Application of Artificial Intelligence and Machine Learning in Drug Development for Infectious Diseases
Keywords:
Artificial Intelligence (AI), Machine Learning (ML), Deep Learning (DL), Drug Discovery, Infectious Diseases, Precision Medicine, Antimicrobial ResistanceAbstract
The persistent threat of infectious diseases and the growing challenge of antimicrobial resistance have validated the need for innovative approaches in drug discovery and development. This review explores the transformative role of artificial intelligence (AI), and machine learning (ML), in addressing these challenges across the drug development pipeline for infectious diseases. AI models and ML algorithms excel at integrating and analyzing diverse datasets, including genomic, proteomic, chemical, clinical, and epidemiological data, to accelerate target identification, optimize compound screening, and predict drug efficacy and safety. The review synthesizes recent advances, highlighting the application of supervised and deep learning models in pathogen identification, antimicrobial susceptibility testing, and the rational design of novel therapeutics. It also examines the use of generative AI for precision medicine, drug repurposing, and real-time outbreak prediction. Despite these advances, the review identifies ongoing challenges related to data quality, model interpretability, and ethical considerations. Positively, AI and ML stand as a catalyst for more efficient, cost-effective, and personalized therapeutic strategies, with the promise of significantly improving global public health outcomes.
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