Literature DB >> 22978710

Growth of ligand-target interaction data in ChEMBL is associated with increasing and activity measurement-dependent compound promiscuity.

Ye Hu1, Jürgen Bajorath.   

Abstract

Compounds with high-confidence target annotations and activity measurements in the original and current release of the ChEMBL database have been compared to better understand how the growth of compound activity data might influence the spectrum of ligand-target interactions and the degree of target promiscuity among active compounds. Compared to the original ChEMBL release, a significant increase in the proportion of target promiscuous compounds was observed in the current version. The presence of these compounds led to large-magnitude changes in compound activity-based target and target family relationships and to a reorganization of major target communities. Surprisingly, however, this strong trend toward increasing target promiscuity was largely caused by growth of compounds with exclusive IC(50) measurements. By contrast, compounds with available equilibrium constants, which were also added in large amounts, did not substantially alter compound-based target relationships and notably contribute to increasing target promiscuity. These findings suggest that apparent compound promiscuity is much dependent on experimental conditions under which activities are determined and that care should be taken when evaluating promiscuity and polypharmacology on the basis of assay-dependent activity measurements.

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Year:  2012        PMID: 22978710     DOI: 10.1021/ci3003304

Source DB:  PubMed          Journal:  J Chem Inf Model        ISSN: 1549-9596            Impact factor:   4.956


  10 in total

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Authors:  Ye Hu; Jürgen Bajorath
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3.  High-resolution view of compound promiscuity.

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Review 4.  Exploring compound promiscuity patterns and multi-target activity spaces.

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7.  Beyond the hype: deep neural networks outperform established methods using a ChEMBL bioactivity benchmark set.

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Journal:  Int J Mol Sci       Date:  2021-10-26       Impact factor: 5.923

9.  Comparability of mixed IC₅₀ data - a statistical analysis.

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10.  VB-MK-LMF: fusion of drugs, targets and interactions using variational Bayesian multiple kernel logistic matrix factorization.

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Journal:  BMC Bioinformatics       Date:  2017-10-04       Impact factor: 3.169

  10 in total

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