FlowMol3：用于三维从头小分子生成的流匹配
FlowMol3: Flow Matching for 3D De Novo Small-Molecule Generation
AI总结：
FlowMol3通过自条件化、伪原子和训练时几何扰动三种低成本技术，在不改变架构的情况下大幅提升了全原子小分子生成的性能，以更少参数实现近100%分子有效性并更准确再现训练数据分布。
摘要：
一个能够采样具有所需性质的现实分子的生成模型，可以加速广泛领域中的化学发现。为了实现这一目标，大量工作集中在开发能够联合采样分子拓扑和三维结构的模型上。我们提出了FlowMol3，这是一个开源的、多模态的流匹配模型，推动了全原子小分子生成的最新技术水平。与之前的FlowMol版本相比，其显著的性能提升是在没有改变图神经网络架构或底层流匹配公式的情况下实现的。相反，FlowMol3的改进来自三种与架构无关的技术，这些技术带来的计算成本可以忽略不计：自条件化、伪原子和训练时几何扰动。FlowMol3对于带显式氢的药物样分子实现了接近100%的分子有效性，更准确地再现了其训练数据的官能团组成和几何结构，并且与可比方法相比，可学习参数量少了一个数量级。我们假设这些技术缓解了影响基于传输的生成模型的一种普遍病理，使得在推理过程中能够检测和校正分布漂移。我们的结果突出了简单、可迁移的策略，用于提高基于扩散和流的分子生成模型的稳定性和质量。
英文摘要：
A generative model capable of sampling realistic molecules with desired properties could accelerate chemical discovery across a wide range of applications. Toward this goal, significant effort has focused on developing models that jointly sample molecular topology and 3D structure. We present FlowMol3, an open-source, multi-modal flow matching model that advances the state of the art for all-atom, small-molecule generation. Its substantial performance gains over previous FlowMol versions are achieved without changes to the graph neural network architecture or the underlying flow matching formulation. Instead, FlowMol3's improvements arise from three architecture-agnostic techniques that incur negligible computational cost: self-conditioning, fake atoms, and train-time geometry distortion. FlowMol3 achieves nearly 100% molecular validity for drug-like molecules with explicit hydrogens, more accurately reproduces the functional group composition and geometry of its training data, and does so with an order of magnitude fewer learnable parameters than comparable methods. We hypothesize that these techniques mitigate a general pathology affecting transport-based generative models, enabling detection and correction of distribution drift during inference. Our results highlight simple, transferable strategies for improving the stability and quality of diffusion- and flow-based molecular generative models.
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。
