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.

翻译：三维分子生成建模是药物发现和材料科学领域一个基础而具有挑战性的问题。现有方法通常将分子表示为三维图，并协同生成离散的原子类型与连续的原子坐标，这导致了固有的学习困难，例如异质模态纠缠以及几何-化学一致性约束。我们提出了VecMol，这是一个范式转换的框架，它通过将三维分子建模为欧几里得空间上的连续矢量场来重新构想分子表示，其中矢量指向附近的原子并隐式地编码分子结构。该矢量场由神经场参数化，并使用潜在扩散模型生成，从而避免了显式的图生成，并将结构学习与离散原子实例化解耦。在QM9和GEOM-Drugs基准测试上的实验验证了这一新颖方法的可行性，表明基于矢量场的表示是三维分子生成的一个有前景的新方向。