| scholarly article | Q13442814 |
| P2093 | author name string | Dong-Sheng Cao | |
| Liu-Xia Zhang | |||
| Qing-Song Xu | |||
| Zheng Xiang | |||
| Gui-Shan Tan | |||
| Alex F Chen | |||
| Wen-Bin Zeng | |||
| P2860 | cites work | Genome-Scale Screening of Drug-Target Associations Relevant to Ki Using a Chemogenomics Approach | Q21133651 |
| DrugBank: a comprehensive resource for in silico drug discovery and exploration | Q24188653 | ||
| KEGG: kyoto encyclopedia of genes and genomes | Q24515297 | ||
| KEGG: Kyoto Encyclopedia of Genes and Genomes | Q24548371 | ||
| Data-driven prediction of drug effects and interactions | Q24599300 | ||
| DrugBank 3.0: a comprehensive resource for 'omics' research on drugs | Q24612505 | ||
| BindingDB: a web-accessible database of experimentally determined protein-ligand binding affinities | Q24675698 | ||
| Network biology: understanding the cell's functional organization | Q27861027 | ||
| Chemogenomics: an emerging strategy for rapid target and drug discovery | Q28260664 | ||
| Automated docking of flexible ligands: applications of AutoDock | Q28285420 | ||
| Network pharmacology: the next paradigm in drug discovery | Q28297997 | ||
| Predicting drug-target interaction networks based on functional groups and biological features | Q28473160 | ||
| Prediction of chemical-protein interactions network with weighted network-based inference method | Q28481329 | ||
| A systematic prediction of multiple drug-target interactions from chemical, genomic, and pharmacological data | Q28484011 | ||
| An overview of the PubChem BioAssay resource | Q28749237 | ||
| Drug-target network | Q29614447 | ||
| Drug repositioning: identifying and developing new uses for existing drugs | Q29614779 | ||
| Drug target identification using side-effect similarity | Q29615103 | ||
| Global mapping of pharmacological space | Q29615876 | ||
| Relating protein pharmacology by ligand chemistry | Q29617492 | ||
| Large-scale prediction of drug-target relationships | Q30002408 | ||
| Chemogenomics: structuring the drug discovery process to gene families. | Q30356988 | ||
| Statistical prediction of protein chemical interactions based on chemical structure and mass spectrometry data | Q31113004 | ||
| Virtual screening of GPCRs: an in silico chemogenomics approach | Q33367388 | ||
| Drug-target interaction prediction from chemical, genomic and pharmacological data in an integrated framework | Q33597328 | ||
| Semi-supervised drug-protein interaction prediction from heterogeneous biological spaces | Q33692324 | ||
| Prediction of protein folding class using global description of amino acid sequence | Q33919861 | ||
| Trends in the exploitation of novel drug targets | Q34205159 | ||
| Lead discovery using molecular docking | Q34754631 | ||
| Large-scale prediction and testing of drug activity on side-effect targets | Q36059863 | ||
| Protein-ligand docking: current status and future challenges | Q36544700 | ||
| Chemogenomic approaches to rational drug design | Q36833536 | ||
| Prediction of drug-target interaction networks from the integration of chemical and genomic spaces. | Q37283297 | ||
| Supervised prediction of drug-target interactions using bipartite local models | Q37327753 | ||
| Target–drug interactions: first principles and their application to drug discovery | Q37903454 | ||
| Relating drug–protein interaction network with drug side effects | Q38495130 | ||
| AAindex: amino acid index database | Q39541361 | ||
| Large-scale prediction of adverse drug reactions using chemical, biological, and phenotypic properties of drugs | Q41962127 | ||
| Protein-ligand interaction prediction: an improved chemogenomics approach. | Q42948052 | ||
| Gaussian interaction profile kernels for predicting drug-target interaction | Q45961548 | ||
| In silico classification of human maximum recommended daily dose based on modified random forest and substructure fingerprint | Q45961984 | ||
| Using entropy of drug and protein graphs to predict FDA drug-target network: theoretic-experimental study of MAO inhibitors and hemoglobin peptides from Fasciola hepatica. | Q45962192 | ||
| 2D MI-DRAGON: a new predictor for protein-ligands interactions and theoretic-experimental studies of US FDA drug-target network, oxoisoaporphine inhibitors for MAO-A and human parasite proteins | Q47992710 | ||
| Large-scale prediction of drug–target interactions using protein sequences and drug topological structures | Q51306231 | ||
| Structure-based maximal affinity model predicts small-molecule druggability. | Q51925540 | ||
| Network pharmacology | Q57932497 | ||
| Using the TOPS-MODE approach to fit multi-target QSAR models for tyrosine kinases inhibitors | Q83742929 | ||
| Editorial: Computational methods for drug repurposing | Q84570124 | ||
| Computer-aided prediction of toxicity with substructure pattern and random forest | Q108527468 | ||
| P433 | issue | 10 | |
| P921 | main subject | computational biology | Q177005 |
| P304 | page(s) | 669-681 | |
| P577 | publication date | 2014-09-26 | |
| P1433 | published in | Molecular Informatics | Q3319476 |
| P1476 | title | Computational Prediction of DrugTarget Interactions Using Chemical, Biological, and Network Features | |
| P478 | volume | 33 |
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