Chaitanya Kumar Jaladanki

This study evaluates three docking programs for RNA-ligand screening, finding similar overall performance but differences across RNA classes and chemotypes, suggesting tailored scoring strategies could improve virtual screening.

This study identifies Mitoxantrone hydrochloride as a drug that targets APP and LRRK2 to improve neurodegeneration in Parkinson's disease models.

This study improves virtual screening of AChE inhibitors by combining multi-structure covalent docking with a machine learning consensus model, achieving higher predictive accuracy than single-structure approaches.

This paper discovers and characterizes SH6, a small molecule degrader of ZBTB7A that increases fetal hemoglobin expression in erythroid cells and shows therapeutic potential for β-hemoglobinopathies.

This study identifies a new IMiD-independent drug targeting SALL4's ZFC4 domain, leading to significant tumor growth inhibition in vivo.

The paper introduces a hybrid machine learning framework that improves protein-ligand interaction prediction by combining biophysicochemical insights with large-scale representations, achieving faster and more accurate virtual screening.