Jiang et al., 2021 - Google Patents
Interactiongraphnet: A novel and efficient deep graph representation learning framework for accurate protein–ligand interaction predictionsJiang et al., 2021
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- 5838776965385238772
- Author
- Jiang D
- Hsieh C
- Wu Z
- Kang Y
- Wang J
- Wang E
- Liao B
- Shen C
- Xu L
- Wu J
- Cao D
- Hou T
- Publication year
- Publication venue
- Journal of medicinal chemistry
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Snippet
Accurate quantification of protein–ligand interactions remains a key challenge to structure- based drug design. However, traditional machine learning (ML)-based methods based on handcrafted descriptors, one-dimensional protein sequences, and/or two-dimensional graph …
- 239000003446 ligand 0 title abstract description 281
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- G06F19/10—Bioinformatics, i.e. methods or systems for genetic or protein-related data processing in computational molecular biology
- G06F19/16—Bioinformatics, i.e. methods or systems for genetic or protein-related data processing in computational molecular biology for molecular structure, e.g. structure alignment, structural or functional relations, protein folding, domain topologies, drug targeting using structure data, involving two-dimensional or three-dimensional structures
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- G06F19/18—Bioinformatics, i.e. methods or systems for genetic or protein-related data processing in computational molecular biology for functional genomics or proteomics, e.g. genotype-phenotype associations, linkage disequilibrium, population genetics, binding site identification, mutagenesis, genotyping or genome annotation, protein-protein interactions or protein-nucleic acid interactions
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- G06F19/706—Chemoinformatics, i.e. data processing methods or systems for the retrieval, analysis, visualisation, or storage of physicochemical or structural data of chemical compounds for drug design with the emphasis on a therapeutic agent, e.g. ligand-biological target interactions, pharmacophore generation
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