Identification of novel serotonin transporter (SERT) inhibitors via large-scale machine learning-based virtual...

The serotonin transporter (SERT) plays a pivotal role in inflammatory responses and is a central therapeutic target for depression. Consequently, identifying potent SERT inhibitors remains a high priority in early-stage drug discovery. In this study, we evaluated twelve regression models, among which LightGBM, Random Forest, and XGBoost exhibited superior predictive performance, yielding R² values of 0.7138, 0.7001, and 0.6920, respectively. Leveraging these optimized machine learning models, we conducted a large-scale virtual screening of over 11.5 million compounds, identifying 24 promising candidates. Subsequent molecular dynamics (MD) simulations and MM/GBSA binding free energy calculations supported the structural stability and strong predicted binding affinities of three lead molecules: Z2215663922, 19,835,875, and Z310319934. Furthermore, ADMET profiling indicated generally acceptable pharmacokinetic properties, although potential hERG-related liabilities for certain candidates warrant further experimental scrutiny. Our findings provide structurally diverse scaffolds and a computational prioritization framework for early-stage discovery and optimization of SERT inhibitor candidates that merit subsequent experimental validation.