Artificial intelligence (AI) has evolved from experimental applications to a central tool of modern drug discovery and development. This editorial reviews AI’s current impact across the pipeline, from target identification and generative molecular design to preclinical prediction, drug repurposing, and clinical trial optimisation, highlighting advances in multi- omics integration, generative chemistry, and absorption, distribution, metabolism, excretion, and toxicity (ADMET) modelling. Novel directions include hybrid AI-quantum approaches, regulatory modernisation, and collaborative frameworks that accelerate translation from discovery to patient benefit. The challenges in data quality, interpretability, governance, and system integration highlight the need for robust infrastructure and ethical governance. By aligning technical innovation with transparent, interoperable platforms and responsible practices, AI can transform drug design into a faster, more predictive, and patient-centric process.
Keywords: Artificial Intelligence; Drug Discovery; Generative Models; Target Identification; Multi-Omics Integration; ADMET Prediction; Drug Repurposing; Clinical Trial Optimisation; Regulatory Modernisation; Quantum Computing in Drug Design.
Download PDF