ArticleBritish journal of pharmacology2013
Comparison of ultra-fast 2D and 3D ligand and target descriptors for side effect prediction and network analysis in polypharmacology.
Article in British journal of pharmacology, 2013. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
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Who cites it
4 citing papers in PubMed.
- A multi-reference poly-conformational method forPeerJ · 2022Article
- Predicting the Reliability of Drug-target Interaction Predictions with Maximum Coverage of Target Space.Scientific reports · 2017Article
- 3D Chemical Similarity Networks for Structure-Based Target Prediction and Scaffold Hopping.ACS chemical biology · 2016Article
- How Reliable Are Ligand-Centric Methods for Target Fishing?Frontiers in chemistry · 2016Article
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Authors and funding
5 authors.
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Abstract
background and purposeSome existing computational methods are used to infer protein targets of small molecules and can therefore be used to find new targets for existing drugs, with the goals of re-directing the molecule towards a different therapeutic purpose or explaining off-target effects due to multiple targeting. Inherent limitations, however, arise from the fact that chemical analogy is calculated on the basis of common frameworks or scaffolds and also because target information is neglected. The method we present addresses these issues by taking into account 3D information from both the ligand and the target. EXPERIMENTAL APPROACH: ElectroShape is an established method for ultra-fast comparison of the shapes and charge distributions of ligands that is validated here for prediction of on-target activities, off-target profiles and adverse effects of drugs and drug-like molecules taken from the DrugBank database. KEY
resultsThe method is shown to predict polypharmacology profiles and relate targets from two complementary viewpoints (ligand- and target-based networks). CONCLUSIONS AND IMPLICATIONS: The open-access web tool presented here (http://ub.cbm.uam.es/chemogenomics/) allows interactive navigation in a unified 'pharmacological space' from the viewpoints of both ligands and targets. It also enables prediction of pharmacological profiles, including likely side effects, for new compounds. We hope this web interface will help many pharmacologists to become aware of this new paradigm (up to now mostly used in the realm of the so-called 'chemical biology') and encourage its use with a view to revealing 'hidden' relationships between new and existing compounds and pharmacologically relevant targets.
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