HyperDiffusionFields (HyDiF): Diffusion-Guided Hypernetworks for Learning Implicit Molecular Neural Fields

• URL: http://arxiv.org/abs/2510.18122v1
• Date: Mon, 20 Oct 2025 21:41:10 GMT
• Title: HyperDiffusionFields (HyDiF): Diffusion-Guided Hypernetworks for Learning Implicit Molecular Neural Fields
• Authors: Sudarshan Babu, Phillip Lo, Xiao Zhang, Aadi Srivastava, Ali Davariashtiyani, Jason Perera, Michael Maire, Aly A. Khan,
• Abstract summary: We introduce HyperDiffusionFields (HyDiF), a framework that models 3D molecular conformers as continuous fields.<n>At the core of our approach is the Molecular Directional Field (MDF), a vector field that maps any point in space to the direction of the nearest atom of a particular type.<n>We demonstrate that our approach scales to larger biomolecules, illustrating a promising direction for field-based molecular modeling.
• Score: 12.849722578846178
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: We introduce HyperDiffusionFields (HyDiF), a framework that models 3D molecular conformers as continuous fields rather than discrete atomic coordinates or graphs. At the core of our approach is the Molecular Directional Field (MDF), a vector field that maps any point in space to the direction of the nearest atom of a particular type. We represent MDFs using molecule-specific neural implicit fields, which we call Molecular Neural Fields (MNFs). To enable learning across molecules and facilitate generalization, we adopt an approach where a shared hypernetwork, conditioned on a molecule, generates the weights of the given molecule's MNF. To endow the model with generative capabilities, we train the hypernetwork as a denoising diffusion model, enabling sampling in the function space of molecular fields. Our design naturally extends to a masked diffusion mechanism to support structure-conditioned generation tasks, such as molecular inpainting, by selectively noising regions of the field. Beyond generation, the localized and continuous nature of MDFs enables spatially fine-grained feature extraction for molecular property prediction, something not easily achievable with graph or point cloud based methods. Furthermore, we demonstrate that our approach scales to larger biomolecules, illustrating a promising direction for field-based molecular modeling.

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