Polarizable Gaussian Multipole force field
A next-generation polarizable force field built on Gaussian-distributed charges and multipoles.
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We develop computational methods to study and predict biomolecular
structures, functions, and intermolecular interactions at atomic
detail — from polarizable force fields and GPU-accelerated implicit
solvent to machine-learned molecular surfaces.
Machine learning for molecular surfaces and implicit solvent
Neural models that accelerate molecular-surface generation and Poisson–Boltzmann electrostatics.
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Binding free energies — MM/PBSA and alchemical methods
GPU-accelerated MM/PBSA and robust free-energy perturbation for binding-affinity prediction.
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Allostery and disease-relevant protein dynamics
Cryptic allosteric sites and functional mutations in signaling and cancer targets.
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