{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,11,28]],"date-time":"2025-11-28T12:01:23Z","timestamp":1764331283160,"version":"build-2065373602"},"reference-count":108,"publisher":"Bentham Science Publishers Ltd.","issue":"18","content-domain":{"domain":["eurekaselect.com"],"crossmark-restriction":true},"short-container-title":["CTMC"],"published-print":{"date-parts":[[2025,7]]},"abstract":"<jats:sec>\n                    <jats:title>Background:<\/jats:title>\n                    <jats:p>Cancers are complex multi-genetic diseases that should be tackled in\nmulti-target drug discovery scenarios. Computational methods are of great importance to accelerate\nthe discovery of multi-target anticancer agents. Here, we employed a ligand-based approach\nby combining a perturbation-theory machine learning model derived from an ensemble of\nmultilayer perceptron networks (PTML-EL-MLP) with the Fragment-Based Topological Design\n(FBTD) approach to rationally design and predict triple-target inhibitors against the cancerrelated\nproteins named Tropomyosin Receptor Kinase A (TRKA), poly[ADP-ribose] polymerase\n1 (PARP-1), and Insulin-like Growth Factor 1 Receptor (IGF1R).<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Methods:<\/jats:title>\n                    <jats:p>We extracted the chemical and biological data from ChEMBL. We applied the Box-\nJenkins approach to generate multi-label topological indices and subsequently created the\nPTML-EL-MLP model.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Results:<\/jats:title>\n                    <jats:p>Our PTML-EL-MLP model exhibited an accuracy of around 80%. The application\nFBTD permitted the physicochemical and structural interpretation of the PTML-EL-MLP model,\nthus enabling a) the chemistry-driven analysis of different molecular fragments with a positive\ninfluence on the multi-target activity and b) the use of those favorable fragments as building\nblocks to virtually design four new drug-like molecules. The designed molecules were predicted\nas triple-target inhibitors against the aforementioned cancer-related proteins.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Conclusion:<\/jats:title>\n                    <jats:p>Our study envisages the capabilities of combining PTML modeling with FBTD for\nthe generation of new chemical diversity for multi-target drug discovery in oncology research\nand beyond.<\/jats:p>\n                  <\/jats:sec>","DOI":"10.2174\/0115680266325897240815112505","type":"journal-article","created":{"date-parts":[[2024,8,22]],"date-time":"2024-08-22T06:33:28Z","timestamp":1724308408000},"page":"2179-2195","update-policy":"https:\/\/doi.org\/10.2174\/bsp_crossmark_policy","source":"Crossref","is-referenced-by-count":2,"title":["Ligand-Based Approach for Multi-Target Drug Discovery: PTML Modeling of Triple-Target Inhibitors"],"prefix":"10.2174","volume":"25","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-1928-853X","authenticated-orcid":true,"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":true,"given":"M. Natalia 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":true,"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":"965","reference":[{"key":"ref=1","doi-asserted-by":"publisher","first-page":"209","DOI":"10.3322\/caac.21660","volume":"71","author":"Sung H.","year":"2021","unstructured":"Sung H.; Ferlay J.; Siegel R.L.; Laversanne M.; Soerjomataram I.; Jemal A.; Bray F.; Global Cancer Statistics 2020: GLOBOCAN Estimates of Incidence and Mortality Worldwide for 36 Cancers in 185 Countries. 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