Molecular graph generation from structural features using an artificial neural network
US12217834B2 · kind B2 · utility
Assignee
Inventors
Key dates
| Filing date | May 29, 2020 |
| Grant date | Feb 4, 2025 |
| Priority date | — |
| Expiry date | Jul 5, 2041 |
Classification
- Technology area (CPC G)Physics
- CPC primaryG16C20/80
- WIPO fieldComputer technology
- WIPO sectorElectrical engineering
Abstract
Discovering molecules (which may be known or may never have been cataloged or ever synthesized) that have desired characteristics is addressed using a machine learning approach. As compared to a brute-force search of a database of known molecules, which may not be computationally feasible, the present machine learning approach renders identification of both known and unknown molecules computationally tractable. Furthermore, the computational effort is largely shifted to training of the machine learning system using a database of known molecules, and the generation of molecules to match any particular characteristics requires relatively little computation. The molecules using the present approach may be further studied, for example, with computer-based simulation or after physical synthesis using biological experimentation to ultimately yield useful chemical compounds.
Source: USPTO / EPO open patent data. Objective bibliographic and citation counts.