal-title":"Nucleic Acids Res"},{"key":"2025070813005788000_ref21","doi-asserted-by":"publisher","first-page":"1123","DOI":"10.1126\/science.ade2574","article-title":"Evolutionary-scale prediction of atomic-level protein structure with a language model","volume":"379","author":"Lin","year":"2023","journal-title":"Science"},{"key":"2025070813005788000_ref22","doi-asserted-by":"publisher","first-page":"382","DOI":"10.1080\/02763869.2020.1826228","article-title":"PubMed 2.0","volume":"39","author":"White","year":"2020","journal-title":"Med Ref Serv Q"},{"key":"2025070813005788000_ref23","doi-asserted-by":"publisher","first-page":"771808","DOI":"10.3389\/fphar.2021.771808","article-title":"DrugHybrid_BS: using hybrid feature combined with bagging-SVM to predict potentially druggable proteins","volume":"12","author":"Gong","year":"2021","journal-title":"Front Pharmacol"},{"key":"2025070813005788000_ref24","doi-asserted-by":"publisher","first-page":"145","DOI":"10.1186\/s12859-024-05744-3","article-title":"DPI_CDF: druggable protein identifier using cascade deep forest","volume":"25","author":"Arif","year":"2024","journal-title":"BMC Bioinformatics"},{"key":"2025070813005788000_ref25","doi-asserted-by":"publisher","first-page":"718","DOI":"10.1016\/j.drudis.2016.01.007","article-title":"DrugMiner: comparative analysis of machine learning algorithms for prediction of potential druggable proteins","volume":"21","author":"Jamali","year":"2016","journal-title":"Drug Discov Today"},{"key":"2025070813005788000_ref26","doi-asserted-by":"publisher","first-page":"106276","DOI":"10.1016\/j.compbiomed.2022.106276","article-title":"Druggable protein prediction using a multi-canal deep convolutional neural network based on autocovariance method","volume":"151","author":"Iraji","year":"2022","journal-title":"Comput Biol Med"},{"key":"2025070813005788000_ref27","doi-asserted-by":"publisher","first-page":"839","DOI":"10.1038\/s41573-021-00252-y","article-title":"Trends in kinase drug discovery: targets, indications and inhibitor design","volume":"20","author":"Attwood","year":"2021","journal-title":"Nat Rev Drug Discov"},{"key":"2025070813005788000_ref28","doi-asserted-by":"publisher","first-page":"652","DOI":"10.1002\/pro.2449","article-title":"Contribution of hydrogen bonds to protein stability","volume":"23","author":"Pace","year":"2014","journal-title":"Protein Sci"},{"key":"2025070813005788000_ref29","doi-asserted-by":"publisher","first-page":"84","DOI":"10.1016\/j.ymeth.2010.05.006","article-title":"Protein structure prediction: do hydrogen bonding and water-mediated interactions suffice?","volume":"52","author":"Oklejas","year":"2010","journal-title":"Methods"},{"key":"2025070813005788000_ref30","doi-asserted-by":"publisher","first-page":"310","DOI":"10.1002\/prot.20613","article-title":"Cooperative effects in hydrogen-bonding of protein secondary structure elements: a systematic analysis of crystal data using Secbase","volume":"61","author":"Koch","year":"2005","journal-title":"Proteins"},{"key":"2025070813005788000_ref31","doi-asserted-by":"publisher","first-page":"9","DOI":"10.2165\/11530550-000000000-00000","article-title":"Glycosylation of therapeutic proteins: an effective strategy to optimize efficacy","volume":"24","author":"Sol\u00e1","year":"2010","journal-title":"BioDrugs"},{"key":"2025070813005788000_ref32","doi-asserted-by":"publisher","first-page":"757","DOI":"10.1016\/j.trecan.2020.04.002","article-title":"Targeting glycosylation: a new road for cancer drug discovery","volume":"6","author":"Costa","year":"2020","journal-title":"Trends in Cancer"},{"key":"2025070813005788000_ref33","doi-asserted-by":"publisher","first-page":"911","DOI":"10.3390\/cancers14040911","article-title":"Glycans as targets for drug delivery in cancer","volume":"14","author":"Diniz","year":"2022","journal-title":"Cancers"},{"key":"2025070813005788000_ref34","doi-asserted-by":"publisher","first-page":"217","DOI":"10.1038\/s41573-020-00093-1","article-title":"The clinical impact of glycobiology: targeting selectins, Siglecs and mammalian glycans","volume":"20","author":"Smith","year":"2021","journal-title":"Nat Rev Drug Discov"},{"key":"2025070813005788000_ref35","doi-asserted-by":"publisher","first-page":"30","DOI":"10.1186\/s13321-020-00437-4","article-title":"Aromatic clusters in protein\u2013protein and protein\u2013drug complexes","volume":"12","author":"Lanzarotti","year":"2020","journal-title":"J Chem"},{"key":"2025070813005788000_ref36","doi-asserted-by":"publisher","first-page":"17","DOI":"10.1002\/pmic.201600391","article-title":"From structure to redox: the diverse functional roles of disulfides and implications in disease","volume":"17","author":"Bechtel","year":"2017","journal-title":"Proteomics"},{"key":"2025070813005788000_ref37","doi-asserted-by":"publisher","first-page":"17","DOI":"10.1007\/s40256-024-00694-w","article-title":"Sotatercept: the first FDA-approved activin A receptor IIA inhibitor used in the management of pulmonary arterial hypertension","volume":"25","author":"Villanueva","year":"2025","journal-title":"Am J Cardiovasc Drugs"},{"key":"2025070813005788000_ref38","doi-asserted-by":"publisher","first-page":"283","DOI":"10.1016\/0014-5793(93)80889-3","article-title":"Cantharidin, another natural toxin that inhibits the activity of serine\/threonine protein phosphatases