Alexander B. Wiltschko

The paper shows that odor mixtures can be predicted linearly from component odorant profiles, challenging the assumption of nonlinear interactions.

The paper develops a Principal Odor Map using graph neural networks that matches human-level odor description performance and generalizes to diverse odor prediction tasks.

The paper identifies a molecular-level representation of odors that predicts receptor, neural, and behavioral responses across species and shows metabolic relationships in odor space.