Identification of novel serotonin transporter (SERT) inhibitors via large-scale machine learning-based virtual screening and molecular dynamics simulations
- 1The First People’s Hospital of Xiaoshan District, Xiaoshan Affiliated Hospital of Wenzhou Medical University, Hangzhou, 311200 China
- 2The Second Affiliated Hospital of Jiaxing University, Jiaxing, 314000 China
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.
Supplementary Information: The online version contains supplementary material available at https://doi.org/10.1007/s11030-026-11705-0.
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BibTeX
@article{zhao2026identification,
title = {{Identification of novel serotonin transporter (SERT) inhibitors via large-scale machine learning-based virtual screening and molecular dynamics simulations}},
author = {Rujia Zhao and Tianzhu Shen and Tongzhou Huang},
journal = {Molecular Diversity},
year = {2026},
month = aug,
doi = {10.1007/s11030-026-11705-0},
url = {https://doi.org/10.1007/s11030-026-11705-0},
note = {CC BY-NC-ND 4.0},
}
RIS
TY - JOUR
TI - Identification of novel serotonin transporter (SERT) inhibitors via large-scale machine learning-based virtual screening and molecular dynamics simulations
AU - Rujia Zhao
AU - Tianzhu Shen
AU - Tongzhou Huang
PY - 2026
DA - 2026/08/28
JO - Molecular Diversity
DO - 10.1007/s11030-026-11705-0
UR - https://doi.org/10.1007/s11030-026-11705-0
AB - 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. Supplementary Information: The online version contains supplementary material available at https://doi.org/10.1007/s11030-026-11705-0.
SN - 1381-1991
SN - 1573-501X
ER -