System and method for molecular reconstruction from molecular probability distributions
US12223435B2 · kind B2 · utility
Assignee
Inventors
Key dates
| Filing date | Dec 1, 2021 |
| Grant date | Feb 11, 2025 |
| Priority date | — |
| Expiry date | Oct 20, 2043 |
Classification
- Technology area (CPC G)Physics
- CPC primaryG16C20/90
- WIPO fieldComputer technology
- WIPO sectorElectrical engineering
Abstract
A system and method comprising a transmoler that identifies common substructures of a given 3D conformer and predicts its structural information. First, based on contrastive learning, substructure embeddings are learned in an unsupervised manner. Secondly, a novel oriented 3D object regressor predicts the dimensions and directions of each substructure in a conformer as well as its fingerprint embedding which are used to create differentiable junction tree molecular graphs. Lastly, using the junction tree graphs, molecular representations such as DeepSMILES are generated which represent new and novel molecules. The system may also generate conformers directly from a pocket. A pocket may be input to the model and the model learns to generate structures which can fit that pocket by conditioning the generative system. Furthermore, structure-based contrastive embeddings generated for transmoler can be recycled in structure-based generative modelling.
Source: USPTO / EPO open patent data. Objective bibliographic and citation counts.