Score-based 3D molecule generation with neural fields

• URL: http://arxiv.org/abs/2501.08508v1
• Date: Wed, 15 Jan 2025 01:10:59 GMT
• Title: Score-based 3D molecule generation with neural fields
• Authors: Matthieu Kirchmeyer, Pedro O. Pinheiro, Saeed Saremi,
• Abstract summary: We introduce a new representation for 3D molecules based on their continuous atomic density fields.<n>We propose a new model based on walk-jump sampling for unconditional 3D molecule generation in the continuous space using neural fields.<n>Our model, FuncMol, encodes molecular fields into latent codes using a conditional neural field.<n>FuncMol performs all-atom generation of 3D molecules without assumptions on the molecular structure and scales well with the size of molecules, unlike most approaches.
• Score: 10.0889807546726
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: We introduce a new representation for 3D molecules based on their continuous atomic density fields. Using this representation, we propose a new model based on walk-jump sampling for unconditional 3D molecule generation in the continuous space using neural fields. Our model, FuncMol, encodes molecular fields into latent codes using a conditional neural field, samples noisy codes from a Gaussian-smoothed distribution with Langevin MCMC (walk), denoises these samples in a single step (jump), and finally decodes them into molecular fields. FuncMol performs all-atom generation of 3D molecules without assumptions on the molecular structure and scales well with the size of molecules, unlike most approaches. Our method achieves competitive results on drug-like molecules and easily scales to macro-cyclic peptides, with at least one order of magnitude faster sampling. The code is available at https://github.com/prescient-design/funcmol.

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