The ChEMBL bioactivity database: an update

Bento, A. Patrícia; Gaulton, Anna; Hersey, Anne; Bellis, Louisa J.; Chambers, Jon; Davies, Mark; Krüger, Felix; Light, Yvonne; Mak, Lora; McGlinchey, Shaun; Nowotka, Michał; Papadatos, George; Santos, Rita; Overington, John P.

doi:10.1093/nar/gkt1031

Cited by 1,405 publications

(1,397 citation statements)

References 38 publications

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“…For the cases where the database entries are manually curated and consistent, we used CHEMBL (Bento et al, 2014), DrugBank (Law et al, 2014) and PubChem (Kim et al, 2016). We downloaded GenAge model organism and human data sets (Tacutu et al, 2018) on 10th October 2017 using GenAge website.…”

Section: Methodsmentioning

confidence: 99%

Gene expression‐based drug repurposing to target aging

et al. 2018

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Aging is the largest risk factor for a variety of noncommunicable diseases. Model organism studies have shown that genetic and chemical perturbations can extend both lifespan and healthspan. Aging is a complex process, with parallel and interacting mechanisms contributing to its aetiology, posing a challenge for the discovery of new pharmacological candidates to ameliorate its effects. In this study, instead of a target‐centric approach, we adopt a systems level drug repurposing methodology to discover drugs that could combat aging in human brain. Using multiple gene expression data sets from brain tissue, taken from patients of different ages, we first identified the expression changes that characterize aging. Then, we compared these changes in gene expression with drug‐perturbed expression profiles in the Connectivity Map. We thus identified 24 drugs with significantly associated changes. Some of these drugs may function as antiaging drugs by reversing the detrimental changes that occur during aging, others by mimicking the cellular defence mechanisms. The drugs that we identified included significant number of already identified prolongevity drugs, indicating that the method can discover de novo drugs that meliorate aging. The approach has the advantages that using data from human brain aging data, it focuses on processes relevant in human aging and that it is unbiased, making it possible to discover new targets for aging studies.

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Section: Methodsmentioning

confidence: 99%

Gene expression‐based drug repurposing to target aging

et al. 2018

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“…This article is a PNAS Direct Submission. 1 To whom correspondence may be addressed. Email: ljc37@cam.ac.uk or alphalee@g.…”

Section: Significancementioning

confidence: 99%

“…Numerous attempts have been made to develop computational algorithms to predict the binding affinity of a ligand to a given receptor, which would allow potential compounds to be screened in silico, reducing costs and saving time. In particular, in response to the wealth of experimental data that exists both within pharmaceutical companies, and also in freely accessible online databases such as ChEMBL (1), approaches that attempt to "learn" from these data are increasingly gaining attention (2).…”

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confidence: 99%

Predicting protein–ligand affinity with a random matrix framework

Lee

Brenner

Colwell

2016

Proc. Natl. Acad. Sci. U.S.A.

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Rapid determination of whether a candidate compound will bind to a particular target receptor remains a stumbling block in drug discovery. We use an approach inspired by random matrix theory to decompose the known ligand set of a target in terms of orthogonal "signals" of salient chemical features, and distinguish these from the much larger set of ligand chemical features that are not relevant for binding to that particular target receptor. After removing the noise caused by finite sampling, we show that the similarity of an unknown ligand to the remaining, cleaned chemical features is a robust predictor of ligand-target affinity, performing as well or better than any algorithm in the published literature. We interpret our algorithm as deriving a model for the binding energy between a target receptor and the set of known ligands, where the underlying binding energy model is related to the classic Ising model in statistical physics.drug discovery | random matrix theory | protein-ligand affinity | computational pharmacology | statistical physics F inding new ligands that bind to a given target is both a crucial step and a major stumbling block in modern drug discovery. Numerous attempts have been made to develop computational algorithms to predict the binding affinity of a ligand to a given receptor, which would allow potential compounds to be screened in silico, reducing costs and saving time. In particular, in response to the wealth of experimental data that exists both within pharmaceutical companies, and also in freely accessible online databases such as ChEMBL (1), approaches that attempt to "learn" from these data are increasingly gaining attention (2).An intuitive data-driven approach builds on the hypothesis that chemical commonalities among the known ligand set reveal salient features of the binding site. A corollary is that ligands with similar chemical functionality are expected to share similar binding affinity toward a particular receptor (3,4). This suggests that the known ligand set of a given target can be used to learn criteria that predict whether a novel ligand will bind to the target. This ligand-based approach is a powerful paradigm that does not require structural information about the receptor, which is potentially arduous to obtain, unlike other more atomistic methods such as docking or molecular dynamics.Any ligand-based method requires a way to quantify the chemical functionalities of a ligand, and various chemical descriptors have been proposed. Examples include a vector of measured or predicted physical properties (5-8), a vector enumerating the presence or absence of known functional groups on the ligand (9, 10), a vectorial representation of connectivities in the molecular graph (11, 12) (known also as molecular fingerprints), and simply the 3D shape of the ligand (13-16). Existing approaches then take the descriptor associated with each ligand and compare ligands with each other, for example through the Tanimoto coefficient (17, 18).Nonetheless, regardless of how ligand chemical func...

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“…The corresponding protein description, sequences, PubMed entry were obtained from UniProt release 2015_08 (Consortium, 2015). The information regarding the 3-D structures and targets were extracted from Protein Data Bank (PDB) and ChEMBL (Berman et al, 2002;Bento et al, 2014).…”

Section: Proteomic Datamentioning

confidence: 99%

OrCanome: a Comprehensive Resource for Oral Cancer

Bhartiya

Kumar²,

Singh³

et al. 2016

Asian Pacific Journal of Cancer Prevention

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Oral cancer is one of the most prevalent cancers in India but the underlying mechanisms are minimally unraveled. Cancer research has immensely benefited from genome scale high throughput studies which have contributed to expanding the volume of data. Such datasets also exist for oral cancer genes but there has been no consolidated approach to integrate the data to reveal meaningful biological information. OrCanome is one of the largest and comprehensive, user-friendly databases of oral cancer. It features a compilation of over 900 genes dysregulated in oral cancer and provides detailed annotations of the genes, transcripts and proteins along with additional information encompassing expression, inhibitors, epitopes and pathways. The resource has been envisioned as a one-stop solution for genomic, transcriptomic and proteomic annotation of these genes and the integrated approach will facilitate the identification of potential biomarkers and therapeutic targets.

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The ChEMBL bioactivity database: an update

Cited by 1,405 publications

References 38 publications

Gene expression‐based drug repurposing to target aging

Gene expression‐based drug repurposing to target aging

Predicting protein–ligand affinity with a random matrix framework

OrCanome: a Comprehensive Resource for Oral Cancer

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