Deep learning-driven QSAR and micro-scale MD simulation-guided strategy reveals non-toxic human HGFR inhibitors.

Hepatocyte Growth Factor Receptor (HGFR) overexpression plays a critical role in ovarian cancer progression by promoting cell proliferation, survival, and metastasis. Despite the therapeutic potential of existing HGFR inhibitors, such as crizotinib, concerns regarding low potency and high toxicity require safer alternatives. This study establishes an integrative in silico framework combining deep learning-based bioactivity prediction, structure-based drug repurposing, and toxicity profiling via Directed Message Passing Neural Network (D-MPNN). A rigorously filtered dataset of HGFR-targeting bioactives was used to train an artificial neural network (ANN), which was subsequently applied to evaluate the bioactivity of 1,040 FDA-approved drugs. Highly potent candidates underwent molecular docking, identifying venetoclax (S-score: -8.78, RMSD: 1.32), LSM-5313 (S-score: -8.50, RMSD: 1.89), and cefoperazone (S-score: -8.24, RMSD: 1.82) as the lead compounds. Micro-scale molecular dynamics simulations (2 µs) and post-trajectory analyses including RMSD, RMSF, Rg, hydrogen bonding, PCA, FEL, DCCM, and MMGBSA confirmed their stable and favorable binding at the HGFR active site. Finally, the D-MPNN-driven toxicity assessment revealed no significant toxic liabilities in the proposed compounds. Overall, this multi-tiered computational approach offers reliable, mechanistically supported candidates for HGFR inhibition. The identified FDA-approved drugs represent promising, non-toxic therapeutic options for ovarian cancer, encouraging further preclinical and clinical investigation.