MING: A Functional Approach to Learning Molecular Generative Models

• URL: http://arxiv.org/abs/2410.12522v2
• Date: Mon, 17 Feb 2025 08:09:21 GMT
• Title: MING: A Functional Approach to Learning Molecular Generative Models
• Authors: Van Khoa Nguyen, Maciej Falkiewicz, Giangiacomo Mercatali, Alexandros Kalousis,
• Abstract summary: This paper introduces a novel paradigm for learning molecule generative models based on functional representations. We propose Molecular Implicit Neural Generation (MING), a diffusion-based model that learns molecular distributions in the function space.
• Score: 46.189683355768736
• License:
• 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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