| scholarly article | Q13442814 |
| review article | Q7318358 |
| P356 | DOI | 10.2174/13816128113199990470 |
| P953 | full work available at URL | http://eurekaselect.com/article/download/119680 |
| https://europepmc.org/articles/PMC3894695 | ||
| https://europepmc.org/articles/pmc3894695?pdf=render | ||
| https://europepmc.org/articles/PMC3894695?pdf=render | ||
| P932 | PMC publication ID | 3894695 |
| P698 | PubMed publication ID | 23530504 |
| P5875 | ResearchGate publication ID | 236082143 |
| P50 | author | Jing Tang | Q46524197 |
| Tero Aittokallio | Q41044826 | ||
| P2860 | cites work | Therapeutic target database update 2012: a resource for facilitating target-oriented drug discovery | Q35631111 |
| Systematic exploration of synergistic drug pairs | Q35682094 | ||
| Using genome-wide transcriptional profiling to elucidate small-molecule mechanism | Q36035006 | ||
| Large-scale prediction and testing of drug activity on side-effect targets | Q36059863 | ||
| Dynamic Networks in Systems Medicine | Q36244362 | ||
| Ligand-dependent responses of the ErbB signaling network: experimental and modeling analyses | Q36254464 | ||
| Graph-based methods for analysing networks in cell biology | Q36552759 | ||
| Strategies for optimizing combinations of molecularly targeted anticancer agents | Q36554418 | ||
| Applying computational modeling to drug discovery and development | Q36578871 | ||
| Methods for the prediction of protein-ligand binding sites for structure-based drug design and virtual ligand screening | Q36638820 | ||
| A model-based approach for assessing in vivo combination therapy interactions | Q36665323 | ||
| Multi-target therapeutics: when the whole is greater than the sum of the parts | Q36697412 | ||
| In silico pharmacology for drug discovery: methods for virtual ligand screening and profiling | Q36840592 | ||
| Molecular drug targets and structure based drug design: A holistic approach | Q36863549 | ||
| Models from experiments: combinatorial drug perturbations of cancer cells | Q36930517 | ||
| Target deconvolution strategies in drug discovery | Q36961595 | ||
| Network medicine | Q37087536 | ||
| Proteomic methods for drug target discovery | Q37087549 | ||
| Computational prediction of human metabolic pathways from the complete human genome | Q21092879 | ||
| BiGG: a Biochemical Genetic and Genomic knowledgebase of large scale metabolic reconstructions | Q21284353 | ||
| MPact: the MIPS protein interaction resource on yeast | Q24538008 | ||
| ChemProt: a disease chemical biology database | Q24608769 | ||
| NCBI GEO: archive for functional genomics data sets--10 years on | Q24610731 | ||
| DrugBank: a knowledgebase for drugs, drug actions and drug targets | Q24650300 | ||
| BindingDB: a web-accessible database of experimentally determined protein-ligand binding affinities | Q24675698 | ||
| Global reconstruction of the human metabolic network based on genomic and bibliomic data | Q24677060 | ||
| Systems Pharmacology: Network Analysis to Identify Multiscale Mechanisms of Drug Action | Q26824166 | ||
| Structure-based virtual screening for drug discovery: a problem-centric review | Q27022502 | ||
| Mass spectrometry-based proteomics in preclinical drug discovery | Q27023935 | ||
| Chem2Bio2RDF: a semantic framework for linking and data mining chemogenomic and systems chemical biology data | Q27136634 | ||
| SuperTarget and Matador: resources for exploring drug-target relationships | Q27136964 | ||
| Network biology: understanding the cell's functional organization | Q27861027 | ||
| Interpretable correlation descriptors for quantitativestructure-activity relationships | Q27867880 | ||
| The Database of Interacting Proteins: 2004 update | Q28234998 | ||
| Efficient discovery of anti-inflammatory small-molecule combinations using evolutionary computing | Q28251171 | ||
| Simulation and prediction of in vivo drug metabolism in human populations from in vitro data | Q28286115 | ||
| Network pharmacology: the next paradigm in drug discovery | Q28297997 | ||
| The BioGRID Interaction Database: 2011 update | Q28298160 | ||
| ChEMBL: a large-scale bioactivity database for drug discovery | Q28315179 | ||
| Analysis of compound synergy in high-throughput cellular screens by population-based lifetime modeling | Q28472663 | ||
| Predicting drug-target interaction networks based on functional groups and biological features | Q28473160 | ||
| Search algorithms as a framework for the optimization of drug combinations | Q28474246 | ||
| Drug discovery using chemical systems biology: identification of the protein-ligand binding network to explain the side effects of CETP inhibitors | Q28475474 | ||
| Drug Off-Target Effects Predicted Using Structural Analysis in the Context of a Metabolic Network Model | Q28475693 | ||
| Prediction of drug combinations by integrating molecular and pharmacological data | Q28478623 | ||
