eng Oriental Scientific Publishing Company Biomedical and Pharmacology Journal 0974-6242 2025-12-30 18 4 2909 2921 10.13005/bpj/3304 68755 article Sivasankari Venkatachalam 1 Manivannan Ekambaram 1 Hemalatha Selvaraj 2 Arbind Kumar Choudhary 3 Pathon Feroz Khan 4 Department of Pharmacology, Vinayaka Mission’s Kirupananda Variyar Medical College, Vinayaka Mission’s Research Foundation (Deemed to be University), Salem, Tamil Nadu, India Faculty of Pharmacy Karpagam Academy of Higher Education, Coimbatore, Tamil Nadu, India Department of Pharmacology, Government Erode Medical College and Hospital, Erode, Tamil Nadu, India Department of Anatomy, Government Erode Medical College and Hospital, Erode, Tamil Nadu, India The serotonin 5-HT₂A receptor is a central target in neuropsychopharmacology, regulating cognition, perception, and mood, and mediating the effects of many psychotropic agents. Tryptamine derivatives, owing to their structural resemblance to serotonin, display strong receptor affinity and provide a rational framework for central nervous system (CNS) drug design. In this study, a machine learning–driven quantitative structure–activity relationship (QSAR) model was developed to predict the psychotomimetic potency (pKi) of 50 tryptamine analogues using four molecular descriptors: molecular weight (MW), lipophilicity (LogP), topological polar surface area (TPSA), and dipole moment (DM). Multiple regression models were assessed, including Linear, Ridge, Partial Least Squares, and Random Forest. Among these, the Random Forest algorithm produced the highest predictive accuracy, achieving a test set R² of 0.79 and RMSE of 0.50, with feature importance analysis identifying TPSA and LogP as the most influential determinants of receptor binding. Diagnostic plots confirmed the absence of outliers and validated the model’s applicability domain. This approach highlights the role of polarity and lipophilicity in serotonergic drug design while demonstrating the utility of ensemble learning for QSAR prediction. Future extensions of this work should focus on expanding the chemical dataset, integrating three-dimensional descriptors, and experimentally validating top-predicted ligands to enhance translational impact in CNS drug discovery https://biomedpharmajournal.org/vol18no4/pharmacophoric-determinants-of-5-ht%e2%82%82a-agonism-a-machine-learning-based-qsar-study-of-tryptamine-derivatives-using-random-forest-for-cns-drug-design/ Central Nervous System Machine Learning pKi Prediction QSAR Random Forest Serotonin Receptor Tryptamine Derivatives