Scalable hybrid deep neural networks/polarizable potentials biomolecular simulations including long-range effects
Théo Jaffrelot Inizan, Thomas Plé, Olivier Adjoua, Pengyu Ren, Hatice Gökcan, Olexandr Isayev, Louis Lagardère, Jean‐Philip Piquemal
Abstract
correction-steps resulting in an order of magnitude acceleration over standard Velocity Verlet integration. Simulating more than 10 μs, we compute charged/uncharged ligand solvation free energies in 4 solvents, and absolute binding free energies of host-guest complexes from SAMPL challenges. ANI-2X/AMOEBA average errors are discussed in terms of statistical uncertainty and appear in the range of chemical accuracy compared to experiment. The availability of the Deep-HP computational platform opens the path towards large-scale hybrid DNN simulations, at force-field cost, in biophysics and drug discovery.
Topics & Concepts
ScalabilityRange (aeronautics)PolarizabilityArtificial neural networkComputer scienceDeep neural networksArtificial intelligenceStatistical physicsMaterials sciencePhysicsQuantum mechanicsDatabaseComposite materialMoleculeMachine Learning in Materials ScienceComputational Drug Discovery MethodsProtein Structure and Dynamics