Kernels for small molecules and the prediction of mutagenicity, toxicity and anti-cancer activity.
about
jCompoundMapper: An open source Java library and command-line tool for chemical fingerprintsAccurate and efficient target prediction using a potency-sensitive influence-relevance voterA constructive approach for discovering new drug leads: Using a kernel methodology for the inverse-QSAR problemLarge scale study of multiple-molecule queriesPredicting activities without computing descriptors: graph machines for QSARMachine learning prediction of cancer cell sensitivity to drugs based on genomic and chemical propertiesSecurely measuring the overlap between private datasets with cryptosetsMachine learning assisted design of highly active peptides for drug discoveryMachine learning for in silico virtual screening and chemical genomics: new strategiesHeterogeneous biomedical database integration using a hybrid strategy: a p53 cancer research database.Novel paradigms for drug discovery: computational multitarget screeningExtending P450 site-of-metabolism models with region-resolution data.Statistical prediction of protein chemical interactions based on chemical structure and mass spectrometry data.Structural similarity assessment for drug sensitivity prediction in cancer.A CROC stronger than ROC: measuring, visualizing and optimizing early retrieval.An efficient algorithm for de novo predictions of biochemical pathways between chemical compoundsPrediction of drug indications based on chemical interactions and chemical similarities.Discriminating precursors of common fragments for large-scale metabolite profiling by triple quadrupole mass spectrometryStructure-activity models of oral clearance, cytotoxicity, and LD50: a screen for promising anticancer compounds.Influence relevance voting: an accurate and interpretable virtual high throughput screening method.Graph Kernels for Molecular Similarity.Probabilistic Substructure Mining From Small-Molecule Screens.Estimation of the applicability domain of kernel-based machine learning models for virtual screeningLossless compression of chemical fingerprints using integer entropy codes improves storage and retrieval.Bounds and algorithms for fast exact searches of chemical fingerprints in linear and sublinear time.Boltzmann-Enhanced Flexible Atom-Pair Kernel with Dynamic Dimension Reduction.Genome scale enzyme-metabolite and drug-target interaction predictions using the signature molecular descriptor.In silico toxicity prediction by support vector machine and SMILES representation-based string kernel.Mining small-molecule screens to repurpose drugs.Improving prediction of heterodimeric protein complexes using combination with pairwise kernel.The Calculation of Molecular Structural Similarity: Principles and Practice.Prediction of chemical toxicity with local support vector regression and activity-specific kernelsEffective feature construction by maximum common subgraph samplingAccurate force field for molybdenum by machine learning large materials data
P2860
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P2860
Kernels for small molecules and the prediction of mutagenicity, toxicity and anti-cancer activity.
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-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
2005年學術文章
@zh-hant
name
Kernels for small molecules an ...... city and anti-cancer activity.
@en
Kernels for small molecules an ...... city and anti-cancer activity.
@nl
type
label
Kernels for small molecules an ...... city and anti-cancer activity.
@en
Kernels for small molecules an ...... city and anti-cancer activity.
@nl
prefLabel
Kernels for small molecules an ...... city and anti-cancer activity.
@en
Kernels for small molecules an ...... city and anti-cancer activity.
@nl
P2093
P356
P1433
P1476
Kernels for small molecules an ...... icity and anti-cancer activity
@en
P2093
Jocelyne Bruand
Jonathan Chen
Liva Ralaivola
Peter Phung
Pierre Baldi
P304
P356
10.1093/BIOINFORMATICS/BTI1055
P407
P478
21 Suppl 1
P577
2005-06-01T00:00:00Z