Analysis of multiple compound-protein interactions reveals novel bioactive molecules - Wikidata - awgldk - TriplyDB

Analysis of multiple compound-protein interactions reveals novel bioactive molecules

Analysis of multiple compound-protein interactions reveals novel bioactive molecules

http://www.wikidata.org/entity/Q34979708

about

Structure and dynamics of molecular networks: a novel paradigm of drug discovery: a comprehensive review Benchmark of four popular virtual screening programs: construction of the active/decoy dataset remains a major determinant of measured performance Prediction of chemical-protein interactions network with weighted network-based inference method A systematic prediction of multiple drug-target interactions from chemical, genomic, and pharmacological data Ligand-target prediction by structural network biology using nAnnoLyze A Multilayer Network Approach for Guiding Drug Repositioning in Neglected Diseases DrugE-Rank: improving drug-target interaction prediction of new candidate drugs or targets by ensemble learning to rank Structural bioinformatics of the interactome.Drug-target interaction prediction by integrating chemical, genomic, functional and pharmacological data.Scalable prediction of compound-protein interactions using minwise hashing Improving compound-protein interaction prediction by building up highly credible negative samples.The chemical interactome space between the human host and the genetically defined gut metabotypes.Predicting drug-target interactions using restricted Boltzmann machines.Development of conformation independent computational models for the early recognition of breast cancer resistance protein substrates.Quantitative Structure-activity Relationship (QSAR) Models for Docking Score Correction.Similarity-based machine learning methods for predicting drug-target interactions: a brief review.On protocols and measures for the validation of supervised methods for the inference of biological networks Computational methods for analysis and inference of kinase/inhibitor relationships.Constructing a Foundational Platform Driven by Japan's K Supercomputer for Next-Generation Drug Design.Using quantitative systems pharmacology for novel drug discovery.Network-based Approaches in Pharmacology.Active learning for computational chemogenomics.CompoundProtein 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.Physicochemical characteristics of structurally determined metabolite-protein and drug-protein binding events with respect to binding specificity.Best of both worlds: on the complementarity of ligand-based and structure-based virtual screening Global optimization-based inference of chemogenomic features from drug-target interactions.Identification of chemogenomic features from drug-target interaction networks using interpretable classifiers.Flaws in evaluation schemes for pair-input computational predictions.SimBoost: a read-across approach for predicting drug-target binding affinities using gradient boosting machines.Predicting drug targets based on protein domains.Novel inhibitor candidates of TRPV2 prevent damage of dystrophic myocytes and ameliorate against dilated cardiomyopathy in a hamster model.Polypharmacology modelling using proteochemometrics (PCM): recent methodological developments, applications to target families, and future prospects Mapping the 3D structures of small molecule binding sites.

P2860

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P2860

Analysis of multiple compound-protein interactions reveals novel bioactive molecules

http://www.wikidata.org/entity/Q34979708

description

2011 nî lūn-bûn

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2011 թուականի Մարտին հրատարակուած գիտական յօդուած

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2011 թվականի մարտին հրատարակված գիտական հոդված

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2011年の論文

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2011年論文

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2011年論文

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2011年論文

@zh-hk

2011年論文

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2011年論文

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2011年论文

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name

Analysis of multiple compound-protein interactions reveals novel bioactive molecules

@ast

Analysis of multiple compound-protein interactions reveals novel bioactive molecules

@en

Analysis of multiple compound-protein interactions reveals novel bioactive molecules

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type

label

Analysis of multiple compound-protein interactions reveals novel bioactive molecules

@ast

Analysis of multiple compound-protein interactions reveals novel bioactive molecules

@en

Analysis of multiple compound-protein interactions reveals novel bioactive molecules

@nl

prefLabel

Analysis of multiple compound-protein interactions reveals novel bioactive molecules

@ast

Analysis of multiple compound-protein interactions reveals novel bioactive molecules

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Analysis of multiple compound-protein interactions reveals novel bioactive molecules

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Molecular Systems Biology

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Analysis of multiple compound-protein interactions reveals novel bioactive molecules

@en

P2093

Gozoh Tsujimoto

http://www.w3.org/2001/XMLSchema#string

Hiroaki Yabuuchi

http://www.w3.org/2001/XMLSchema#string

Hiromu Takematsu

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Satoshi Niijima

http://www.w3.org/2001/XMLSchema#string

Takafumi Hara

http://www.w3.org/2001/XMLSchema#string

Takatsugu Hirokawa

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Teppei Ogawa

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Tomomi Ida

http://www.w3.org/2001/XMLSchema#string

Yasushi Okuno

http://www.w3.org/2001/XMLSchema#string

Yohsuke Minowa

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P2860

DrugBank: a comprehensive resource for in silico drug discovery and exploration

The protein kinase complement of the human genome

Predicting new molecular targets for known drugs

High-resolution crystal structure of an engineered human beta2-adrenergic G protein-coupled receptor

GLIDA: GPCR-ligand database for chemical genomic drug discovery

Crystal structure of the human beta2 adrenergic G-protein-coupled receptor

Targeted polypharmacology: discovery of dual inhibitors of tyrosine and phosphoinositide kinases

Development and validation of a genetic algorithm for flexible docking

Glide: a new approach for rapid, accurate docking and scoring. 1. Method and assessment of docking accuracy

Virtual screening strategies in drug discovery

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10.1038/MSB.2011.5

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7

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2011-03-01T00:00:00Z

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50250492

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21364574

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3094066

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