VecMol: Vector-Field Representations for 3D Molecule Generation

2026-03-13

AI Summary

This work addresses the challenges of heterogeneous modality entanglement and geometric-chemical inconsistency in 3D molecular generation, which arise from jointly modeling discrete atom types and continuous coordinates. To overcome these issues, the authors propose a vector field–based continuous representation paradigm, where a molecule is modeled as a continuous vector field in Euclidean space, with its direction implicitly encoding local structural information—eliminating the need for explicit graph construction. The vector field is parameterized by a neural field and integrated into a latent diffusion model to enable end-to-end generation, naturally decoupling structure learning from atom instantiation. Experiments on the QM9 and GEOM-Drugs benchmarks demonstrate the effectiveness of the approach, marking the first successful demonstration of vector field representations for 3D molecular generation and highlighting their feasibility and potential.

Technology Category

• Machine Learning: Deep Generative Models & Autoencoders
• Computer Vision: Diffusion Models for Vision
• Natural Language Processing: Generation

Application Category

• Graph Algorithms and Modeling for the Web: Foundation models and LLMs for Web-related graphs
• Semantics and Knowledge: Methods to enhance, augment, integrate or synergize semantic models such as knowledge graphs and LLMs
• User Modeling, Personalization and Recommendation: Federated recommendation systems and personalization

Abstract

Generative modeling of three-dimensional (3D) molecules is a fundamental yet challenging problem in drug discovery and materials science. Existing approaches typically represent molecules as 3D graphs and co-generate discrete atom types with continuous atomic coordinates, leading to intrinsic learning difficulties such as heterogeneous modality entanglement and geometry-chemistry coherence constraints. We propose VecMol, a paradigm-shifting framework that reimagines molecular representation by modeling 3D molecules as continuous vector fields over Euclidean space, where vectors point toward nearby atoms and implicitly encode molecular structure. The vector field is parameterized by a neural field and generated using a latent diffusion model, avoiding explicit graph generation and decoupling structure learning from discrete atom instantiation. Experiments on the QM9 and GEOM-Drugs benchmarks validate the feasibility of this novel approach, suggesting vector-field-based representations as a promising new direction for 3D molecular generation.

Problem

• 3D molecule generation
• molecular representation
• generative modeling
• drug discovery
• materials science

Innovation

• vector field
• 3D molecule generation
• neural field
• latent diffusion model
• molecular representation