Alexander Matthew Payne

The paper evaluates how many crystal structures are needed for accurate pose prediction in drug discovery, finding that five structures per scaffold often suffice.

The paper shows that incorporating physics-based priors improves structure-based binding affinity models, achieving performance comparable to ligand-only models on a drug discovery dataset.

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.

This paper introduces a computational pipeline for identifying broad-spectrum antivirals by analyzing viral sequences, predicting ligand poses, and scoring protein-ligand complexes, validated on SARS-CoV-2 and MERS-CoV datasets.

The paper describes the rapid structural determination of SARS-CoV-2 proteins during the pandemic and the efforts to evaluate and improve these structures for drug and vaccine development.