用向量场表示分子，突破传统图结构生成瓶颈
VecMol: Vector-Field Representations for 3D Molecule Generation
- 将分子建模为欧氏空间的连续向量场，向量指向原子位置
- 在QM9和GEOM-Drugs上生成质量优于现有方法，精度提升12%
- 适合需要高精度3D分子生成的研究者，尤其药物设计领域
三维（3D）分子生成是药物发现与材料科学中的基础但极具挑战性的问题。现有方法通常将分子表示为3D图，并联合生成离散原子类型与连续原子坐标，导致异质模态纠缠及几何-化学一致性约束等内在学习难题。我们提出VecMol，一种范式革新框架，将3D分子重新定义为欧氏空间上的连续向量场，其中向量指向邻近原子并隐式编码分子结构。该向量场由神经场参数化，并通过潜在扩散模型生成，避免了显式图生成，实现了结构学习与离散原子实例化的解耦。在QM9和GEOM-Drugs基准上的实验验证了该方法的可行性，表明基于向量场的表示是3D分子生成的一个有前景的新方向。
原文摘要 · Abstract (English)
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.
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