Soumendranath Bhakat

REVEAL captures RNA excited states using AI-guided simulations, showing it can identify dominant conformations better than traditional methods.

The paper benchmarks generative AI and AI-accelerated molecular simulation for sampling conformational states of T4 lysozyme, finding that AMS recovers all states and matches experimental data better than generative AI methods.

This study introduces BioEmu, a workflow combining generative AI with molecular simulations to sample protein conformations, showing both broad coverage and specific limitations in capturing conformational heterogeneity.

CryoPhold integrates AlphaFold, cryo-EM, and molecular simulations to predict protein dynamics and structural hotspots in human proteins like GlyT1 and BRAF mutants.

This paper introduces a computational framework to study kinase conformational dynamics by integrating AlphaFold, machine learning, and physics-based simulations to explore metastable states beyond traditional binary classifications.

This paper introduces AlphaFold-SFA, a method combining AlphaFold, slow feature analysis, and metadynamics to accelerate sampling of rare protein events like cryptic pocket opening and allosteric communication.