DiffMD: A Geometric Diffusion Model for Molecular Dynamics Simulations

Abstract

Molecular dynamics (MD) has long been the de facto choice for modeling complex atomistic systems from first principles, and recently deep learning become a popular way to accelerate it. Notwithstanding, preceding approaches depend on intermediate variables such as the potential energy or force fields to update atomic positions, which requires additional computations to perform back-propagation. To waive this requirement, we propose a novel model called ScoreMD by directly estimating the gradient of the log density of molecular conformations. Moreover, we analyze that diffusion processes highly accord with the principle of enhanced sampling in MD simulations, and is therefore a perfect match to our sequential conformation generation task. That is, ScoreMD perturbs the molecular structure with a conditional noise depending on atomic accelerations and employs conformations at previous timeframes as the prior distribution for sampling. Another challenge of modeling such a conformation generation process is that the molecule is kinetic instead of static, which no prior studies strictly consider. To solve this challenge, we introduce a equivariant geometric Transformer as a score function in the diffusion process to calculate the corresponding gradient. It incorporates the directions and velocities of atomic motions via 3D spherical Fourier-Bessel representations. With multiple architectural improvements, we outperforms state-of-the-art baselines on MD17 and isomers of C7O2H10. This research provides new insights into the acceleration of new material and drug discovery. IntroductionsMolecular dynamics (MD) [18,33,86], an in silico tool that simulates complex atomic systems based on first principles, has exerted dramatic impacts in scientific research. Instead of yielding an average structure by experimental approaches including X-ray crystallography and cryo-EM, MD simulations can capture the sequential behavior of molecules in full atomic details at very fine temporal resolution, and thus allows researchers to quantify how much various regions of the molecule move at equilibrium and what types of structural fluctuations they undergo. In the areas of molecular biology and drug discovery [26], the most basic and intuitive application of MD is to assess the mobility or flexibility * Equal contributions.† The corresponding author.Preprint. Under review.

Pre‐Training of Equivariant Graph Matching Networks with Conformation Flexibility for Drug Binding

“…This operation of perturbation had both theoretical and empirical supports. As demonstrated by Wu, Zhang, Jin, Jiang, and Li, [79] the denoising diffusion architecture [80][81][82] had a strong connectivity with the enhanced sampling method in MD, [83][84][85][86] where energy was injected into the microscopic system to smooth biomolecular potential energy surface and decrease energy barriers. Besides, it had been shown in Godwin et al [33] that the simple noise regularization could be an effective way to address oversmoothing.…”