标题： 基于分数的3D分子生成与神经场 显示英文标题

标题： Score-based 3D molecule generation with neural fields

摘要： 我们引入了一种基于连续原子密度场的新3D分子表示方法。 使用这种表示方法，我们提出了一种基于行走-跳跃采样的新模型，用于在连续空间中无条件生成3D分子，该模型使用神经场。 我们的模型FuncMol通过条件神经场将分子场编码为潜在代码，从高斯平滑分布中使用Langevin MCMC（行走）采样噪声代码，在一步中去噪这些样本（跳跃），最后将其解码为分子场。 FuncMol无需对分子结构做出假设即可生成所有原子的3D分子，并且随着分子大小的增加而表现出良好的扩展性，这与大多数方法不同。 我们的方法在药物样分子上取得了有竞争力的结果，并且可以轻松扩展到大环肽，采样速度至少快一个数量级。 代码可在https://github.com/prescient-design/funcmol获取。 显示英文摘要

摘要： We introduce a new representation for 3D molecules based on their continuous atomic density fields. Using this representation, we propose a new model based on walk-jump sampling for unconditional 3D molecule generation in the continuous space using neural fields. Our model, FuncMol, encodes molecular fields into latent codes using a conditional neural field, samples noisy codes from a Gaussian-smoothed distribution with Langevin MCMC (walk), denoises these samples in a single step (jump), and finally decodes them into molecular fields. FuncMol performs all-atom generation of 3D molecules without assumptions on the molecular structure and scales well with the size of molecules, unlike most approaches. Our method achieves competitive results on drug-like molecules and easily scales to macro-cyclic peptides, with at least one order of magnitude faster sampling. The code is available at https://github.com/prescient-design/funcmol.