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This study combines protein-versus-ligand matching molecular docking (MD) data extracted from 10 independent molecular docking (MD) evaluations \u2014 ADFR, DOCK, Gemdock, Ledock, Plants, Psovina, Quickvina2, smina, vina, and vinaxb to identify top MRSA drug candidates. Twenty-nine active protein targets (APT) from the enhanced DUD-E repository (<jats:ext-link xmlns:xlink=\"http:\/\/www.w3.org\/1999\/xlink\" ext-link-type=\"uri\" xlink:href=\"http:\/\/DUD-E.decoys.org\">http:\/\/DUD-E.decoys.org<\/jats:ext-link>) are matched against 1040 ligands using \u201cforward modeling\u201d machine learning for initial \u201cdata mining and modeling\u201d (DDM) to extract PPTs and the corresponding high affinity ligands (HALs). K-means clustering (KMC) is then performed on 400 ligands matched against 29 PTs, with each cluster accommodating HALs, and the corresponding PPTs. Performance of KMC is then validated against randomly chosen head, tail, and middle active ligands (ALs). KMC outcomes have been validated against two other clustering methods, namely, Gaussian mixture model (GMM) and density based spatial clustering of applications with noise (DBSCAN). While GMM shows similar results as with KMC, DBSCAN has failed to yield more than one cluster and handle the noise (outliers), thus affirming the choice of KMC or GMM. Databases obtained from ADFR to mine PPTs are then ranked according to the number of the corresponding HAL-PPT combinations (HPC) inside the derived clusters, an approach called \u201creverse modeling\u201d (RM). From the set of 29 PTs studied, RM predicts high fidelity of 5 PPTs (17%) that bind with 76 out of 400, i.e., 19% ligands leading to a prediction of next-generation MRSA drug candidates: <jats:italic>PPT2<\/jats:italic> (average HPC is 41.1%) is the top choice, followed by <jats:italic>PPT14<\/jats:italic> (average HPC 25.46%), and then <jats:italic>PPT15<\/jats:italic> (average HPC 23.12%). This algorithm can be generically implemented irrespective of pathogenic forms and is particularly effective for sparse data.<\/jats:p>\n                <jats:p><jats:bold>Graphical Abstract<\/jats:bold><\/jats:p>","DOI":"10.1007\/s11517-023-02893-0","type":"journal-article","created":{"date-parts":[[2023,8,22]],"date-time":"2023-08-22T23:02:26Z","timestamp":1692745346000},"page":"3035-3048","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":2,"title":["Extracting prime protein targets as possible drug candidates: machine learning evaluation"],"prefix":"10.1007","volume":"61","author":[{"given":"Subhagata","family":"Chattopadhyay","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Nhat Phuong","family":"Do","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Darren R.","family":"Flower","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-5499-6008","authenticated-orcid":false,"given":"Amit K.","family":"Chattopadhyay","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2023,8,23]]},"reference":[{"key":"2893_CR1","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1155\/2020\/4516250","volume":"2020","author":"X Zhan","year":"2020","unstructured":"Zhan X, You Z, Yu C, Li L, Pan J (2020) Ensemble learning prediction of drug-target interactions using GIST descriptor extracted from PSSM-based evolutionary information. 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