{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,10,30]],"date-time":"2025-10-30T12:15:52Z","timestamp":1761826552401,"version":"build-2065373602"},"reference-count":0,"publisher":"Sociedade Brasileira de Quimica (SBQ)","content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["J. Braz. Chem. Soc."],"abstract":"<jats:p>Neuropsychiatric disorders often involve dysregulation of serotonergic and dopaminergic\npathways. This study applied machine learning (ML) with quantitative structure-activity\nrelationship  (QSAR) methods to predict the bioactivity (-log half-maximal inhibitory\nconcentration  (pIC50)) of compounds targeting both receptor families, aiming to identify\nmultitarget inhibitors among US Food and Drug Administration (FDA)-approved drugs. A dataset\nof 5,628 compounds with experimental IC50 values was obtained from ChEMBL and encoded\nwith PubChem fingerprints. Random Forest and Extreme Gradient Boosting models were trained,\noptimized, and evaluated with 5-fold cross-validation, and Shapley Additive Explanations (SHAP)\nvalues were used for interpretation. After outlier removal and descriptor selection, models achieved\ncoefficient of determination (R2) test ca. 0.69 and were used to screen over 1,500 approved\ndrugs. A total of 162 were predicted to have dual bioactivity (pIC50 &gt; 6, coefficient of variation &lt;\n1%), including antipsychotics, adrenergic agonists (e.g., epinephrine), dopamine agonists\n(e.g., levodopa), antihistamines (e.g., cyproheptadine), antiemetics (e.g., droperidol), ergot alkaloids (e.g., ergotamine), antibiotics (e.g., penicillin G), and lipid-lowering agents (e.g., pravastatin). Key molecular descriptors indicated the relevance of nitrogen-containing fragments and conjugated aromatic substructures for dual receptor binding. These results provide a computational framework for repurposing drugs and guiding experimental validation in neuropsychiatric research.<\/jats:p>","DOI":"10.21577\/0103-5053.20250164","type":"journal-article","created":{"date-parts":[[2025,10,30]],"date-time":"2025-10-30T12:10:09Z","timestamp":1761826209000},"source":"Crossref","is-referenced-by-count":0,"title":["Integration of Machine Learning, QSAR, and Polypharmacology for Multitarget Drug Discovery in Neuropsychiatric Disorders: Prediction of Serotonergic and Dopaminergic Receptor Inhibitors"],"prefix":"10.21577","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-2789-3083","authenticated-orcid":false,"given":"Caroline","family":"M. Folchini","sequence":"first","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0001-6642-3928","authenticated-orcid":false,"given":"Alexandre","family":"F. Cobre","sequence":"additional","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0001-8510-1473","authenticated-orcid":false,"given":"Karime","family":"Z. A. Domingues","sequence":"additional","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0001-9951-587X","authenticated-orcid":false,"given":"Luana","family":"M. Ferreira","sequence":"additional","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0001-5918-4738","authenticated-orcid":false,"given":"Mariana","family":"M. Fachi","sequence":"additional","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0002-7049-4363","authenticated-orcid":false,"given":"Roberto","family":"Pontarolo","sequence":"additional","affiliation":[]}],"member":"9021","published-online":{"date-parts":[[2025]]},"container-title":["Journal of the Brazilian Chemical Society"],"original-title":[],"link":[{"URL":"https:\/\/jbcs.sbq.org.br\/pdf\/2025-0223FP","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,30]],"date-time":"2025-10-30T12:10:09Z","timestamp":1761826209000},"score":1,"resource":{"primary":{"URL":"https:\/\/jbcs.sbq.org.br\/audiencia_pdf.asp?aid2=13023&nomeArquivo=2025-0223FP.pdf"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025]]},"references-count":0,"URL":"https:\/\/doi.org\/10.21577\/0103-5053.20250164","relation":{},"ISSN":["0103-5053","1678-4790"],"issn-type":[{"value":"0103-5053","type":"print"},{"value":"1678-4790","type":"electronic"}],"subject":[],"published":{"date-parts":[[2025]]}}}