Subinoy Adhikari

The paper fine-tunes a protein language model for PTM-site prediction and shows how model size, annotation depth, and modification chemistry affect PTM recognition.

This paper introduces a Transformer-based framework to predict full molecular dynamics trajectories from limited initial data, showing it captures long-range temporal structures and improves sampling compared to traditional methods.

The paper introduces a dataset and a graph-transformer model to predict IDR-protein binding affinity, achieving high accuracy and generalizing to designed binders.

This paper introduces a GAN-based method to generate molecular dynamics trajectories with kinetic fidelity, enabling efficient sampling of rare events in complex systems.

This study uses deep learning and molecular dynamics to show how a single methylation change in Glycocin F drastically reduces its activity by altering its structural dynamics.

This study uses machine learning to show that fasudil modulates α-synuclein's conformational states through entropic expansion, offering insights into small molecule interactions with IDPs.