Sukrit Singh

This study uses molecular dynamics simulations and Markov state models to compare activation mechanisms of tyrosine kinases, revealing kinase-specific conformational ensembles.

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.

The paper uses MCCE to analyze how kinase inhibitors affect protonation and tautomeric states, finding that inhibitor charges change dynamically and minority tautomers can dominate binding.

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.