ADVANCING DRUG DISCOVERY AND PRECISION ONCOLOGY: A DEEP LEARNING–DRIVEN PHARMACEUTICAL FRAMEWORK FOR LUNG CANCER TREATMENT—A COLLABORATIVE INITIATIVE BETWEEN PAKISTAN'S DEPARTMENT OF PHARMACEUTICAL SCIENCES AND ZHEJIANG UNIVERSITY, CHINA

Abstract

Lung cancer continues to impose a substantial global healthcare burden, necessitating advanced pharmaceutical approaches for accelerating anticancer drug discovery and improving precision-guided therapeutic interventions. This study presents an integrated deep learning–driven pharmaceutical framework designed to enhance molecular drug screening, drug–target interaction prediction, and personalized treatment optimization through interdisciplinary collaboration between pharmaceutical researchers in Pakistan and Zhejiang University, China. The proposed framework incorporates a multimodal research dataset comprising 18,500 molecular compounds, 4,250 lung cancer patient profiles, 12,800 gene-expression records, and 6,400 drug–target interaction pairs, enabling comprehensive analysis of molecular characteristics, genomic biomarkers, and therapeutic response patterns. A hybrid Graph Neural Network–Convolutional Neural Network–Bidirectional Long Short-Term Memory–Attention (GNN–CNN–BiLSTM–Attention) architecture is developed to integrate molecular graph representations, hierarchical biochemical features, contextual genomic dependencies, and attention-guided pharmacological information. The methodological pipeline encompasses molecular descriptor extraction, drug-likeness evaluation, toxicity assessment, genomic feature normalization, multimodal data fusion, and predictive therapeutic stratification. The reported experimental evaluation demonstrates that the proposed framework achieves 97.6% drug–target interaction prediction accuracy and 96.8% therapeutic response classification accuracy, with precision, recall, and F1-score values of 97.2%, 96.5%, and 96.8%, respectively. Furthermore, the framework attains an area under the receiver operating characteristic curve of 0.985, accompanied by a 42.7% reduction in computational drug-screening time and a 35.4% improvement in candidate prioritization efficiency relative to conventional computational approaches. Explainable artificial intelligence analysis highlights influential molecular descriptors, oncogenic biomarkers, and drug-response determinants, providing biologically interpretable insights into candidate compound selection and patient-specific therapeutic suitability. Comparative evaluation against conventional machine learning algorithms and standalone deep learning architectures further supports the potential advantages of multimodal representation learning for pharmaceutical decision support. The findings establish a promising computational foundation for accelerating lung cancer drug discovery, strengthening biomarker-informed therapeutic selection, and advancing precision oncology through Pakistan–China pharmaceutical research collaboration, while emphasizing the importance of independent clinical and experimental validation before implementation in routine cancer care.