This course explores the application of artificial intelligence to bridge scientific discovery and the engineering of practical biological and chemical solutions. The course presents a unified, end-to-end workflow in which students leverage multimodal bioinformatics and biochemical data to detect and characterize novel biological or chemical phenomena. Learners then design targeted interventions—using state-of-the-art generative and diffusion models for both protein-based therapeutics and small-molecule compounds—and investigate how synthetic biology and biomanufacturing principles enable scalable production of these designs. The curriculum emphasizes a holistic systems approach that integrates data acquisition, advanced modeling, numerical validation, and real-world deployment. Through hands-on coding, implementation, and rigorous analytical evaluation, students gain the skills to construct, interpret, and effectively communicate AI-driven solutions that are technically robust, reproducible, and directly applicable in life sciences settings.
Course Prerequisite(s)
EN.705.601 Applied Machine Learning or EN.605.649 Principles and Methods in Machine Learning