Identification of novel serotonin transporter (SERT) inhibitors via large-scale machine learning-based virtual screening and molecular dynamics simulations.

Abstract

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.