Artificial Intelligence and Machine Learning in Drug Discovery: Trends, Techniques, Challenges, and Future Prospects

The conventional drug development process has a high attrition rate, is expensive, and takes a long time. Machine learning (ML) and artificial intelligence (AI) have become revolutionary technologies that can find new therapeutic candidates, increase efficiency, and lower prices. Supervised and unsupervised learning, deep learning (CNNs, RNNs), graph neural networks (GNNs), natural language processing (NLP), generative/reinforcement learning models, and their applications throughout the drug discovery process are all covered in detail in this review. We look at these techniques in the areas of drug repurposing, ADMET prediction, virtual screening and lead optimization, target discovery, and clinical trial design. Important topics are covered, including the availability and quality of data, the interpretability of models, ethical and legal concerns, and the requirement for experimental integration. We focus on upcoming topics like explainable AI, hybrid computational-experimental techniques, and frontier technologies like quantum computing. Finally, recent commercial examples demonstrate real-world applications of AI in drug discovery. Given the rapid evolution of AI/ML, ongoing innovation and collaboration will be required to fully fulfill their promise to alter pharmaceutical research and development.