Molecular dynamics (MD) has long been the de facto choice for simulating complex atomistic systems from first principles. Recently deep learning models become a popular way to accelerate MD. Notwithstanding, existing models 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 DiffMD by directly estimating the gradient of the log density of molecular conformations. DiffMD relies on a score-based denoising diffusion generative model that perturbs the molecular structure with a conditional noise depending on atomic accelerations and treats conformations at previous timeframes as the prior distribution for sampling. Another challenge of modeling such a conformation generation process is that a molecule is kinetic instead of static, which no prior works have strictly studied. To solve this challenge, we propose an equivariant geometric Transformer as the score function in the diffusion process to calculate corresponding gradients. It incorporates the directions and velocities of atomic motions via 3D spherical Fourier-Bessel representations. With multiple architectural improvements, we outperform state-of-the-art baselines on MD17 and isomers of C7O2H10 datasets. This work contributes to accelerating material and drug discovery.
翻译:分子动态( MD) 长期以来一直是从最初的原则中模拟复杂原子系统的事实上的选择。 最近深层次的学习模型成为加速MD的流行方式。 尽管如此, 现有的模型依赖于中间变量, 如潜在的能量或更新原子位置的强制场, 需要额外的计算来进行反向反向分析。 为了放弃这一要求, 我们提议了一个名为 DiffMD 的新模型, 直接估计分子一致性的日志密度的梯度。 DiffMD 依靠一个基于分数的分数分解扩散基因化模型, 该模型在根据原子加速度和在先前的取样时间框架内处理符合的有条件噪音, 作为先前的抽样分布。 建模这种相容生成过程的另一个挑战是分子是动的而不是静态的, 而以前没有认真研究过这种静态。 为了解决这一挑战, 我们建议用一个等离式的几何几何几何几何变变变变变变变变变变变变器作为扩散过程的分数函数, 来计算相应的梯度。 它包含通过 3D Fourier- besel 表示, 有条件的噪音, 和处理符合先前的时间段的相, 在先前的分布中, 之前的分布中, 我们超越了这个结构改进了同步的模型的模型的模型的模型的模型的模型, 17, 我们超越了这个模型的模型的模型的模型的模型的模型的模型的模型的模型的模型的模型的模型的定位, 。