论文标题

MDM:3D分子产生的分子扩散模型

MDM: Molecular Diffusion Model for 3D Molecule Generation

论文作者

Huang, Lei, Zhang, Hengtong, Xu, Tingyang, Wong, Ka-Chun

论文摘要

分子产生,尤其是从头开始产生3D分子几何形状(即3d \ textit {de novo} Generation)已成为药物设计中的一项基本任务。现有的基于扩散的3D分子生成方法可能会遭受性能不令人满意的性能,尤其是在产生大分子时。同时,产生的分子缺乏足够的多样性。本文提出了一个新颖的扩散模型,以应对这两个挑战。 首先,原子间关系不在分子的3D点云表示中。因此,现有生成模型很难捕获潜在的原子间力和丰富的局部约束。为了应对这一挑战,我们建议增强潜在的原子间力,并进一步涉及双重性编码器,以编码具有不同强度的原子质力。其次,现有基于扩散的模型基本上是沿数据密度梯度的几何元素。这样的过程在Langevin动力学的中间步骤中缺乏足够的探索。为了解决这个问题,我们在每个扩散/反向步骤中引入了一个分布控制变量,以实施彻底的探索并进一步改善发电多样性。 对多个基准测试的广泛实验表明,所提出的模型对无条件和有条件生成任务的现有方法显着胜过现有的方法。我们还进行案例研究以帮助了解产生的分子的理化特性。

Molecule generation, especially generating 3D molecular geometries from scratch (i.e., 3D \textit{de novo} generation), has become a fundamental task in drug designs. Existing diffusion-based 3D molecule generation methods could suffer from unsatisfactory performances, especially when generating large molecules. At the same time, the generated molecules lack enough diversity. This paper proposes a novel diffusion model to address those two challenges. First, interatomic relations are not in molecules' 3D point cloud representations. Thus, it is difficult for existing generative models to capture the potential interatomic forces and abundant local constraints. To tackle this challenge, we propose to augment the potential interatomic forces and further involve dual equivariant encoders to encode interatomic forces of different strengths. Second, existing diffusion-based models essentially shift elements in geometry along the gradient of data density. Such a process lacks enough exploration in the intermediate steps of the Langevin dynamics. To address this issue, we introduce a distributional controlling variable in each diffusion/reverse step to enforce thorough explorations and further improve generation diversity. Extensive experiments on multiple benchmarks demonstrate that the proposed model significantly outperforms existing methods for both unconditional and conditional generation tasks. We also conduct case studies to help understand the physicochemical properties of the generated molecules.

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