Machine learning-driven QSAR modeling combined with single cell transcriptomics identifies novel drug targets for lung cancer.

Authors

Nagasundaram Nagarajan, Sushil Kumar Shakyawar, Kayode Raheem, Chittibabu Guda

Published in

Journal of translational medicine. Jun 19, 2026. Epub Jun 19, 2026.

Abstract

Non-small cell lung cancer (NSCLC) is a leading cause of cancer-related mortality, largely due to frequent metastasis to the brain and bones. Therapeutic outcomes are often limited by drug resistance, tumor heterogeneity, and the lack of effective treatment options across different stages and metastatic sites. Identifying druggable targets that are conserved between primary tumors and metastases is critical for advancing precision oncology.
scRNA-seq expression profiles from primary NSCLC tumors and matched brain and bone metastases were analyzed to identify conserved and site-specific gene expression signatures. Ingenuity Pathway Analysis was used for target prioritization. Potential targets identified from scRNA-seq analysis were used for ligand screening using machine learning (ML)-based quantitative structure-activity relationship (QSAR) modeling. QSAR models were developed using ChEMBL bioactivity data and evaluated across multiple ML algorithms. Large-scale virtual screening was followed by molecular docking and molecular dynamics (MD) simulations for lead optimization.
Eight candidate therapeutic targets were prioritized, among which ARPC2, PSMB4, and RAC2 were consistently overexpressed across primary, brain, and bone metastatic sites and were functionally implicated in key cancer-associated pathways. QSAR modeling demonstrated strong predictive performance, with XGBoost and Random Forest models achieving AUROC values greater than 0.97. Virtual screening of approximately 9-15 million compounds per target identified high-affinity candidates. Subsequent docking and MD simulations revealed that the ARPC2-14465616, PSMB4-74833722, and RAC2-57175325 complexes exhibited the highest structural stability and sustained intermolecular interactions.
This integrative single-cell transcriptomics and ML-driven drug discovery framework identified conserved druggable targets and promising lead compounds for metastatic NSCLC. The results provide a strong foundation for experimental validation and the development of novel therapeutic strategies targeting both primary tumors and metastatic lesions.

PMID:
42321836
Bibliographic data and abstract were imported from PubMed on 20 Jun 2026.

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