The Role of Descriptors Extracted From Ligand-Target Interaction to Improve Conventional Qsar Model Performance in the Realm of Angiogenesis Receptor Modulation to Fight Cancer Publisher Pubmed



Mr Torabi Mohammad REZA ; S Sardari SOROUSH ; He Perezsanchez Horacio EMILIO ; F Ghasemi FAHIMEH
Source: Future Medicinal Chemistry Published:2025

Abstract

Aims: This study aims to develop a receptor-dependent 4D-QSAR model to overcome key limitations of traditional QSAR, including its dependency on molecular alignment and poor performance with small datasets, by integrating ligand–target interaction information. Materials & methods: Angiogenesis-related receptors, including VEGFR2, FGFR1–4, EGFR, PDGFR, RET, and HGFR (MET) were chosen based on the biological relevance in cancer. Ligand datasets with known IC₅₀ values were extracted from PubChem. One hundred docked conformers per ligand were generated using AutoDock. Protein–ligand interaction fingerprints were computed and encoded as 4D-descriptors. After evaluation via multiple classification algorithms, Random Forest was selected for model construction. Results: The results shown that the proposed model outperformed traditional 2D-QSAR approaches across all targets. Accuracy exceeded 70% in most datasets, including those with fewer than 30 compounds. Besides, the model performance was significantly improved via using all conformers versus using a single best pose. The model demonstrated robust predictive power across varying receptor classes under consistent assay conditions. Conclusions: The proposed receptor-dependent 4D-QSAR model provides enhanced accuracy and generalizability for small, diverse datasets. Its integration of LTI-derived descriptors makes it a valuable tool for early-stage lead optimization and supports rational multi-target drug design in oncology. © 2025 Elsevier B.V., All rights reserved.