Computational Drug Development (AE: MC, DS; GE)

3 credits · Graduate

This course covers datasets, principles, and algorithms for analyzing and designing chemicals with implications for health and disease. Explore the history, challenges, and opportunities of AI in drug discovery. The class includes five sections: (1) datasets, (2) algorithms, (3) machine learning tools, (4) evaluations, and (5) applications in pharmacology. Students will analyze data, develop predictive models, and complete a final project designing a novel algorithm. Skills gained will enhance expertise in data science, biomedical engineering, and drug design while fostering appreciation for interdisciplinary pharmaceutical research.