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Deep-HP is a scalable extension of the \TinkerHP\ multi-GPUs moleculardynamics (MD) package enabling the use of Pytorch/TensorFlow Deep NeuralNetworks (DNNs) models. Deep-HP increases DNNs MD capabilities by orders ofmagnitude offering access to ns simulations for 100k-atom biosystems whileoffering the possibility of coupling DNNs to any classical (FFs) and many-bodypolarizable (PFFs) force fields. It allows therefore to introduce theANI-2X/AMOEBA hybrid polarizable potential designed for ligand binding studieswhere solvent-solvent and solvent-solute interactions are computed with theAMOEBA PFF while solute-solute ones are computed by the ANI-2x DNN.ANI-2X/AMOEBA explicitly includes AMOEBA's physical long-range interactions viaan efficient Particle Mesh Ewald implementation while preserving ANI-2X'ssolute short-range quantum mechanical accuracy. The DNNs/PFFs partition can beuser-defined allowing for hybrid simulations to include biosimulation keyingredients such as polarizable solvents, polarizable counter ions, etc...ANI-2X/AMOEBA is accelerated using a multiple-timestep strategy focusing on themodels contributions to low-frequency modes of nuclear forces. It primarilyevaluates AMOEBA forces while including ANI-2x ones only via correction-stepsresulting in an order of magnitude acceleration over standard Velocity Verletintegration. Simulating more than 10 $\mu$, we compute charged/unchargedligands solvation free energies in 4 solvents, and absolute binding freeenergies of host-guest complexes from SAMPL challenges. ANI-2X/AMOEBA averageerrors are within chemical accuracy opening the path towards large-scale hybridDNNs simulations, at force-field cost, in biophysics and drug discovery.

Scalable Hybrid Deep Neural Networks/Polarizable Potentials Biomolecular Simulations including long-range effects