Traditional molecule generation methods often rely on sequence or graph-based representations, which can limit their expressive power or require complex permutation-equivariant architectures. This paper introduces a novel paradigm for learning molecule generative models based on functional representations. Specifically, we propose Molecular Implicit Neural Generation (MING), a diffusion-based model that learns molecular distributions in function space. Unlike standard diffusion processes in data space, MING employs a novel functional denoising probabilistic process, which jointly denoises the information in both the function's input and output spaces by leveraging an expectation-maximization procedure for latent implicit neural representations of data. This approach allows for a simple yet effective model design that accurately captures underlying function distributions. Experimental results on molecule-related datasets demonstrate MING's superior performance and ability to generate plausible molecular samples, surpassing state-of-the-art data-space methods while offering a more streamlined architecture and significantly faster generation times.

翻译：传统的分子生成方法通常依赖于序列或图表示，这可能限制其表达能力或需要复杂的置换等变架构。本文提出了一种基于函数表示学习分子生成模型的新范式。具体而言，我们提出了分子隐式神经生成（MING），这是一种在函数空间中学习分子分布的扩散模型。与数据空间中的标准扩散过程不同，MING采用了一种新颖的函数去噪概率过程，该过程通过利用数据隐式神经表示的期望最大化过程，联合去噪函数输入和输出空间中的信息。这种方法实现了一种简单而有效的模型设计，能够准确捕捉底层函数分布。在分子相关数据集上的实验结果表明，MING具有优越的性能和生成合理分子样本的能力，超越了最先进的数据空间方法，同时提供了更精简的架构和显著更快的生成速度。