Modeling liver-related adverse effects of drugs using knearest neighbor quantitative structure-activity relationship method.
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Pathogenesis of idiosyncratic drug-induced liver injury and clinical perspectives.A graph-based approach to construct target-focused libraries for virtual screeningKey Challenges and Opportunities Associated with the Use of In Vitro Models to Detect Human DILI: Integrated Risk Assessment and Mitigation PlansComputer-aided design of carbon nanotubes with the desired bioactivity and safety profilesFusing dual-event data sets for Mycobacterium tuberculosis machine learning models and their evaluation.Predicting drug-induced hepatotoxicity using QSAR and toxicogenomics approaches.Prediction models for drug-induced hepatotoxicity by using weighted molecular fingerprints.Computational methods for early predictive safety assessment from biological and chemical data.Encompassing receptor flexibility in virtual screening using ensemble docking-based hybrid QSAR: discovery of novel phytochemicals for BACE1 inhibition.What if the number of nanotoxicity data is too small for developing predictive Nano-QSAR models? An alternative read-across based approach for filling data gaps.In silico models for drug-induced liver injury--current status.Toward predictive models for drug-induced liver injury in humans: are we there yet?Ecotoxicological modelling of cosmetics for aquatic organisms: A QSTR approach.The Promise of New Technologies to Reduce, Refine, or Replace Animal Use while Reducing Risks of Drug Induced Liver Injury in Pharmaceutical Development.Identification of Novel Inhibitors of Organic Anion Transporting Polypeptides 1B1 and 1B3 (OATP1B1 and OATP1B3) Using a Consensus Vote of Six Classification Models.In Silico Models for Hepatotoxicity.In silico research to assist the investigation of carboxamide derivatives as potent TRPV1 antagonists.Refinement, Reduction, and Replacement of Animal Toxicity Tests by Computational Methods.In silico Prediction of Drug Induced Liver Toxicity Using Substructure Pattern Recognition Method.Supervised extensions of chemography approaches: case studies of chemical liabilities assessmentCheminformatics-aided pharmacovigilance: application to Stevens-Johnson Syndrome.Adverse drug reactions triggered by the common HLA-B*57:01 variant: a molecular docking study.Predicting Drug-Induced Cholestasis with the Help of Hepatic Transporters-An in Silico Modeling Approach.Mixed learning algorithms and features ensemble in hepatotoxicity prediction.Structural features of falcipain-3 inhibitors: an in silico study.Using random forest and decision tree models for a new vehicle prediction approach in computational toxicologyDetermining the Balance Between Drug Efficacy and Safety by the Network and Biological System Profile of Its Therapeutic Target
P2860
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P2860
Modeling liver-related adverse effects of drugs using knearest neighbor quantitative structure-activity relationship method.
description
2010 nî lūn-bûn
@nan
2010 թուականի Ապրիլին հրատարակուած գիտական յօդուած
@hyw
2010 թվականի ապրիլին հրատարակված գիտական հոդված
@hy
2010年の論文
@ja
2010年学术文章
@wuu
2010年学术文章
@zh-cn
2010年学术文章
@zh-hans
2010年学术文章
@zh-my
2010年学术文章
@zh-sg
2010年學術文章
@yue
name
Modeling liver-related adverse ...... -activity relationship method.
@ast
Modeling liver-related adverse ...... -activity relationship method.
@en
Modeling liver-related adverse ...... -activity relationship method.
@nl
type
label
Modeling liver-related adverse ...... -activity relationship method.
@ast
Modeling liver-related adverse ...... -activity relationship method.
@en
Modeling liver-related adverse ...... -activity relationship method.
@nl
prefLabel
Modeling liver-related adverse ...... -activity relationship method.
@ast
Modeling liver-related adverse ...... -activity relationship method.
@en
Modeling liver-related adverse ...... -activity relationship method.
@nl
P2093
P2860
P356
P1476
Modeling liver-related adverse ...... -activity relationship method.
@en
P2093
Amie D Rodgers
Ivan Rusyn
P2860
P304
P356
10.1021/TX900451R
P577
2010-04-01T00:00:00Z