Enregistré dans:
| Auteurs principaux: | , , , , , , , , , , , , |
|---|---|
| Format: | Preprint |
| Publié: |
2026
|
| Sujets: | |
| Accès en ligne: | https://arxiv.org/abs/2601.22327 |
| Tags: |
Ajouter un tag
Pas de tags, Soyez le premier à ajouter un tag!
|
| _version_ | 1866911408606674944 |
|---|---|
| author | Wang, Zehong Han, Xiaolong Yang, Qi Tang, Xiangru Wu, Fang Guo, Xiaoguang Sun, Weixiang Ma, Tianyi Lio, Pietro Cong, Le Wang, Sheng Zhang, Chuxu Ye, Yanfang |
| author_facet | Wang, Zehong Han, Xiaolong Yang, Qi Tang, Xiangru Wu, Fang Guo, Xiaoguang Sun, Weixiang Ma, Tianyi Lio, Pietro Cong, Le Wang, Sheng Zhang, Chuxu Ye, Yanfang |
| contents | Molecular representations fundamentally shape how machine learning systems reason about molecular structure and physical properties. Most existing approaches adopt a discrete pipeline: molecules are encoded as sequences, graphs, or point clouds, mapped to fixed-dimensional embeddings, and then used for task-specific prediction. This paradigm treats molecules as discrete objects, despite their intrinsically continuous and field-like physical nature. We argue that molecular learning can instead be formulated as learning in function space. Specifically, we model each molecule as a continuous function over three-dimensional (3D) space and treat this molecular field as the primary object of representation. From this perspective, conventional molecular representations arise as particular sampling schemes of an underlying continuous object. We instantiate this formulation with MolField, a hyper-network-based framework that learns distributions over molecular fields. To ensure physical consistency, these functions are defined over canonicalized coordinates, yielding invariance to global SE(3) transformations. To enable learning directly over functions, we introduce a structured weight tokenization and train a sequence-based hyper-network to model a shared prior over molecular fields. We evaluate MolField on molecular dynamics and property prediction. Our results show that treating molecules as continuous functions fundamentally changes how molecular representations generalize across tasks and yields downstream behavior that is stable to how molecules are discretized or queried. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2601_22327 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | Molecular Representations in Implicit Functional Space via Hyper-Networks Wang, Zehong Han, Xiaolong Yang, Qi Tang, Xiangru Wu, Fang Guo, Xiaoguang Sun, Weixiang Ma, Tianyi Lio, Pietro Cong, Le Wang, Sheng Zhang, Chuxu Ye, Yanfang Machine Learning Molecular representations fundamentally shape how machine learning systems reason about molecular structure and physical properties. Most existing approaches adopt a discrete pipeline: molecules are encoded as sequences, graphs, or point clouds, mapped to fixed-dimensional embeddings, and then used for task-specific prediction. This paradigm treats molecules as discrete objects, despite their intrinsically continuous and field-like physical nature. We argue that molecular learning can instead be formulated as learning in function space. Specifically, we model each molecule as a continuous function over three-dimensional (3D) space and treat this molecular field as the primary object of representation. From this perspective, conventional molecular representations arise as particular sampling schemes of an underlying continuous object. We instantiate this formulation with MolField, a hyper-network-based framework that learns distributions over molecular fields. To ensure physical consistency, these functions are defined over canonicalized coordinates, yielding invariance to global SE(3) transformations. To enable learning directly over functions, we introduce a structured weight tokenization and train a sequence-based hyper-network to model a shared prior over molecular fields. We evaluate MolField on molecular dynamics and property prediction. Our results show that treating molecules as continuous functions fundamentally changes how molecular representations generalize across tasks and yields downstream behavior that is stable to how molecules are discretized or queried. |
| title | Molecular Representations in Implicit Functional Space via Hyper-Networks |
| topic | Machine Learning |
| url | https://arxiv.org/abs/2601.22327 |