{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,24]],"date-time":"2026-04-24T18:52:57Z","timestamp":1777056777914,"version":"3.51.4"},"reference-count":86,"publisher":"MDPI AG","issue":"8","license":[{"start":{"date-parts":[[2021,4,11]],"date-time":"2021-04-11T00:00:00Z","timestamp":1618099200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100001871","name":"Funda\u00e7\u00e3o para a Ci\u00eancia e a Tecnologia","doi-asserted-by":"publisher","award":["UID\/QUI\/50006\/2020"],"award-info":[{"award-number":["UID\/QUI\/50006\/2020"]}],"id":[{"id":"10.13039\/501100001871","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["IJMS"],"abstract":"<jats:p>AKT, is a serine\/threonine protein kinase comprising three isoforms\u2014namely: AKT1, AKT2 and AKT3, whose inhibitors have been recognized as promising therapeutic targets for various human disorders, especially cancer. In this work, we report a systematic evaluation of multi-target Quantitative Structure-Activity Relationship (mt-QSAR) models to probe AKT\u2019 inhibitory activity, based on different feature selection algorithms and machine learning tools. The best predictive linear and non-linear mt-QSAR models were found by the genetic algorithm-based linear discriminant analysis (GA-LDA) and gradient boosting (Xgboost) techniques, respectively, using a dataset containing 5523 inhibitors of the AKT isoforms assayed under various experimental conditions. The linear model highlighted the key structural attributes responsible for higher inhibitory activity whereas the non-linear model displayed an overall accuracy higher than 90%. Both these predictive models, generated through internal and external validation methods, were then used for screening the Asinex kinase inhibitor library to identify the most potential virtual hits as pan-AKT inhibitors. The virtual hits identified were then filtered by stepwise analyses based on reverse pharmacophore-mapping based prediction. Finally, results of molecular dynamics simulations were used to estimate the theoretical binding affinity of the selected virtual hits towards the three isoforms of enzyme AKT. Our computational findings thus provide important guidelines to facilitate the discovery of novel AKT inhibitors.<\/jats:p>","DOI":"10.3390\/ijms22083944","type":"journal-article","created":{"date-parts":[[2021,4,12]],"date-time":"2021-04-12T03:04:06Z","timestamp":1618196646000},"page":"3944","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":21,"title":["AKT Inhibitors: The Road Ahead to Computational Modeling-Guided Discovery"],"prefix":"10.3390","volume":"22","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-4818-9047","authenticated-orcid":false,"given":"Amit Kumar","family":"Halder","sequence":"first","affiliation":[{"name":"LAQV@REQUIMTE\/Faculty of Sciences, University of Porto, Rua do Campo Alegre, s\/n, 4169-007 Porto, Portugal"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-3375-8670","authenticated-orcid":false,"given":"M. Nat\u00e1lia D. S.","family":"Cordeiro","sequence":"additional","affiliation":[{"name":"LAQV@REQUIMTE\/Faculty of Sciences, University of Porto, Rua do Campo Alegre, s\/n, 4169-007 Porto, Portugal"}]}],"member":"1968","published-online":{"date-parts":[[2021,4,11]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"389","DOI":"10.1515\/bmc.2010.035","article-title":"The AKT isoforms, their unique functions and potential as anticancer therapeutic targets","volume":"1","author":"Santi","year":"2010","journal-title":"Biomol. Concepts"},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"154","DOI":"10.1186\/s12964-019-0450-3","article-title":"Distinct functions of AKT isoforms in breast cancer: A comprehensive review","volume":"17","author":"Hinz","year":"2019","journal-title":"Cell Commun. 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