| Predicate |
Object |
| assignee |
http://rdf.ncbi.nlm.nih.gov/pubchem/patentassignee/MD5_53b4d53983adbf56dd0e7d0bf96f8ce5 |
| classificationCPCInventive |
http://rdf.ncbi.nlm.nih.gov/pubchem/patentcpc/G06N3-045
http://rdf.ncbi.nlm.nih.gov/pubchem/patentcpc/G06N3-08 |
| classificationIPCInventive |
http://rdf.ncbi.nlm.nih.gov/pubchem/patentipc/G06N3-08
http://rdf.ncbi.nlm.nih.gov/pubchem/patentipc/G06N3-04 |
| filingDate |
2020-08-05-04:00^^<http://www.w3.org/2001/XMLSchema#date> |
| inventor |
http://rdf.ncbi.nlm.nih.gov/pubchem/patentinventor/MD5_1146f72d8a7347e4556ecbf465158380
http://rdf.ncbi.nlm.nih.gov/pubchem/patentinventor/MD5_fee9b07b361770eaff728d199053cacf
http://rdf.ncbi.nlm.nih.gov/pubchem/patentinventor/MD5_d59ff16759dfac728b08c7bd3932c195
http://rdf.ncbi.nlm.nih.gov/pubchem/patentinventor/MD5_37d9af5eee809507c978b283d69874d3
http://rdf.ncbi.nlm.nih.gov/pubchem/patentinventor/MD5_e4872c7e91a0fc75acb3f421278aba0e
http://rdf.ncbi.nlm.nih.gov/pubchem/patentinventor/MD5_2bda0d0bdd6239e14b1136a3ab2d7ad6 |
| publicationDate |
2020-11-03-04:00^^<http://www.w3.org/2001/XMLSchema#date> |
| publicationNumber |
CN-111882044-A |
| titleOfInvention |
A eutectic prediction method and deep learning framework based on graph neural network |
| abstract |
The invention belongs to the technical field of eutectic formation prediction, and discloses a eutectic prediction method and a deep learning framework based on a graph neural network, including: eutectic sample collection; data processing; data set division; The graph neural network framework CCGNet is used for co-crystal screening, and a prediction model of co-crystal is constructed under the CCGNet framework for co-crystal screening. The prediction performance of the model established by the deep learning framework CCGNet constructed by the present invention greatly surpasses the traditional machine learning model and the classical graph neural network model, and provides a high-throughput and high-accuracy solution for eutectic screening. The methodology of eutectic engineering has been developed, which is an important step towards realizing data-driven eutectic engineering design. The invention also collects a large amount of reliable cocrystal data, which provides strong data support for future cocrystal screening work based on machine learning. |
| isCitedBy |
http://rdf.ncbi.nlm.nih.gov/pubchem/patent/CN-114818948-B
http://rdf.ncbi.nlm.nih.gov/pubchem/patent/CN-114462336-A
http://rdf.ncbi.nlm.nih.gov/pubchem/patent/CN-113327652-A
http://rdf.ncbi.nlm.nih.gov/pubchem/patent/CN-113327652-B
http://rdf.ncbi.nlm.nih.gov/pubchem/patent/CN-114818948-A
http://rdf.ncbi.nlm.nih.gov/pubchem/patent/CN-115762658-A
http://rdf.ncbi.nlm.nih.gov/pubchem/patent/CN-112435720-A
http://rdf.ncbi.nlm.nih.gov/pubchem/patent/CN-113053457-A |
| priorityDate |
2020-08-05-04:00^^<http://www.w3.org/2001/XMLSchema#date> |
| type |
http://data.epo.org/linked-data/def/patent/Publication |