AI in Drug Discovery: How Models Reshape the Path from Molecule to Medicine

Drug discovery has always been slow, expensive, and full of dead ends. AI does not change the underlying biology, but it changes how teams search molecular space, prioritize candidates, and learn from past failures. This course gives you a calm, structured introduction so you can speak confidently about AI in drug discovery without overstating its current reach. You will learn what stages of drug discovery AI now influences, where it has demonstrated impact, and where it remains experimental. The course stays grounded in widely used concepts and respects the long timelines and risk profile of pharmaceutical work. What you'll learn: - Understand the major stages of drug discovery from target identification through preclinical evaluation - Recognize how AI supports target identification through omics analysis and pathway modeling - Explore generative approaches to molecule design and the role of physics-aware models - Read how AI predicts efficacy, toxicity, and pharmacokinetic properties of candidate molecules - Identify the role of drug repurposing and how AI accelerates the search across approved drugs - Understand the integration points between AI workflows and traditional medicinal chemistry teams The course begins with the stages of drug discovery, moves through where AI fits in, and closes with the realities of integrating AI into pharmaceutical workflows. Written exercises help you connect each concept to a realistic discovery scenario. This course is designed for absolute beginners with no chemistry or AI background, including life sciences students, pharmaceutical operations professionals, and software developers entering the field. No prerequisites are needed. The course is informational and stays focused on understanding rather than providing scientific or medical advice.