Development, Validation, and Application of a Structure-Based Pharmacophore Model for Human Dihydrofolate Reductase Inhibitor Discovery through Drug Repurposing

Introduction: Human dihydrofolate reductase (hDHFR) is a key enzyme in folate metabolism and an established therapeutic target for cancer and autoimmune diseases. Although methotrexate remains the reference hDHFR inhibitor, its clinical use is limited by toxicity and the emergence of drug resistance. Drug repurposing, which explores new therapeutic uses for existing compounds, offers a cost-effective alternative for identifying hDHFR inhibitors with established safety profiles.

Methods: A structure-based pharmacophore model was generated from the crystal structure of the hDHFR–MTX complex (PDB ID: 1U72). The model was validated using an external dataset of 410 compounds and assessed through receiver operating characteristic (ROC) analysis, average precision, enrichment factor, and Güner–Henry scoring. The validated pharmacophore was subsequently applied to virtual screening of the DrugBank database to identify approved and investigational drugs with potential hDHFR inhibitory activity.

Results: The pharmacophore model demonstrated excellent predictive performance, achieving a ROC-AUC of 0.989, an average precision of 0.95, an enrichment factor of 10.53, and a Güner–Henry score of 0.882. Virtual screening identified compounds that satisfied the pharmacophoric requirements and were prioritized according to their predicted binding potential and drug repurposing relevance. Subsequent molecular docking estimated binding energies ranging from −7.0 to −11.1 kcal/mol. Importantly, the workflow successfully recovered previously reported DHFR inhibitors, supporting the reliability of the screening strategy while identifying promising candidates.

Conclusions: Overall, the results demonstrate that structure-based pharmacophore modeling combined with virtual screening and molecular docking constitutes an efficient strategy for hDHFR inhibitor discovery through drug repurposing. The prioritized compounds represent promising candidates for subsequent molecular dynamics simulations and experimental validation.