# Score-based 3D molecule generation with neural fields

arXiv: https://arxiv.org/abs/2501.08508 · HTML: https://mlsanity.com/paper/2501.08508/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- First posted on arXiv 15 Jan 2025.

## Abstract (arXiv)

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.