| Reconstruction of genome-scale active metabolic networks for 69 human cell types and 16 cancer types using INIT | Q28483779 | ||
| The Connectivity Map: a new tool for biomedical research | Q28530408 | ||
| The MetaCyc Database | Q34010702 | ||
| Identifying Cellular Targets of Small-Molecule Probes and Drugs with Biochemical Enrichment and SILAC | Q34069417 | ||
| Discovery of drug mode of action and drug repositioning from transcriptional responses | Q34093341 | ||
| Identification of optimal drug combinations targeting cellular networks: integrating phospho-proteomics and computational network analysis | Q34101667 | ||
| A decade of systems biology | Q34124493 | ||
| Role of systems pharmacology in understanding drug adverse events | Q34134791 | ||
| Principles and strategies for developing network models in cancer | Q34171479 | ||
| Diagnosing the decline in pharmaceutical R&D efficiency | Q34257771 | ||
| Sequential application of anticancer drugs enhances cell death by rewiring apoptotic signaling networks. | Q34274373 | ||
| Identifying mechanism-of-action targets for drugs and probes. | Q34282670 | ||
| Combinatorial drug therapy for cancer in the post-genomic era. | Q34287101 | ||
| Inferring cellular networks using probabilistic graphical models | Q34295031 | ||
| The MIPS mammalian protein-protein interaction database | Q34365947 | ||
| Predicting and characterizing selective multiple drug treatments for metabolic diseases and cancer. | Q34397561 | ||
| The Biomolecular Interaction Network Database in PSI-MI 2.5. | Q34495056 | ||
| Reconstruction of genome-scale metabolic models for 126 human tissues using mCADRE | Q34509501 | ||
| A probabilistic coevolutionary biclustering algorithm for discovering coherent patterns in gene expression dataset | Q34530287 | ||
| Systems approaches to polypharmacology and drug discovery | Q34746314 | ||
| Structure-based systems biology for analyzing off-target binding | Q34761978 | ||
| Closed-loop control of cellular functions using combinatory drugs guided by a stochastic search algorithm | Q34763799 | ||
| Network analyses in systems pharmacology | Q34995155 | ||
| Do cancer proteins really interact strongly in the human protein-protein interaction network? | Q35034277 | ||
| Systematic discovery of multicomponent therapeutics | Q35147474 | ||
| Predicting selective drug targets in cancer through metabolic networks. | Q35176392 | ||
| Quantitative models of signal transduction networks: How detailed should they be? | Q35278503 | ||
| PINA v2.0: mining interactome modules. | Q35630696 | ||
| Co-evolution of proteins with their interaction partners | Q41742351 | ||
| Chemical combination effects predict connectivity in biological systems | Q41851112 | ||
| Querying quantitative logic models (Q2LM) to study intracellular signaling networks and cell‐cytokine interactions | Q41887989 | ||
| A Comprehensive Statistical Model for Cell Signaling | Q41891471 | ||
| INDI: a computational framework for inferring drug interactions and their associated recommendations | Q42280497 | ||
| High-order combination effects and biological robustness | Q42554339 | ||
| Flux balance analysis of biological systems: applications and challenges. | Q43740877 | ||
| Integrated network analysis platform for protein-protein interactions | Q43961965 | ||
| A network view of disease and compound screening | Q44111868 | ||
| Enhancing the Accuracy of Chemogenomic Models with a Three-Dimensional Binding Site Kernel | Q44737665 | ||
| PRIDE and "Database on Demand" as valuable tools for computational proteomics | Q46241689 | ||
| Predicting drug-target interactions from chemical and genomic kernels using Bayesian matrix factorization. | Q46766552 | ||
| Comparing bioassay response and similarity ensemble approaches to probing protein pharmacology | Q48112341 | ||
| Predicting adverse drug reaction profiles by integrating protein interaction networks with drug structures | Q50734440 | ||
| A robustness-based approach to systems-oriented drug design | Q51062307 | ||
| An Algorithmic Framework for Predicting Side Effects of Drugs | Q51594993 | ||
| Biochemical network-based drug-target prediction | Q51688924 | ||
| Detecting drug targets with minimum side effects in metabolic networks | Q51772558 | ||
| Structure-based maximal affinity model predicts small-molecule druggability. | Q51925540 | ||
| Interrogating the kinome | Q54551329 | ||
| Navigating cancer network attractors for tumor-specific therapy | Q56029373 | ||
| Genetic Interactions in Cancer Progression and Treatment | Q63383811 | ||
| Computer model predicts side effects | Q74441210 | ||
| Mechanisms of drug combinations: interaction and network perspectives | Q83265745 | ||
| The relationship between rational drug design and drug side effects | Q84992161 | ||
