The target at the center of the study is the mechanistic target of rapamycin, or mTOR, a serine/threonine kinase that functions as one of the cell’s most important signaling hubs. mTOR integrates signals about nutrient availability, growth factors, energy status, and stress, and it operates through two distinct multiprotein assemblies, mTORC1 and mTORC2. mTORC1 drives anabolic metabolism, protein and lipid synthesis, and the suppression of autophagy, while mTORC2 promotes cell survival, cytoskeletal organization, and Akt-dependent signaling. When this pathway is persistently or inappropriately activated, the delicate balance between growth, metabolism, and cellular quality control breaks down, a scenario closely linked to cancer as well as metabolic and neurodegenerative disorders. Existing drugs such as rapamycin and its analogs modulate the pathway, but their clinical usefulness is limited by incomplete pathway inhibition, feedback activation, and off-target effects, which is precisely why the search for next-generation mTOR inhibitors with better specificity remains so intense.

Instead of screening existing chemical libraries, the Indian team turned the problem around and asked a machine to invent molecules from scratch. The researchers used the DeepScreening web server, which employs a recurrent neural network, a class of deep learning model particularly adept at learning sequential patterns. Molecules are represented as SMILES strings, compact text encodings of chemical structures, and the network learns the statistical grammar of these strings well enough to generate chemically plausible new ones. The crucial step was transfer learning: the team took a pre-trained model and fine-tuned it on CHEMBL2842, a curated subset of the ChEMBL database containing 5,178 experimentally validated mTOR inhibitors with reported IC50 values. Because this training set spans diverse scaffolds and activity ranges, the fine-tuned generator absorbed the structural features associated with mTOR inhibition and produced a library of 200 candidate molecules, each scored by the model for target relevance.

With 200 synthetic candidates in hand, the researchers moved to structure-based validation. They retrieved the crystal structure of the human mTOR kinase domain bound to Torin2 from the Protein Data Bank, entry 4JSX, determined by X-ray diffraction at 3.50 angstroms. After stripping the co-crystallized ligand and preparing the receptor with polar hydrogens and Kollman charges, they first redocked Torin2 into the catalytic pocket to prove the protocol worked. The redocked inhibitor reproduced its crystallographic pose with a root mean square deviation of just 0.99 angstroms and a binding energy of minus 10.3 kilocalories per mole, anchored by a hydrogen bond to Val2240. Only then did they dock the generated molecules, using AutoDock Vina with an exhaustiveness setting of 8 and a grid centered on the Torin2-defined binding site.

Four compounds emerged from the docking campaign with scores equal to or better than the reference drug. S000031 led the pack at minus 12.2 kilocalories per mole, followed by S000046 at minus 10.9, while S000090 and S000043 matched Torin2 at minus 10.3. The interaction analysis revealed why S000031 stood out. It formed multiple hydrogen bonds with Val2240, Thr2245, and Glu2190, and it wrapped itself in an extensive hydrophobic network involving residues such as Cys2243, Lys2187, Met2345, Ile2356, Leu2185, Ala2248, Trp2239, and Gly2238. Notably, the key residues engaged by Torin2, particularly Val2240, Glu2190, Met2345, and Trp2239, were conserved across all four designed compounds, suggesting a shared binding mode. The authors emphasize that superior affinity came not from the sheer number of hydrogen bonds but from an optimal balance between polar anchoring and hydrophobic packing, a mechanistic insight that could guide future lead optimization.

Binding energy alone means little if a molecule cannot survive in a human body, so the team ran the four hits through a battery of pharmacological filters. Lipinski’s Rule of Five analysis using DruLiTo showed that S000046 and S000043 satisfied every criterion with zero violations, while S000031 and S000090 each showed a single violation, still within the accepted threshold for orally active drugs. Toxicity predictions from ProTox-3.0 were striking: all four designed compounds were predicted inactive for hepatotoxicity, cardiotoxicity, carcinogenicity, mutagenicity, and cytotoxicity, whereas the reference compound Torin2 showed predicted activity for hepatotoxicity, carcinogenicity, and mutagenicity. The authors are careful to note that these predictions come from a single in silico platform and must be confirmed experimentally before any claims about safety can be made.

