  Deep-HP is a scalable extension of the \TinkerHP\ multi-GPUs molecular
dynamics (MD) package enabling the use of Pytorch/TensorFlow Deep Neural
Networks (DNNs) models. Deep-HP increases DNNs MD capabilities by orders of
magnitude offering access to ns simulations for 100k-atom biosystems while
offering the possibility of coupling DNNs to any classical (FFs) and many-body
polarizable (PFFs) force fields. It allows therefore to introduce the
ANI-2X/AMOEBA hybrid polarizable potential designed for ligand binding studies
where solvent-solvent and solvent-solute interactions are computed with the
AMOEBA PFF while solute-solute ones are computed by the ANI-2x DNN.
ANI-2X/AMOEBA explicitly includes AMOEBA's physical long-range interactions via
an efficient Particle Mesh Ewald implementation while preserving ANI-2X's
solute short-range quantum mechanical accuracy. The DNNs/PFFs partition can be
user-defined allowing for hybrid simulations to include biosimulation key
ingredients such as polarizable solvents, polarizable counter ions, etc...
ANI-2X/AMOEBA is accelerated using a multiple-timestep strategy focusing on the
models contributions to low-frequency modes of nuclear forces. It primarily
evaluates AMOEBA forces while including ANI-2x ones only via correction-steps
resulting in an order of magnitude acceleration over standard Velocity Verlet
integration. Simulating more than 10 $\mu$, we compute charged/uncharged
ligands solvation free energies in 4 solvents, and absolute binding free
energies of host-guest complexes from SAMPL challenges. ANI-2X/AMOEBA average
errors are within chemical accuracy opening the path towards large-scale hybrid
DNNs simulations, at force-field cost, in biophysics and drug discovery.
