Andrew L. Beam

This paper introduces Chroma, a generative model for proteins that can sample novel structures and sequences, conditioned on desired properties and functions.

This paper presents a deep convolutional neural network to predict antibiotic resistance in M. tuberculosis isolates, achieving state-of-the-art accuracy and enabling interpretable feature analysis.

This paper uses machine learning to predict small molecule teratogenicity from structural and bioactivity data, identifying chemical features associated with teratogenic risk.

This study uses a multitask deep learning model to predict tuberculosis drug resistance from genomic data, achieving high accuracy in both first-line and second-line drug resistance prediction.