Intelligent consensus-based QSAR modeling and virtual screening of dual inhibitors of 5HT1A/5HT7 serotonin receptors.

Dual inhibitors of 5HT1A and 5HT7 serotonin receptors provide improved therapeutic effectiveness for depression, anxiety disorders, and neuropsychiatric conditions, potentially decreasing the necessity for multiple medications and reducing side effects. This study presents a cutting-edge computational framework for the discovery and optimization of dual 5HT1A/5HT7 inhibitors, integrating machine learning (ML), molecular docking, and virtual screening. A meticulously curated dataset of dual inhibitors was employed to develop regression and classification models using state-of-the-art ML algorithms. The consensus regression model achieved remarkable predictive performance (R²Test>0.93), and reduced RMSECV by 30-40 % compared to individual models. In classification tasks, the majority voting method boosts accuracy to 92 % and achieves a 25 % increase in F1 scores, surpassing individual models and showcasing strong generalization across 5-fold cross-validation and y-randomization tests. The validated consensus-based QSAR model was employed as endpoint analysis of virtual screening of dual 5HT1A/5HT7 inhibitors. This research highlights the power of consensus modeling and ensemble approaches in enhancing predictive accuracy and robustness. By integrating machine learning, structural insights, virtual screening and ADMET analysis, we provide a reliable and efficient strategy for identifying novel 5HT1A/5HT7 inhibitors, paving the way for future experimental validation and therapeutic development of next-generation treatments for serotonin-related disorders.