通過去噪體素網格生成3D分子

摘要

我們提出了一種基於分數的新方法，用於生成以原子密度在常規網格上表示的3D分子。首先，我們訓練了一個去噪神經網絡，該網絡學習將具有噪聲分子的平滑分佈映射到真實分子的分佈。然後，我們遵循神經經驗貝葉斯框架[Saremi和Hyvarinen，2019]，並通過兩個步驟生成分子：(i)通過過阻尼朗之萬歷維蒙特卡羅方法從平滑分佈中採樣帶有噪聲的密度網格，以及(ii)通過單步驟對帶有噪聲的網格進行去噪以恢復“乾淨”分子。我們的方法VoxMol以一種基本不同於當前技術水準（即應用於原子點雲的擴散模型）的方式生成分子。它在數據表示、噪聲模型、網絡架構和生成建模算法方面有所不同。VoxMol在無條件的3D分子生成方面取得了與技術水準相當的結果，同時更容易訓練並且生成速度更快。

English

We propose a new score-based approach to generate 3D molecules represented as atomic densities on regular grids. First, we train a denoising neural network that learns to map from a smooth distribution of noisy molecules to the distribution of real molecules. Then, we follow the neural empirical Bayes framework [Saremi and Hyvarinen, 2019] and generate molecules in two steps: (i) sample noisy density grids from a smooth distribution via underdamped Langevin Markov chain Monte Carlo, and (ii) recover the ``clean'' molecule by denoising the noisy grid with a single step. Our method, VoxMol, generates molecules in a fundamentally different way than the current state of the art (i.e., diffusion models applied to atom point clouds). It differs in terms of the data representation, the noise model, the network architecture and the generative modeling algorithm. VoxMol achieves comparable results to state of the art on unconditional 3D molecule generation while being simpler to train and faster to generate molecules.

通過去噪體素網格生成3D分子

3D molecule generation by denoising voxel grids

摘要

Support