Graph kernels for molecular structure-activity relationship analysis with support vector machines.
about
Comparison and enumeration of chemical graphsA constructive approach for discovering new drug leads: Using a kernel methodology for the inverse-QSAR problemMachine learning for in silico virtual screening and chemical genomics: new strategiesDrug-target interaction prediction by integrating chemical, genomic, functional and pharmacological data.Virtual screening of GPCRs: an in silico chemogenomics approach.Reaction graph kernels predict EC numbers of unknown enzymatic reactions in plant secondary metabolismNovel topological descriptors for analyzing biological networks.Drug target prediction using adverse event report systems: a pharmacogenomic approach.Support vector machines for drug discovery.Drug-target interaction prediction via chemogenomic space: learning-based methods.Graph Kernels for Molecular Similarity.CompoundProtein Interaction Prediction Within Chemogenomics: Theoretical Concepts, Practical Usage, and Future Directions.Benchmarking a Wide Range of Chemical Descriptors for Drug-Target Interaction Prediction Using a Chemogenomic Approach.QSAR modeling to design selective histone deacetylase 8 (HDAC8) inhibitors.The Development of Novel Chemical Fragment-Based Descriptors Using Frequent Common Subgraph Mining Approach and Their Application in QSAR Modeling.Estimation of the applicability domain of kernel-based machine learning models for virtual screeningA modified uncorrelated linear discriminant analysis model coupled with recursive feature elimination for the prediction of bioactivity.Efficient enumeration of monocyclic chemical graphs with given path frequenciesRchemcpp: a web service for structural analoging in ChEMBL, Drugbank and the Connectivity Map.A lazy learning-based QSAR classification study for screening potential histone deacetylase 8 (HDAC8) inhibitors.Enumerating tree-like chemical graphs with given upper and lower bounds on path frequencies.DINIES: drug-target interaction network inference engine based on supervised analysis.Protein-ligand interaction prediction: an improved chemogenomics approach.Enhancing Team Composition in Professional Networks: Problem Definitions and Fast Solutions.DeepTox: Toxicity Prediction using Deep LearningOne-class approach: models for virtual screening of non-nucleoside HIV-1 reverse transcriptase inhibitors based on the concept of continuous molecular fields
P2860
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P2860
Graph kernels for molecular structure-activity relationship analysis with support vector machines.
description
2005 nî lūn-bûn
@nan
2005年の論文
@ja
2005年学术文章
@wuu
2005年学术文章
@zh
2005年学术文章
@zh-cn
2005年学术文章
@zh-hans
2005年学术文章
@zh-my
2005年学术文章
@zh-sg
2005年學術文章
@yue
2005年學術文章
@zh-hant
name
Graph kernels for molecular st ...... with support vector machines.
@en
Graph kernels for molecular st ...... with support vector machines.
@nl
type
label
Graph kernels for molecular st ...... with support vector machines.
@en
Graph kernels for molecular st ...... with support vector machines.
@nl
prefLabel
Graph kernels for molecular st ...... with support vector machines.
@en
Graph kernels for molecular st ...... with support vector machines.
@nl
P2093
P356
P1476
Graph kernels for molecular st ...... with support vector machines.
@en
P2093
Jean-Luc Perret
Nobuhisa Ueda
Pierre Mahé
P304
P356
10.1021/CI050039T
P577
2005-07-01T00:00:00Z