Artificial Intelligence in Drug Discovery: Integrating Bioinformatics and System Biology for Next- Generation Therapeutics.

Abstract

Integrating bioinformatics, systems biology and drug discovery, artificial intelligence (AI) has become a pivotal force in the advancement of biomedical research. Bioinformatics, typically, analyses huge data-sets and organizes them in such a way that the disease-associated genes, proteins, biomarkers and molecular signatures can be identified. On the other hand, systems biology goes a step further and with the help of combined multi-omics data, it can expose the complicated molecular networks, mechanisms of diseases and also show the ways to potential drug-targets. AI technologies, including machine learning, deep learning, artificial neural networks, graph neural networks, large language models, generative AI and reinforcement learning, significantly enhance these domains by improving pattern recognition, predictive modelling and biological data integration. Their applications span genomics, transcriptomics, protein structure prediction, pathway analysis, disease modelling, precision medicine and digital twin technologies, ultimately facilitates therapeutic target identification, virtual screening, lead optimization, ADMET prediction, de novo drug design and drug repurposing. This review highlights the unified role of AI in integrating bioinformatics and systems biology to enable faster, more accurate and cost-effective drug discovery. It also discusses current limitation, including data quality, model interpretability, ethical considerations and clinical validation, while presenting the future perspectives for AI-driven precision therapeutics and personalized medicine.