用于3D从头分子生成的混合连续与分类流匹配
Mixed Continuous and Categorical Flow Matching for 3D De Novo Molecule Generation
AI总结：
本文提出FlowMol，一种用于3D从头分子生成的流匹配模型，通过将流匹配扩展到分类数据并比较SimplexFlow与简化方法，实现了优于先前流匹配方法的性能，同时引发了对先验分布设计的思考。
摘要：
能够产生新型分子结构的深度生成模型具有促进化学发现的潜力。扩散模型目前在3D分子生成方面取得了最先进的性能。在这项工作中，我们探索使用流匹配（flow matching）——一种最近提出的、对扩散模型进行推广的生成建模框架——来完成从头分子生成任务。流匹配在模型设计上提供了灵活性；然而，该框架建立在数据为连续值这一假设之上。3D从头分子生成需要联合采样连续变量和分类变量，例如原子位置和原子类型。我们通过构造被约束在分类数据的连续表示（即概率单纯形）上存在的流，将流匹配框架扩展到分类数据。我们将这一扩展称为SimplexFlow。我们探索了使用SimplexFlow进行从头分子生成。然而，我们发现，在实践中，一种不对数据的分类性质做任何特殊处理的更简单方法能够取得相当或更优的性能。基于这些实验，我们提出了FlowMol，这是一个用于3D从头生成模型的流匹配模型，其性能优于先前的流匹配方法，并且我们提出了关于先验分布设计以实现流匹配模型强大性能的重要问题。用于复现本工作的代码和训练模型可在https://github.com/dunni3/FlowMol获取。
英文摘要：
Deep generative models that produce novel molecular structures have the potential to facilitate chemical discovery. Diffusion models currently achieve state of the art performance for 3D molecule generation. In this work, we explore the use of flow matching, a recently proposed generative modeling framework that generalizes diffusion models, for the task of de novo molecule generation. Flow matching provides flexibility in model design; however, the framework is predicated on the assumption of continuously-valued data. 3D de novo molecule generation requires jointly sampling continuous and categorical variables such as atom position and atom type. We extend the flow matching framework to categorical data by constructing flows that are constrained to exist on a continuous representation of categorical data known as the probability simplex. We call this extension SimplexFlow. We explore the use of SimplexFlow for de novo molecule generation. However, we find that, in practice, a simpler approach that makes no accommodations for the categorical nature of the data yields equivalent or superior performance. As a result of these experiments, we present FlowMol, a flow matching model for 3D de novo generative model that achieves improved performance over prior flow matching methods, and we raise important questions about the design of prior distributions for achieving strong performance in flow matching models. Code and trained models for reproducing this work are available at https://github.com/dunni3/FlowMol
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
