探索用于3D从头分子生成的离散流匹配
Exploring Discrete Flow Matching for 3D De Novo Molecule Generation
AI总结：
针对3D从头分子生成需离散数据而现有流匹配框架仅适连续数据的问题，本文基准测试现有离散流匹配方法并解释其行为差异，提出参数更少且性能更优的开源模型FlowMol-CTMC，还提出超越局部价键约束的高阶结构基序分子质量度量，发现模型易生成训练分布外的异常官能团。
摘要：
生成新型分子结构的深度生成模型具有促进化学发现的潜力。流匹配是最近提出的一种生成建模框架，已在包括生物分子结构在内的多种任务上取得了令人印象深刻的性能。开创性的流匹配框架仅针对连续数据开发。然而，从头分子设计任务需要生成离散数据，例如原子元素或氨基酸残基序列。最近已提出几种离散流匹配方法来填补这一空白。在这项工作中，我们对现有离散流匹配方法在3D从头小分子生成中的性能进行基准测试，并解释它们的不同行为。因此，我们提出FlowMol-CTMC，这是一种开源模型，在3D从头设计中实现了最先进的性能，且可学习参数比现有方法更少。此外，我们提出使用超越局部化学价键约束、面向高阶结构基序的分子质量度量。这些度量表明，即使满足基本约束，模型仍倾向于生成训练数据分布之外的不常见且可能有问题的官能团。用于复现这项工作的代码和训练模型可在\
英文摘要：
Deep generative models that produce novel molecular structures have the potential to facilitate chemical discovery. Flow matching is a recently proposed generative modeling framework that has achieved impressive performance on a variety of tasks including those on biomolecular structures. The seminal flow matching framework was developed only for continuous data. However, de novo molecular design tasks require generating discrete data such as atomic elements or sequences of amino acid residues. Several discrete flow matching methods have been proposed recently to address this gap. In this work we benchmark the performance of existing discrete flow matching methods for 3D de novo small molecule generation and provide explanations of their differing behavior. As a result we present FlowMol-CTMC, an open-source model that achieves state of the art performance for 3D de novo design with fewer learnable parameters than existing methods. Additionally, we propose the use of metrics that capture molecule quality beyond local chemical valency constraints and towards higher-order structural motifs. These metrics show that even though basic constraints are satisfied, the models tend to produce unusual and potentially problematic functional groups outside of the training data distribution. Code and trained models for reproducing this work are available at \url{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。
