In this paper we tackle the problem of generating conformers of a molecule in
3D space given its molecular graph. We parameterize these conformers as
continuous functions that map elements from the molecular graph to points in 3D
space. We then formulate the problem of learning to generate conformers as
learning a distribution over these functions using a diffusion generative
model, called Molecular Conformer Fields (MCF). Our approach is simple and
scalable, and achieves state-of-the-art performance on challenging molecular
conformer generation benchmarks while making no assumptions about the explicit
structure of molecules (e.g. modeling torsional angles). MCF represents an
advance in extending diffusion models to handle complex scientific problems in
a conceptually simple, scalable and effective manner.

本论文探讨了在给定分子图的情况下如何在三维空间中生成分子构象的问题，并将这些构象参数化为将分子图中的元素映射到三维空间中的连续函数。我们通过使用扩散生成模型 —— 分子构象场（Molecular Conformer Fields，MCF）来学习生成构象的分布。我们的方法简单而可扩展，在具有挑战性的分子构象生成基准测试中取得了最先进的性能，同时对分子的显式结构（例如建模转动角度）不做任何假设。MCF 在将扩散模型扩展到处理复杂科学问题方面表示了一种概念简单、可扩展且有效的进展。