论文标题
可扩展的杂交深神经网络/可极化电位生物分子模拟，包括远程效应
Scalable Hybrid Deep Neural Networks/Polarizable Potentials Biomolecular Simulations including long-range effects
论文作者
论文摘要
Deep-HP是\ tinkerhp \ Multi-GPUS分子动力学（MD）的可扩展扩展，可实现Pytorch/Tensorflow深神经网络（DNNS）模型的使用。深HP通过提供100k-ATOM生物系统的NS模拟访问的数量级来提高DNNS MD功能，同时提供与任何经典（FFS）和多体极化（PFFS）力场偶联DNN的可能性。因此，它允许引入用于配体结合研究设计的ANI-2X/变形虫混合极化电位，在该研究中，使用Amoeba PFF计算溶剂 - 溶剂和溶剂 - 溶剂相互作用，而溶质 - 溶质溶剂是由Ani-2X DNN计算的。 ANI-2X/变形虫通过有效的粒子网状Ewald实现明确包括Amoeba的物理长距离相互作用，同时保留了Ani-2X的溶质溶质短距离量子机械精度。 DNNS/PFF分区可以用户定义，允许混合模拟包括生物仿真关键成分，例如可极化溶剂，可极化的柜台离子等... Ani-2X/Amoeba使用针对低核能模型的多次启动策略加速了Ani-2X/Amoeba。它主要评估变形虫力，同时仅通过校正步骤包括ANI-2X的力，从而在标准速度Verlet积分上得出了数量级加速度的顺序。在4个溶剂中模拟超过10美元的$ $ $，我们计算了带电/未充电的配体溶剂溶解能，并从Sampl挑战中计算了宿主 - 阵线复合物的绝对结合自由能。 ANI-2X/Amoeba平均误差在化学精度之内，在生物物理学和药物发现中以力场成本以力场成本开辟了通往大型混合DNNS模拟的道路。
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 $μ$, 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.
