Prediction of biological targets for compounds using multiple-category Bayesian models trained on chemogenomics databases.
about
Towards a unifying, systems biology understanding of large-scale cellular death and destruction caused by poorly liganded iron: Parkinson's, Huntington's, Alzheimer's, prions, bactericides, chemical toxicology and others as examplesComputational approaches in target identification and drug discoverySystems Pharmacology in Small Molecular Drug DiscoveryLarge scale meta-analysis of fragment-based screening campaigns: privileged fragments and complementary technologiesQuantitative assessment of the expanding complementarity between public and commercial databases of bioactive compoundsProteochemometric modelling coupled to in silico target prediction: an integrated approach for the simultaneous prediction of polypharmacology and binding affinity/potency of small moleculesDRABAL: novel method to mine large high-throughput screening assays using Bayesian active learningNew target prediction and visualization tools incorporating open source molecular fingerprints for TB Mobile 2.0A multi-label approach to target prediction taking ligand promiscuity into accountAccurate and efficient target prediction using a potency-sensitive influence-relevance voterVerifying the fully “Laplacianised” posterior Naïve Bayesian approach and moreTarget prediction utilising negative bioactivity data covering large chemical spaceChallenges predicting ligand-receptor interactions of promiscuous proteins: the nuclear receptor PXRNetwork-based relating pharmacological and genomic spaces for drug target identificationBinding of protein kinase inhibitors to synapsin I inferred from pair-wise binding site similarity measurementsTranslating clinical findings into knowledge in drug safety evaluation--drug induced liver injury prediction system (DILIps)Systematic drug repositioning based on clinical side-effectsAssessing drug target association using semantic linked dataTarget prediction for an open access set of compounds active against Mycobacterium tuberculosisMycobacterial dihydrofolate reductase inhibitors identified using chemogenomic methods and in vitro validationApplication of RNAi to Genomic Drug Target Validation in SchistosomesToxEvaluator: an integrated computational platform to aid the interpretation of toxicology study-related findingsFinding the targets of a drug by integration of gene expression data with a protein interaction network.The polypharmacology browser: a web-based multi-fingerprint target prediction tool using ChEMBL bioactivity dataTarget fishing for chemical compounds using target-ligand activity data and ranking based methods.Recent trends and observations in the design of high-quality screening collections.Exploiting PubChem for Virtual Screening.Identifying compound-target associations by combining bioactivity profile similarity search and public databases mining.Mining the ChEMBL database: an efficient chemoinformatics workflow for assembling an ion channel-focused screening libraryComputational methods for early predictive safety assessment from biological and chemical data.Computational chemical biology: identification of small molecular probes that discriminate between members of target protein families.Drug repositioning using in silico compound profiling.Curation and analysis of multitargeting agents for polypharmacological modeling.Identifying mechanism-of-action targets for drugs and probes.Polypharmacology: drug discovery for the future.Target identification and mechanism of action in chemical biology and drug discovery.Drugs, non-drugs, and disease category specificity: organ effects by ligand pharmacology.Diversity selection of compounds based on 'protein affinity fingerprints' improves sampling of bioactive chemical space.Connecting Small Molecules with Similar Assay Performance Profiles Leads to New Biological Hypotheses.DanQi Pill protects against heart failure through the arachidonic acid metabolism pathway by attenuating different cyclooxygenases and leukotrienes B4.
P2860
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P2860
Prediction of biological targets for compounds using multiple-category Bayesian models trained on chemogenomics databases.
description
2006 nî lūn-bûn
@nan
2006年の論文
@ja
2006年学术文章
@wuu
2006年学术文章
@zh
2006年学术文章
@zh-cn
2006年学术文章
@zh-hans
2006年学术文章
@zh-my
2006年学术文章
@zh-sg
2006年學術文章
@yue
2006年學術文章
@zh-hant
name
Prediction of biological targe ...... ed on chemogenomics databases.
@en
Prediction of biological targe ...... ed on chemogenomics databases.
@nl
type
label
Prediction of biological targe ...... ed on chemogenomics databases.
@en
Prediction of biological targe ...... ed on chemogenomics databases.
@nl
prefLabel
Prediction of biological targe ...... ed on chemogenomics databases.
@en
Prediction of biological targe ...... ed on chemogenomics databases.
@nl
P2093
P356
P1476
Prediction of biological targe ...... ed on chemogenomics databases.
@en
P2093
P304
P356
10.1021/CI060003G
P577
2006-05-01T00:00:00Z