Jessica B. White

The paper presents a pan-cancer ex vivo drug screen atlas with 2.1M experiments, identifying tissue-specific drug sensitivities and demonstrating their predictive value for clinical profiles.

This paper describes the development of KinoML, a framework for structure-enabled machine learning in drug discovery, focusing on kinases, and highlights key lessons in data management and model reproducibility.

The paper evaluates structure-based methods to predict how kinase mutations affect inhibitor binding, finding they accurately classify mutations as resistant or sensitizing.

BATCHIE efficiently identifies effective drug combinations through adaptive experimentation, reducing the number of required experiments while accurately predicting synergies.