http://rdf.ncbi.nlm.nih.gov/pubchem/patent/JP-2022516344-A
Outgoing Links
| Predicate | Object |
|---|---|
| classificationCPCInventive | http://rdf.ncbi.nlm.nih.gov/pubchem/patentcpc/G16B15-30 http://rdf.ncbi.nlm.nih.gov/pubchem/patentcpc/G16B40-30 http://rdf.ncbi.nlm.nih.gov/pubchem/patentcpc/G16B35-10 http://rdf.ncbi.nlm.nih.gov/pubchem/patentcpc/G16B40-20 |
| classificationIPCInventive | http://rdf.ncbi.nlm.nih.gov/pubchem/patentipc/G16B40-20 http://rdf.ncbi.nlm.nih.gov/pubchem/patentipc/G16B15-30 |
| filingDate | 2020-01-02-04:00^^<http://www.w3.org/2001/XMLSchema#date> |
| publicationDate | 2022-02-25-04:00^^<http://www.w3.org/2001/XMLSchema#date> |
| publicationNumber | JP-2022516344-A |
| titleOfInvention | Methods and systems for predicting drug binding using synthetic data |
| abstract | Methods for predicting drug-target binding using synthetically enhanced data include generating multiple ghost ligands for multiple proteins in a protein structure database and using multiple ghost ligands. It can be used to generate a large number of drug-target interaction (DTI) features for proteins and ligands in a DTI database, and to generate a machine learning model using a large number of DTI features, and machine learning. Predicting the potential interactions for a combination of query proteins and query ligands using a model includes. [Selection diagram] FIG. 1A |
| priorityDate | 2019-01-04-04:00^^<http://www.w3.org/2001/XMLSchema#date> |
| type | http://data.epo.org/linked-data/def/patent/Publication |
Incoming Links
Total number of triples: 45.