Aaron L. Feller

This study evaluates zero-shot mutation prediction models in protein engineering, finding they struggle to identify optimal mutations and predict new functions due to limitations in capturing evolutionary constraints.

The paper validates and analyzes 12,000 AI-designed CAR-T binders from a global competition, identifying successful designs and factors improving their performance.

This study combines machine learning and directed evolution to optimize the monooxygenase HpaBC for improved L-Dopa production activity.

This paper introduces PeptideCLM-2, a chemical language model for therapeutic peptides, showing strong performance in predicting drug development endpoints.

The paper introduces PeptideCLM, a chemical language model that predicts membrane diffusion of cyclic peptides more effectively than existing models.