Will Gatlin

This paper shows that deep learning models' performance in predicting protein-ligand binding poses depends on the energy landscape's frustration quenching and network topology, not algorithmic flaws.

This study uses computational methods to analyze class 3 and class 4 antibodies' mechanisms of neutralizing SARS-CoV-2, revealing distinct strategies for binding, dynamics, and immune escape.

This paper analyzes the molecular mechanisms of broadly neutralizing SARS-CoV-2 antibodies by combining computational methods to reveal how conserved epitopes resist immune escape.

This study uses explainable AI to analyze the energy landscape of protein kinases, revealing that allosteric sites are in neutrally frustrated regions, explaining their detection challenges.

This study identifies a universal failure in AI models to predict allosteric ligand binding, linking it to biophysical constraints in energy landscapes.

This paper compares sequence-based and structure-based methods for predicting allosteric binding sites in protein kinases, finding that sequence-based models perform poorly on allosteric sites despite good performance on orthosteric ones.