QSAR-Guided Design of Serotonin Transporter Inhibitors Supported by Molecular Docking and Biased Molecular Dynamics

Background/Objectives: Serotonin transporter (SERT) inhibition represents a central pharmacological strategy in the treatment of major depressive disorder. In this study, an integrated computational framework combining quantitative structure-activity relationship (QSAR) modeling, molecular docking analysis, and in silico ADMET profiling was applied to identify and prioritize novel candidate structures. Methods: Conformation-independent QSAR models were developed using local molecular graph invariants and SMILES-based descriptors optimized through a Monte Carlo learning procedure, while a genetic algorithm-multiple linear regression (GA-MLR) was employed to derive statistically robust predictive models from a large descriptor pool. Model quality, robustness, and external predictivity were rigorously evaluated using multiple statistical validation criteria. In parallel, a field-based contribution analysis was applied to construct a three-dimensional QSAR model, enabling spatial interpretation of structure-activity relationships. Fragment-level contributions associated with activity enhancement or attenuation were subsequently identified and used to design new candidate inhibitor structures. Results: The designed compounds were further evaluated by molecular docking, InducedFit Docking and Binding Pose MetaDynamics (BPMD) into the SERT binding site, providing a structure-based assessment consistent with the trends observed in QSAR modeling. In addition, in silico ADMET analysis was performed to assess key pharmacokinetic and safety-related properties relevant to central nervous system drug development. Conclusions: The proposed workflow demonstrates the utility of combining data-driven QSAR modeling with structure-based and pharmacokinetic considerations to rationalize and prioritize novel serotonin transporter-focused scaffold optimization, offering a transferable strategy for early-stage antidepressant drug discovery.