types 1 and 2A","volume":"330","author":"Honkanen","year":"1993","journal-title":"FEBS Lett"},{"key":"2025070813005788000_ref39","doi-asserted-by":"publisher","first-page":"95","DOI":"10.1007\/s40272-023-00600-y","article-title":"Cantharidin topical solution 0.7%: first approval","volume":"26","author":"Keam","year":"2024","journal-title":"Pediatr Drugs"},{"key":"2025070813005788000_ref40","doi-asserted-by":"publisher","first-page":"11556","DOI":"10.1200\/JCO.2024.42.16_suppl.11556","article-title":"Efficacy of nirogacestat in participants with poor prognostic factors for desmoid tumors: analyses from the randomized phase 3 DeFi study","volume":"42","author":"Vincenzi","year":"2024","journal-title":"JCO"},{"key":"2025070813005788000_ref41","doi-asserted-by":"publisher","first-page":"1544","DOI":"10.1242\/dev.143255","article-title":"IFT56 regulates vertebrate developmental patterning by maintaining IFTB complex integrity and ciliary microtubule architecture","volume":"144","author":"Xin","year":"2017","journal-title":"Development"},{"key":"2025070813005788000_ref42","doi-asserted-by":"publisher","first-page":"2723","DOI":"10.3390\/molecules27092723","article-title":"Drug repurposing for COVID-19: a review and a novel strategy to identify new targets and potential drug candidates","volume":"27","author":"Rodrigues","year":"2022","journal-title":"Molecules"},{"key":"2025070813005788000_ref43","doi-asserted-by":"publisher","first-page":"1829","DOI":"10.1007\/s13204-021-02063-4","article-title":"Effective treatment of imbalanced datasets in health care using modified SMOTE coupled with stacked deep learning algorithms","volume":"13","author":"Sowjanya","year":"2023","journal-title":"Appl Nanosci"},{"key":"2025070813005788000_ref44","doi-asserted-by":"publisher","first-page":"e0296107","DOI":"10.1371\/journal.pone.0296107","article-title":"Novel ensemble learning approach with SVM-imputed ADASYN features for enhanced cervical cancer prediction","volume":"19","author":"Munshi","year":"2024","journal-title":"PloS One"},{"key":"2025070813005788000_ref45","doi-asserted-by":"publisher","first-page":"373","DOI":"10.5662\/wjm.v13.i5.373","article-title":"Challenges and limitations of synthetic minority oversampling techniques in machine learning","volume":"13","author":"Alkhawaldeh","year":"2023","journal-title":"World J Methodol"},{"key":"2025070813005788000_ref46","doi-asserted-by":"publisher","first-page":"5","DOI":"10.1023\/A:1010933404324","article-title":"No title found","volume":"45","author":"Breiman","year":"2001","journal-title":"Machine Learning"},{"key":"2025070813005788000_ref47","doi-asserted-by":"publisher","DOI":"10.1145\/2939672.2939785","volume-title":"KDD '16: Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining","author":"Chen","year":"2016"},{"key":"2025070813005788000_ref48","doi-asserted-by":"publisher","first-page":"725","DOI":"10.1016\/j.procs.2016.05.259","article-title":"Classifier ensemble design for imbalanced data classification: a hybrid approach","volume":"85","author":"Salunkhe","year":"2016","journal-title":"Procedia Computer Science"},{"key":"2025070813005788000_ref49","doi-asserted-by":"publisher","first-page":"82","DOI":"10.1186\/s12911-022-01821-w","article-title":"Solving the class imbalance problem using ensemble algorithm: application of screening for aortic dissection","volume":"22","author":"Liu","year":"2022","journal-title":"BMC Med Inform Decis Mak"},{"key":"2025070813005788000_ref50","doi-asserted-by":"publisher","first-page":"8091","DOI":"10.1007\/s11042-020-10139-6","article-title":"A review on genetic algorithm: past, present, and future","volume":"80","author":"Katoch","year":"2021","journal-title":"Multimed Tools Appl"},{"key":"2025070813005788000_ref51","doi-asserted-by":"publisher","volume-title":"NIPS'17: Proceedings of the 31st International Conference on Neural Information Processing Systems","author":"Lundberg","DOI":"10.48550\/ARXIV.1705.07874"},{"key":"2025070813005788000_ref52","doi-asserted-by":"publisher","first-page":"356","DOI":"10.1186\/s12859-020-03693-1","article-title":"Game theoretic centrality: a novel approach to prioritize disease candidate genes by combining biological networks with the Shapley value","volume":"21","author":"Sun","year":"2020","journal-title":"BMC Bioinformatics"}],"container-title":["Briefings in Bioinformatics"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/academic.oup.com\/bib\/article-pdf\/26\/4\/bbaf330\/63704623\/bbaf330.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"syndication"},{"URL":"https:\/\/academic.oup.com\/bib\/article-pdf\/26\/4\/bbaf330\/63704623\/bbaf330.pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,7,8]],"date-time":"2025-07-08T13:01:03Z","timestamp":1751979663000},"score":1,"resource":{"primary":{"URL":"https:\/\/academic.oup.com\/bib\/article\/doi\/10.1093\/bib\/bbaf330\/8193855"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025,7]]},"references-count":49,"journal-issue":{"issue":"4","published-print":{"date-parts":[[2025,7,2]]}},"URL":"https:\/\/doi.org\/10.1093\/bib\/bbaf330","relation":{},"ISSN":["1467-5463","1477-4054"],"issn-type":[{"value":"1467-5463","type":"print"},{"value":"1477-4054","type":"electronic"}],"subject":[],"published-other":{"date-parts":[[2025,7]]},"published":{"date-parts":[[2025,7]]},"article-number":"bbaf330"}}