| A quantitative framework and strategies for management and evaluation of metabolic drug-drug interactions in oncology drug development: new molecular entities as object drugs | Q85159440 | ||
| Network-based drug discovery by integrating systems biology and computational technologies | Q28677268 | ||
| The IntAct molecular interaction database in 2010 | Q28749230 | ||
| PubChem: a public information system for analyzing bioactivities of small molecules | Q28842768 | ||
| Novel Computational Approaches to Polypharmacology as a Means to Define Responses to Individual Drugs | Q28969532 | ||
| A side effect resource to capture phenotypic effects of drugs | Q29387552 | ||
| KEGG for integration and interpretation of large-scale molecular data sets | Q29547277 | ||
| Human Protein Reference Database--2009 update | Q29547392 | ||
| Network medicine: a network-based approach to human disease | Q29547462 | ||
| Drug-target network | Q29614447 | ||
| Drug target identification using side-effect similarity | Q29615103 | ||
| Interactome networks and human disease | Q29615773 | ||
| Relating protein pharmacology by ligand chemistry | Q29617492 | ||
| MINT, the molecular interaction database: 2009 update | Q29617595 | ||
| Towards a knowledge-based Human Protein Atlas | Q29617987 | ||
| Intra-tumour heterogeneity: a looking glass for cancer? | Q29620149 | ||
| STITCH 3: zooming in on protein-chemical interactions | Q30002340 | ||
| From in silico target prediction to multi-target drug design: current databases, methods and applications | Q30403222 | ||
| Systematic identification of synergistic drug pairs targeting HIV | Q30422333 | ||
| Empirical statistical estimates for sequence similarity searches. | Q30471883 | ||
| Calculating similarities between biological activities in the MDL Drug Data Report database | Q30914165 | ||
| Compartmentalization of the Edinburgh Human Metabolic Network | Q30986319 | ||
| Critical assessment of human metabolic pathway databases: a stepping stone for future integration | Q31039028 | ||
| A strategy for extracting and analyzing large-scale quantitative epistatic interaction data | Q31049488 | ||
| Discovery of agents that eradicate leukemia stem cells using an in silico screen of public gene expression data | Q31147913 | ||
| Prediction of potential drug targets based on simple sequence properties | Q33299753 | ||
| The synergy between combinatorial chemistry and high-throughput screening. | Q33329840 | ||
| Modeling ERBB receptor-regulated G1/S transition to find novel targets for de novo trastuzumab resistance | Q33396832 | ||
| Optimal drug combinations and minimal hitting sets | Q33491286 | ||
| The logic of EGFR/ErbB signaling: theoretical properties and analysis of high-throughput data | Q33491534 | ||
| A formal model for analyzing drug combination effects and its application in TNF-alpha-induced NFkappaB pathway | Q33563358 | ||
| Network systems biology for drug discovery | Q33592795 | ||
| Identifying the cellular targets of bioactive small molecules with activity-based probes. | Q33632526 | ||
| Genome-wide scoring of positive and negative epistasis through decomposition of quantitative genetic interaction fitness matrices | Q33640073 | ||
| Identification of small molecules enhancing autophagic function from drug network analysis | Q33712703 | ||
| Predicting enzyme targets for cancer drugs by profiling human Metabolic reactions in NCI-60 cell lines | Q33713220 | ||
| Targeting the cancer kinome through polypharmacology | Q33893019 | ||
| Network target for screening synergistic drug combinations with application to traditional Chinese medicine | Q33937467 | ||
| Prediction of protein function from protein sequence and structure | Q33976748 | ||
| Evolutionary dynamics of carcinogenesis and why targeted therapy does not work. | Q34005286 | ||
| Systems pharmacology and genome medicine: a future perspective | Q37118302 | ||
| Human metabolic network reconstruction and its impact on drug discovery and development | Q37158701 | ||
| Prediction of drug-target interaction networks from the integration of chemical and genomic spaces. | Q37283297 | ||
| Pharmacogenomics in clinical practice and drug development | Q37284234 | ||
| Quantifying the relationships among drug classes | Q37294973 | ||
| A network medicine approach to human disease | Q37408792 | ||
| Molecular networks as sensors and drivers of common human diseases | Q37594580 | ||
| Unveiling the role of network and systems biology in drug discovery | Q37684484 | ||
| Diseases as network perturbations | Q37779993 | ||
| Probing the links between in vitro potency, ADMET and physicochemical parameters. | Q37848051 | ||
| The application of next-generation sequencing technologies to drug discovery and development | Q37858174 | ||
| Drug metabolite profiling and identification by high-resolution mass spectrometry. | Q37883742 | ||
| Chemical Genetics–Based Target Identification in Drug Discovery | Q37912160 | ||
