Designing AI Workflows for Molecule Generation and Property Prediction

Designing AI workflows for early drug discovery is as much about chemistry literacy as it is about model selection. The molecular representations you choose, the data you train on, the way you handle uncertainty, and the integration with medicinal chemists all shape whether the workflow produces useful candidates or expensive noise. This course walks through those decisions in a structured way. You will work through written design exercises that mirror how a computational chemistry team would plan an AI-supported discovery workflow. The emphasis is on the practical tradeoffs that matter when data is sparse and the cost of wrong predictions is high. What you'll learn: - Choose molecular representations including SMILES, graph, and 3D-aware encodings - Plan datasets that balance public sources, internal data, and synthetic augmentation - Compare generative approaches for molecule design including variational autoencoders, transformers, and diffusion - Build predictive models for activity, toxicity, and pharmacokinetic properties with calibrated uncertainty - Apply active learning and Bayesian optimization to prioritize the next molecules to synthesize - Design feedback loops with medicinal chemists that close the gap between models and bench work The course progresses from molecular representation through generative modeling, prediction, active learning, and chemist collaboration. A capstone written exercise asks you to draft a one-page design for an AI-supported discovery workflow targeted at a specific therapeutic area. This course is designed for computational chemists, data scientists entering pharma, and life sciences students with some software background. No deep medicinal chemistry experience is required. The course treats the workflow as a design problem and is informational; it does not provide guidance for specific scientific or medical decisions.