We selected the CLN025 variant of chignolin (sequence YYDPETGTWY), which forms a β-hairpin turn while folded ( Figure 5). However, we believe that, with an upcoming implementation of neighbor lists, TorchMD can reach a much better performance, albeit still slower than highly specialized codes as ACEMD3 due to the generic nature of PyTorch operations in addition to the PyTorch library overhead. This is not a strongly limiting factor for the CG simulations conducted in this paper as the number of beads remains relatively low for the test case. Most of the performance discrepancy can be attributed to the lack of neighbor lists for nonbonded interactions in TorchMD and is currently prohibitive for much larger systems as the pair distances cannot fit into GPU memory. As it can be seen, TorchMD is around 60 times slower on the test systems than ACEMD3 running on a TITAN V NVIDIA GPU. In Table 1, we can see the three different test systems comprised of a simple periodic water box of 97 water molecules, alanine dipeptide, and trypsin with the ligand benzamidine bound to it. The performance of TorchMD is compared against ACEMD3, (31) a high-performance molecular dynamics code. A step-by-step example of the training and simulating CG model is presented in the tutorial available in the /torchmd/torchmd-cg repository.
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Here, we also describe how to produce a neural network-based coarse-grained model of chignolin, although the methods exposed are general to any other protein. Finally, we present a coarse-grained simulation of a miniprotein, chignolin, (37) using NNP trained on all-atom MD simulation data. Then, we demonstrate end-to-end differentiable capabilities of TorchMD by recovering force-field parameters from a short MD trajectory. (36) In this case, however we cannot run any dynamical simulations as the data set only presents ground state conformations of the molecules, so we are mainly validating the training.
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Second, we validate the training procedure on QM9, a data set of 134k small molecule conformations with energies. First, a set of typical MD use cases (water box, small peptide, protein, and ligand) is used mainly to assess speed and energy conservation.
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To demonstrate the functionalities of TorchMD, here we present some application examples. (29) but not using a differentiable PyTorch environment. Ab initio QM-based training of potentials is being tackled by several groups, including Gao et al., (22) Yao et al., (28) and Schütt et al. (23) demonstrated the use of graph networks to recover empirical atom types. (27) Other efforts have tackled the integration of MD codes with DNN libraries, although in different contexts. Similarly, Jax (25) was used to perform end-to-end differentiable molecular simulations on Lennard-Jones systems (26) and for biomolecular systems as well. (8,9) Second, TorchMD provides the capability to perform end-to-end differentiable simulations, (14,23,24) being differentiable on all of its parameters. The two key points of TorchMD are that, being written in PyTorch, it is very easy to integrate other ML PyTorch models, like ab initio neural network potentials (NNPs) (5,22) and machine learning coarse-grained potentials. (21) TorchMD enables the rapid prototyping and integration of machine-learned potentials by extending the bonded and nonbonded force terms commonly used in MD with DNN-based ones of arbitrary complexity.
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Here, we introduce TorchMD, a molecular dynamics code built from scratch to leverage the primitives of the ML library PyTorch. (13,14) Indeed, molecular modeling on a more granular scale has been tackled by so-called coarse-graining (CG) approaches before, (15−20) but it is particularly interesting in combination with DNNs. More recently, this has been extended to learn a potential of mean force which involves averaging of a potential over some coarse-grained degrees of freedom, (6−12) which however pose challenges in their parametrization. A key feature of using SchNet is that the model is inherently transferable across molecular systems.
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SchNet was originally used in quantum chemistry to predict energies of small molecules from their atomistic representations. The SchNet architecture, (4,5) for instance, learns a set of features using continuous filter convolutions on a graph neural network and predicts the forces and energy of the system. One particularly interesting feature of neural network potentials is that they can learn many-body interactions. As such, DNNs offer a very promising avenue to embed fast-yet-accurate potential energy functions in MD simulations, after training on large-scale databases obtained from more expensive approaches.
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Machine learning (ML) potentials have become especially attractive with the advent of deep neural network (DNN) architectures, which enable the example-driven definition of arbitrarily complex functions and their derivatives.