| Integrative Genomics Strategies to Elucidate the Complexity of Drug Response | Q37961088 | ||
| Metabolic network modeling and simulation for drug targeting and discovery | Q37962486 | ||
| Chemical-genomic profiling: Systematic analysis of the cellular targets of bioactive molecules | Q37977218 | ||
| Structural Biology and Drug Discovery of Difficult Targets: The Limits of Ligandability | Q37979386 | ||
| Network-based analysis of complex diseases | Q37987552 | ||
| Quantitative in vitro to in vivo extrapolation of cell-based toxicity assay results | Q38016204 | ||
| Mathematical and Statistical Modeling in Cancer Systems Biology | Q38023403 | ||
| Mechanisms of acquired resistance to targeted cancer therapies. | Q38034816 | ||
| Relating drug–protein interaction network with drug side effects | Q38495130 | ||
| SuperTarget goes quantitative: update on drug-target interactions | Q38499993 | ||
| Constraint-based functional similarity of metabolic genes: going beyond network topology | Q38515961 | ||
| Kernel methods for predicting protein-protein interactions | Q38520179 | ||
| Comparing signaling networks between normal and transformed hepatocytes using discrete logical models | Q38925118 | ||
| Neighbor communities in drug combination networks characterize synergistic effect | Q39270928 | ||
| Gene expression signatures associated with the in vitro resistance to two tyrosine kinase inhibitors, nilotinib and imatinib | Q39308709 | ||
| canSAR: an integrated cancer public translational research and drug discovery resource | Q39455504 | ||
| Synergistic drug combinations tend to improve therapeutically relevant selectivity | Q39828335 | ||
| Rational design of cancer-drug combinations | Q40106730 | ||
| Computational modelling of ErbB family phosphorylation dynamics in response to transforming growth factor alpha and heregulin indicates spatial compartmentation of phosphatase activity | Q40230231 | ||
| The druggable genome: an update | Q40362716 | ||
| Analysis of drug-induced effect patterns to link structure and side effects of medicines | Q40363457 | ||
| PROMISCUOUS: a database for network-based drug-repositioning | Q41578922 | ||
| P275 | copyright license | Creative Commons Attribution-NonCommercial 3.0 Unported | Q18810331 |
| P433 | issue | 1 | |
| P407 | language of work or name | English | Q1860 |
| P921 | main subject | antineoplastic | Q2853144 |
| pharmacology | Q128406 | ||
| P304 | page(s) | 23-36 | |
| 20-36 | |||
| P577 | publication date | 2014-01-01 | |
| P1433 | published in | Current Pharmaceutical Design | Q5195068 |
| P1476 | title | Network pharmacology strategies toward multi-target anticancer therapies: from computational models to experimental design principles | |
| P478 | volume | 20 |
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| Q64329761 | An Integrated Study on the Antitumor Effect and Mechanism of Triphala Against Gynecological Cancers Based on Network Pharmacological Prediction and In Vitro Experimental Validation |
| Q42697768 | Data driven polypharmacological drug design for lung cancer: analyses for targeting ALK, MET, and EGFR. |
| Q26776294 | Deep sea as a source of novel-anticancer drugs: update on discovery and preclinical/clinical evaluation in a systems medicine perspective |
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| Q91844985 | DrugComb: an integrative cancer drug combination data portal |
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| Q90799071 | Experimental Characterization of the Binding Affinities between Proapoptotic BH3 Peptides and Antiapoptotic Bcl-2 Proteins |
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| Q38597446 | Identification of drug candidates and repurposing opportunities through compound-target interaction networks |
| Q64889734 | Identifying the Antiproliferative Effect of Astragalus Polysaccharides on Breast Cancer: Coupling Network Pharmacology With Targetable Screening From the Cancer Genome Atlas. |
| Q52323287 | In silico cancer research towards 3R. |
| Q38668684 | Informatics Approaches for Predicting, Understanding, and Testing Cancer Drug Combinations. |
| Q89941176 | Integrated network pharmacology and targeted metabolomics to reveal the mechanism of nephrotoxicity of triptolide |
| Q36310219 | Large-Scale Computational Screening Identifies First in Class Multitarget Inhibitor of EGFR Kinase and BRD4. |
| Q47561424 | Methods for High-throughput Drug Combination Screening and Synergy Scoring. |
| Q38888255 | Multi-scale Modeling in Clinical Oncology: Opportunities and Barriers to Success. |
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| Q37660769 | Network Pharmacology Studies on the Bioactive Compounds and Action Mechanisms of Natural Products for the Treatment of Diabetes Mellitus: A Review. |
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