Structural novelty was verified through Tanimoto similarity analysis using ECFP4 molecular fingerprints. All four leads scored far below the novelty threshold of 0.7 relative to Torin2, with S000046 at 0.0948 and S000031 at 0.096, meaning the neural network had not merely produced close analogs of known inhibitors but had explored genuinely new chemical space. ADMET profiling with SwissADME added further encouragement: S000031, S000046, and S000090 showed high predicted gastrointestinal absorption comparable to Torin2, none of the compounds were predicted to cross the blood-brain barrier, and none triggered PAINS alerts, the red flags that often disqualify screening hits. Synthetic accessibility scores ranged from 3.32 to 5.95, indicating moderate but feasible synthetic routes. One caveat surfaced in metabolism: S000090 and S000043 showed predicted inhibition of CYP3A4 and multiple CYP isoforms respectively, which may require optimization, while S000031 and S000046 showed minimal cytochrome P450 inhibition, suggesting favorable metabolic stability.

The final layer of analysis came from density functional theory, the quantum mechanical method that computes electronic structure from first principles. Using the ORCA software package at the B3LYP/6-311G(d,p) level of theory, the team optimized the geometries of the leads and Torin2 and examined their frontier molecular orbitals. Torin2 showed a HOMO-LUMO energy gap of 3.601 electron volts, while the designed compounds displayed much smaller gaps, with S000031 at just 0.44 electron volts. Derived descriptors told a consistent story: S000031 exhibited the lowest global hardness at 0.22 electron volts, the highest softness at 4.545 inverse electron volts, and a strikingly high electrophilicity index of 96.496 electron volts, indicating strong potential for interaction with nucleophilic sites in the protein. The authors stress that these quantum descriptors should be read as indicators of electronic character and chemical reactivity rather than direct evidence of biological potency.

The most promising candidate to emerge from the entire workflow is S000031, which combines the strongest predicted binding affinity, a chemically novel scaffold, clean toxicity predictions, favorable absorption, minimal CYP liability, and exceptional electronic reactivity. The study’s genuine contribution lies less in any single algorithm than in the integration: a target-focused generative model, validated docking, drug-likeness and ADMET screening, toxicity prediction, novelty assessment, and quantum chemical characterization assembled into one reproducible pipeline that could in principle be redirected at any kinase or disease target. The researchers themselves are candid about the limitations. Docking scores are approximate estimates prone to false positives, the DeepScreening server’s internal architecture could not be independently tuned or benchmarked, and no experimental assays have yet been performed on the new molecules. The immediate next steps they propose include molecular dynamics simulations, MM-PBSA and MM-GBSA binding free-energy calculations, and ultimately laboratory testing of the top-ranked compounds. Until those experiments are done, the four designed inhibitors remain computational hypotheses, but they are unusually well-documented ones, and they demonstrate that a neural network fine-tuned on validated data can propose molecules that a rigorous computational gauntlet judges to be at least as promising as the best known drug for one of cancer biology’s most important targets.

Subject of Research: Computational de novo design of mTOR kinase inhibitors using recurrent neural networks, molecular docking, ADMET prediction, and density functional theory

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

Article References: Bisht, A., Kumar, S., & Chandra, S. (2026). Computational de novo discovery of novel mTOR inhibitors using recurrent neural networks and integrated in silico approaches including docking and DFT. Discover Chemistry, 3(1), Article 529. https://doi.org/10.1007/s44371-026-00994-x

Image Credits: AI Generated

DOI: 10.1007/s44371-026-00994-x

Keywords: mTOR, de novo drug design, recurrent neural network, molecular docking, AutoDock Vina, ADMET, density functional theory, cancer, kinase inhibitors, transfer learning, ChEMBL, drug discovery