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arXiv · 2410.12522

MING: A Functional Approach to Learning Molecular Generative Models

Van Khoa Nguyen·Maciej Falkiewicz·Giangiacomo Mercatali·Alexandros Kalousis
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Abstract

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 the function space. Unlike standard diffusion processes in the data space, MING employs a novel functional denoising probabilistic process, which jointly denoises 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 enables 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. The code is available at https://github.com/v18nguye/MING.

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Van Khoa Nguyen, Maciej Falkiewicz, Giangiacomo Mercatali, Alexandros Kalousis. 2025-02-17. MING: A Functional Approach to Learning Molecular Generative Models. https://arxiv.org/abs/2410.12522

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