Making every SAR point count: the development of Chemistry Connect for the large-scale integration of structure and bioactivity data

Mureşan, Sorel; Petrov, Plamen; Southan, Christopher; Kjellberg, Magnus J.; Kogej, Thierry; Tyrchan, Christian; Várkonyi, Péter L.; Xie, Paul Hongxing

doi:10.1016/j.drudis.2011.10.005

Cited by 72 publications

(82 citation statements)

References 23 publications

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“…1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 16 To create (Q)SAR models, we used the program GUSAR. 17,18 A combination of three types of descriptors is used in GUSAR: (1) QNA (Quantitative Neighborhoods of Atoms) descriptors, (2) descriptors that are both topological (length and volume) and physicochemical parameters of the whole molecule, and (3) descriptors based on the prediction of the biological activity spectra by the program PASS. 19,20 The robustness of the GUSAR algorithm vis-à-vis the utilization of data sets that are non-homogeneous (in terms of chemical similarity) has been shown earlier.…”

Section: Modeling Set Preparation From Chembl Datamentioning

confidence: 99%

QSAR Modeling Using Large-Scale Databases: Case Study for HIV-1 Reverse Transcriptase Inhibitors

Tarasova

Urusova

Филимонов

et al. 2015

J. Chem. Inf. Model.

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Large-scale databases are important sources of training sets for various QSAR modeling approaches. Generally, these databases contain information extracted from different sources. This variety of sources can produce inconsistency in the data, defined as sometimes widely diverging activity results for the same compound against the same target. Because such inconsistency can reduce the accuracy of predictive models built from these data, we are addressing the question of how best to use data from publicly and commercially accessible databases to create accurate and predictive QSAR models. We investigate the suitability of commercially and publicly available databases to QSAR modeling of antiviral activity (HIV-1 reverse transcriptase (RT) inhibition). We present several methods for the creation of modeling (i.e., training and test) sets from two, either commercially or freely available, databases: Thomson Reuters Integrity and ChEMBL. We found that the typical predictivities of QSAR models obtained using these different modeling set compilation methods differ significantly from each other. The best results were obtained using training sets compiled for compounds tested using only one method and material (i.e., a specific type of biological assay). Compound sets aggregated by target only typically yielded poorly predictive models. We discuss the possibility of "mix-and-matching" assay data across aggregating databases such as ChEMBL and Integrity and their current severe limitations for this purpose. One of them is the general lack of complete and semantic/computer-parsable descriptions of assay methodology carried by these databases that would allow one to determine mix-and-matchability of result sets at the assay level.

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Section: Modeling Set Preparation From Chembl Datamentioning

confidence: 99%

QSAR Modeling Using Large-Scale Databases: Case Study for HIV-1 Reverse Transcriptase Inhibitors

Tarasova

Urusova

Филимонов

et al. 2015

J. Chem. Inf. Model.

View full text Add to dashboard Cite

show abstract

“…Advances in screening technologies and an increasing awareness of the value of biological data utilization have led to the accumulation of diverse small molecule bioactivity data in public and corporate repositories [1,2]. The wealth of information ranges from quantitative (such as gene expression, cellular phenotypes and protein phosphorylation) to categorical (such as clinical adverse events) data.…”

Section: Introductionmentioning

confidence: 99%

The opportunities of mining historical and collective data in drug discovery

Wassermann¹,

Lounkine²,

Davies³

et al. 2015

Drug Discovery Today

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“…Compound structures were standardized according to AstraZeneca rules 13 and used for compound identification and comparison of compounds tested in both internal and external HTS assays. 4 Compound activity between primary HTS assays screened on the same human-derived protein target (defined by gene symbol) was analyzed using compound activity overlap A 1,2 /(A 1 + A 2 + A 1,2 ), where A 1,2 is the number of compounds active in both assay 1 and assay 2, and A 1 and A 2 are the number of compounds only active in one of the assays and inactive in the other. Where more than two assays were evaluated for the same target, the compound activity was analyzed both in pairs as above and by evaluating the compound activity overlap (also calculated in assay pairs according to the equation above) using only compounds tested in all three assays.…”

Section: Compound Activity Analysismentioning

confidence: 99%

“…There have been both proprietary and open public initiatives to improve the integration of HTS data from different sources. One example of a proprietary solution is the ChemistryConnect application, 4 while Open PHACTS (http://openphacts.org) and BARD (http://bard. nih.gov) are examples of two major publicly funded initiatives for better integration of screening data.…”

Section: Introductionmentioning

confidence: 99%

Using the BioAssay Ontology for Analyzing High-Throughput Screening Data

et al. 2015

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High-throughput screening (HTS) is the main starting point for hit identification in drug discovery programs. This has led to a rapid increase of available screening data both within pharmaceutical companies and the public domain. We have used the BioAssay Ontology (BAO) 2.0 for assay annotation within AstraZeneca to enable comparison with external HTS methods. The annotated assays have been analyzed to identify technology gaps, evaluate new methods, verify active hits, and compare compound activity between in-house and PubChem assays. As an example, the binding of a fluorescent ligand to formyl peptide receptor 1 (FPR1, involved in inflammation, for example) in an in-house HTS was measured by fluorescence intensity. In total, 155 active compounds were also tested in an external ligand binding flow cytometry assay, a method not used for in-house HTS detection. Twelve percent of the 155 compounds were found active in both assays. By the annotation of assay protocols using BAO terms, internal and external assays can easily be identified and method comparison facilitated. They can be used to evaluate the effectiveness of different assay methods, design appropriate confirmatory and counterassays, and analyze the activity of compounds for identification of technology artifacts.

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Making every SAR point count: the development of Chemistry Connect for the large-scale integration of structure and bioactivity data

Cited by 72 publications

References 23 publications

QSAR Modeling Using Large-Scale Databases: Case Study for HIV-1 Reverse Transcriptase Inhibitors

QSAR Modeling Using Large-Scale Databases: Case Study for HIV-1 Reverse Transcriptase Inhibitors

The opportunities of mining historical and collective data in drug discovery

Using the BioAssay Ontology for Analyzing High-Throughput Screening Data

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