Computational de novo discovery of novel mTOR inhibitors using recurrent neural networks and integrated in silico approaches including docking and DFT

Abstract

The mechanistic target of rapamycin (mTOR) is a key serine/threonine kinase that regulates cell growth, metabolism, and survival, making it an important therapeutic target for cancer and other diseases. In this study, a Recurrent Neural Network (RNN)-based de novo drug design approach was employed to discover novel mTOR inhibitors. The model was fine-tuned on 5,178 experimentally validated inhibitors from the ChEMBL database (CHEMBL2842), generating 200 new candidate molecules. The candidates were evaluated using a comprehensive in silico pipeline including molecular docking, drug-likeness, ADMET profiling, toxicity prediction, synthetic accessibility, Tanimoto similarity, and Density Functional Theory (DFT) calculations. Molecular docking identified four promising leads (S000031, S000046, S000090, and S000043) with strong binding affinities ranging from − 12.2 to − 10.3 kcal/mol, outperforming or matching the reference inhibitor Torin2. These compounds showed stable interactions with key active-site residues and favorable pharmacokinetic profiles. DFT analysis revealed desirable electronic properties, including smaller HOMO-LUMO gaps, higher electronegativity, and increased electrophilicity. Among them, S000031 emerged as the most promising candidate due to its superior binding affinity, structural novelty, and electronic characteristics. This integrated computational framework demonstrates an effective strategy for identifying potential mTOR inhibitors, though experimental validation is essential to confirm their therapeutic efficacy.