{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,12,23]],"date-time":"2025-12-23T10:26:54Z","timestamp":1766485614654,"version":"build-2065373602"},"reference-count":86,"publisher":"MDPI AG","issue":"20","license":[{"start":{"date-parts":[[2024,10,14]],"date-time":"2024-10-14T00:00:00Z","timestamp":1728864000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"Foundation for Science and Technology\/the Ministry of Science, Technology and Higher Education of the Government of Portugal","award":["UIDB\/50006\/2020"],"award-info":[{"award-number":["UIDB\/50006\/2020"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Applied Sciences"],"abstract":"<jats:p>Lung cancer is the most diagnosed malignant neoplasm worldwide and it is associated with great mortality. Currently, developing antineoplastic agents is a challenging, time-consuming, and costly process. Computational methods can speed up the early discovery of anti-lung-cancer chemicals. Here, we report a perturbation theory machine learning model based on a multilayer perceptron (PTML-MLP) model for phenotypic early antineoplastic drug discovery, enabling the rational design and prediction of new molecules as virtual versatile inhibitors of multiple lung cancer cell lines. The PTML-MLP model achieved an accuracy above 80%. We applied the fragment-based topological design (FBTD) approach to physicochemically and structurally interpret the PTML-MLP model. This enabled the extraction of suitable fragments with a positive influence on anti-lung-cancer activity against the different lung cancer cell lines. By following the aforementioned interpretations, we could assemble several suitable fragments to design four novel molecules, which were predicted by the PTML-MLP model as versatile anti-lung-cancer agents. Such predictions of potent multi-cellular anticancer activity against diverse lung cancer cell lines were rigorously confirmed by a well-established virtual screening tool reported in the literature. The present work envisages new opportunities for the application of PTML models to accelerate early antineoplastic discovery from phenotypic assays.<\/jats:p>","DOI":"10.3390\/app14209344","type":"journal-article","created":{"date-parts":[[2024,10,17]],"date-time":"2024-10-17T04:42:15Z","timestamp":1729140135000},"page":"9344","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":12,"title":["Perturbation Theory Machine Learning Model for Phenotypic Early Antineoplastic Drug Discovery: Design of Virtual Anti-Lung-Cancer Agents"],"prefix":"10.3390","volume":"14","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-1928-853X","authenticated-orcid":false,"given":"Valeria V.","family":"Kleandrova","sequence":"first","affiliation":[{"name":"LAQV@REQUIMTE\/Department of Chemistry and Biochemistry, Faculty of Sciences, University of Porto, 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\/Department of Chemistry and Biochemistry, Faculty of Sciences, University of Porto, 4169-007 Porto, Portugal"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-9544-9016","authenticated-orcid":false,"given":"Alejandro","family":"Speck-Planche","sequence":"additional","affiliation":[{"name":"LAQV@REQUIMTE\/Department of Chemistry and Biochemistry, Faculty of Sciences, University of Porto, 4169-007 Porto, Portugal"}]}],"member":"1968","published-online":{"date-parts":[[2024,10,14]]},"reference":[{"key":"ref_1","unstructured":"Global Burden of Disease 2019 Cancer Collaboration, Kocarnik, J.M., Compton, K., Dean, F.E., Fu, W., Gaw, B.L., Harvey, J.D., Henrikson, H.J., Lu, D., and Pennini, A. (2022). 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