Nidhi Jatana

PandoraRLO uses reinforcement learning to predict optimized ligand poses for protein-ligand interactions, achieving accuracy within 0.5 to 4 angstroms for many test complexes.

This paper introduces PandoraRL, a reinforcement learning model using DQN and graph convolution to predict optimal ligand poses for SARS-CoV1 Mprotease.

This study introduces a reinforcement learning method for predicting optimized ligand poses and identifying binding sites using graph CNNs.

The paper identifies mutational patterns in SARS-CoV-2 spike protein using genomic and structural data, finding structural changes that may affect vaccine efficacy.

The paper identifies a mutation in the papA2 gene of Mycobacterium tuberculosis that leads to loss of sulfolipids by disrupting protein stability and structure.