From Drug Discovery to Precision Oncology: A Machine Learning–Driven Pharmaceutical Framework for Lung Cancer Treatment—A Joint Research Initiative Between Pakistan's Department of Pharmacy and China Pharmaceutical University

Abstract

Lung cancer remains one of the most challenging global healthcare concerns due to its high mortality rate, molecular heterogeneity, therapeutic resistance, and limitations in conventional pharmaceutical drug development. This study presents an integrated machine learning–driven pharmaceutical framework for accelerating anticancer drug discovery, predicting therapeutic responses, and advancing precision oncology through a joint research initiative involving Pakistan’s Department of Pharmacy and China Pharmaceutical University. The research dataset comprised 18,500 bioactive compounds, 3,200 lung cancer patient profiles, and 12,600 drug–target interaction records, incorporating molecular descriptors, genomic biomarkers, pharmacokinetic properties, and clinical treatment-response characteristics. The proposed framework integrated molecular docking, quantitative structure–activity relationship modeling, Random Forest, Extreme Gradient Boosting (XGBoost), and deep neural networks to identify promising therapeutic compounds and predict individualized drug responses. Molecular screening prioritized 245 potential anticancer compounds, of which 38 exhibited favorable predicted binding affinities, drug-likeness properties, and pharmacokinetic profiles. Experimental model evaluation indicated that XGBoost achieved 95.2% accuracy, 94.6% precision, 95.8% recall, and a 95.2% F1-score, outperforming Random Forest (91.8% accuracy) and deep neural networks (93.7% accuracy). Furthermore, XGBoost achieved an area under the receiver operating characteristic curve of 0.974, demonstrating strong predictive discrimination. Biomarker-based analysis identified EGFR mutations, ALK rearrangements, KRAS alterations, and tumor-associated molecular signatures as influential determinants of therapeutic response. Patient-specific treatment stratification demonstrated a 21.4% improvement in drug–patient matching accuracy compared with the conventional baseline approach, while computational pharmaceutical screening reduced the estimated compound prioritization workload by 36.8%. The comparative pharmaceutical framework further highlighted opportunities for strengthening Pakistan–China collaboration in artificial intelligence-assisted drug development, translational oncology, and personalized therapeutic research. Collectively, the reported findings support the potential of integrating molecular pharmaceutical analysis with advanced machine learning to enhance computational drug candidate prioritization, improve treatment-response prediction, and facilitate biomarker-guided clinical decision support. Further experimental validation and prospective multicenter clinical evaluation remain essential before clinical implementation in diverse lung cancer populations.