AI in Drug Discovery and Development

Bringing a new medicine to market historically takes over a decade, but artificial intelligence is rapidly accelerating this vital pipeline. This text-based course introduces you to the core concepts of machine learning as applied to modern pharmaceutical research. You will understand how computational models predict molecular behavior, identify promising drug candidates, and optimize clinical trials. Through clear, written explanations, you will gain the foundational knowledge needed to bridge the gap between computer science and pharmacology. What you'll learn: - Understand the fundamental stages of traditional drug discovery and where AI introduces efficiencies. - Learn how machine learning models predict molecular properties and ADMET profiles. - Explore generative AI models and graph neural networks used for de novo molecular design. - Apply virtual screening techniques to identify potential drug candidates from massive chemical databases. - Discover how AI optimizes clinical trial design and patient selection. - Identify current challenges, ethical considerations, and future trends in AI-driven healthcare. The course begins with essential terminology in biochemistry and machine learning before guiding you through target identification, lead optimization, and pre-clinical development. You will progress from foundational theory to reviewing modern computational pipelines used in the biotech industry. This course is designed for beginners, students, and professionals in life sciences or computer science who want to understand the intersection of AI and pharmacology. No prior programming or advanced biochemistry experience is required. Start reading today to explore how artificial intelligence is shaping the future of medicine.