GLIDA: GPCR ligand database for chemical genomics drug discovery database and tools update

Okuno, Yasushi; Tamon, Akiko; Yabuuchi, Hiroaki; Niijima, Satoshi; Minowa, Yohsuke; Tonomura, Koichiro; Kunimoto, Ryo; Feng, Chunlai

doi:10.1093/nar/gkm948

Cited by 122 publications

(120 citation statements)

References 21 publications

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“…Table 3). The DRD2-perphenazine association is substantiated in STITCH by the databases DrugBank [37], Matador [38], TTD [51], as well as GLIDA [52], where one can find that perphenazine is an antagonist of DRD2. Interestingly, we found a drug interaction on drugs.com for perphenazine and dopamine stating that "Dopamine may be ineffective for the treatment of excessively low blood pressure, shock, or collapse if you are receiving perphenazine.…”

Section: Discussionmentioning

confidence: 97%

Novel Neural Network Approach to Predict Drug-Target Interactions Based on Drug Side Effects and Genome-Wide Association Studies

et al. 2018

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Aims: We propose a novel machine learning approach to expand the knowledge about drug-target interactions. Our method may help to develop effective, less harmful treatment strategies and to enable the detection of novel indications for existing drugs. Methods: We developed a novel machine learning strategy to predict drug-target interactions based on drug side effects and traits from genome-wide association studies. We integrated data from the databases SIDER and GWASdb and utilized them in a unique way by a neural network approach. Results: We validate our method using drug-target interactions from the STITCH database. In addition, we compare the chemical similarity of the predicted target to known targets of the drug under consideration and present literature-based evidence for predicted interactions. We find drug combination warnings for drugs we predict to target the same protein, hinting to synergistic effects aggravating harmful events. This substantiates the translational value of our approach, because we are able to detect drugs that should be taken together with care due to common mechanisms of action. Conclusion: Taken together, we conclude that our approach is able to generate a novel and clinically applicable insight into the molecular determinants of drug action.

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Section: Discussionmentioning

confidence: 97%

Novel Neural Network Approach to Predict Drug-Target Interactions Based on Drug Side Effects and Genome-Wide Association Studies

et al. 2018

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“…Experimentally determined GPCR agonists and antagonists were taken from GLIDA 17 and Prous Science Integrity databases. 18 GLIDA is a public GPCR-related Chemical Genomics database that is primarily focused on the integration of information between GPCRs and their ligands.…”

Section: Methodsmentioning

confidence: 99%

“…It provides interaction data between GPCRs and their ligands, along with chemical information on the ligands, as well as biological information regarding GPCRs. 17 Prous Science Integrity enables researchers to manage and correlate chemistry and genomics data with experimental and clinical pharmacology results and with a knowledge base of disease entities. 18 Only the human full agonists and antagonists were retrieved but both partial and inverse agonists were retrieved from these databases.…”

Section: Methodsmentioning

confidence: 99%

Bayesian Model for the Classification of GPCR Agonists and Antagonists

Choi¹,

Kim²,

Jung³

et al. 2010

Bulletin of the Korean Chemical Society

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G-protein coupled receptors (GPCRs) are involved in a wide variety of physiological processes and are known to be targets for nearly 50% of drugs. The various functions of GPCRs are affected by their cognate ligands which are mainly classified as agonists and antagonists. The purpose of this study is to develop a Bayesian classification model, that can predict a compound as either human GPCR agonist or antagonist. Total 6627 compounds experimentally determined as either GPCR agonists or antagonists covering all the classes of GPCRs were gathered to comprise the dataset. This model distinguishes GPCR agonists from GPCR antagonists by using chemical fingerprint, FCFP_6. The model revealed distinctive structural characteristics between agonistic and antagonistic compounds: in general, 1) GPCR agonists were flexible and had aliphatic amines, and 2) GPCR antagonists had planar groups and aromatic amines. This model showed very good discriminative ability in general, with pretty good discriminant statistics for the training set (accuracy: 90.1%) and a good predictive ability for the test set (accuracy: 89.2%). Also, receiver operating characteristic (ROC) plot showed the area under the curve (AUC) to be 0.957, and Matthew's Correlation Coefficient (MCC) value was 0.803. The quality of our model suggests that it could aid to classify the compounds as either GPCR agonists or antagonists, especially in the early stages of the drug discovery process.

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“…The number of GPCRs in the human genome is more than 1000, with at least 400 of therapeutic interest. In contrast to such a number of potential therapeutical GPCRs, drugs currently available on the market address less than 10% of them (Okuno et al, 2008). For a number of GPCRs, the only ligand known is its endogenous (natural) ligand, and for a considerable number of cases, some GPCRs are orphaned, meaning that no ligand is known for which binding occurs.…”

Section: Gpcr Ligands As Drug Targetsmentioning

confidence: 99%

“…The GPCR LIgand DAtabase (GLIDA) represents a major effort in using protein and chemical similarity informatics techniques independently as well as synergystically (Okuno et al, 2008). As discussed above, the majority of drugs available on the market address only a small fraction of GPCRs.…”

Section: Gpcr-ligand Datamentioning

confidence: 99%