Andrea Volkamer

The paper combines Bayesian optimization with MMGBSA-based binding affinity calculations to improve ligand discovery by enhancing compound ranking and recovery rates.

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.