Raj S. Roy

CARTnet improves B-cell epitope prediction using a contact-aware deep learning framework, outperforming existing methods on internal and external datasets.

Agent K automates the mapping of bacterial genomes to biosynthetic pathways and metabolite discovery using an agent-orchestrated framework with a large-language model.

This work evaluates factors affecting generalization of GNN-based TCR-pMHC binding predictors to unseen peptides and proposes architectural improvements.

This paper introduces a computational pipeline for antigen discovery that uses filtering and machine learning to identify conserved neutralizing epitopes in viruses like SARS-CoV-2 and RVFV.

The paper introduces MULTICOM, a system that enhances AlphaFold-Multimer for protein complex structure prediction, achieving higher TM-scores in CASP15.

The paper improves AlphaFold2-based protein structure prediction using MULTICOM_refine, achieving higher accuracy in CASP15 compared to standard AlphaFold2.

The paper introduces a hybrid method combining pairwise similarity and deep learning for estimating protein complex model accuracy, achieving high correlation and low ranking loss in CASP15.

This paper introduces DRLComplex, a deep reinforcement learning method for reconstructing protein dimer quaternary structures from interchain contacts, achieving high accuracy on standard datasets.

This paper introduces DRCon, a deep learning model for predicting interchain contacts in homodimers, achieving higher accuracy than existing methods on benchmark datasets.

This paper introduces a gradient descent algorithm to reconstruct protein quaternary structures from inter-chain contacts, showing it outperforms other methods and can produce high-quality models even with predicted contacts.

The paper uses deep learning on monomeric multiple sequence alignments to predict interchain contacts in homomultimeric proteins, achieving high precision in some cases.