Dataset Information

Discovery of High-Affinity Cannabinoid Receptors Ligands through a 3D-QSAR Ushered by Scaffold-Hopping Analysis.

ABSTRACT: Two 3D quantitative structure?activity relationships (3D-QSAR) models for predicting Cannabinoid receptor 1 and 2 (CB? and CB?) ligands have been produced by way of creating a practical tool for the drug-design and optimization of CB? and CB? ligands. A set of 312 molecules have been used to build the model for the CB? receptor, and a set of 187 molecules for the CB? receptor. All of the molecules were recovered from the literature among those possessing measured K_i values, and Forge was used as software. The present model shows high and robust predictive potential, confirmed by the quality of the statistical analysis, and an adequate descriptive capability. A visual understanding of the hydrophobic, electrostatic, and shaping features highlighting the principal interactions for the CB? and CB? ligands was achieved with the construction of 3D maps. The predictive capabilities of the model were then used for a scaffold-hopping study of two selected compounds, with the generation of a library of new compounds with high affinity for the two receptors. Herein, we report two new 3D-QSAR models that comprehend a large number of chemically different CB? and CB? ligands and well account for the individual ligand affinities. These features will facilitate the recognition of new potent and selective molecules for CB? and CB? receptors.

SUBMITTER: Floresta G

PROVIDER: S-EPMC6225167 | biostudies-literature | 2018 Aug

REPOSITORIES: biostudies-literature

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Discovery of High-Affinity Cannabinoid Receptors Ligands through a 3D-QSAR Ushered by Scaffold-Hopping Analysis.

Floresta Giuseppe G Apirakkan Orapan O Rescifina Antonio A Abbate Vincenzo V

Molecules (Basel, Switzerland) 20180830 9

Two 3D quantitative structure⁻activity relationships (3D-QSAR) models for predicting Cannabinoid receptor 1 and 2 (CB₁ and CB₂) ligands have been produced by way of creating a practical tool for the drug-design and optimization of CB₁ and CB₂ ligands. A set of 312 molecules have been used to build the model for the CB₁ receptor, and a set of 187 molecules for the CB₂ receptor. All of the molecules were recovered from the literature among those possessing measured <i>K</i><sub>i</sub> values, and ...[more]

PMID: 30200181

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Project description:BackgroundVirtual screening methods are now well established as effective to identify hit and lead candidates and are fully integrated in most drug discovery programs. Ligand-based approaches make use of physico-chemical, structural and energetics properties of known active compounds to search large chemical libraries for related and novel chemotypes. While 2D-similarity search tools are known to be fast and efficient, the use of 3D-similarity search methods can be very valuable to many research projects as integration of "3D knowledge" can facilitate the identification of not only related molecules but also of chemicals possessing distant scaffolds as compared to the query and therefore be more inclined to scaffolds hopping. To date, very few methods performing this task are easily available to the scientific community.ResultsWe introduce a new approach (LigCSRre) to the 3D ligand similarity search of drug candidates. It combines a 3D maximum common substructure search algorithm independent on atom order with a tunable description of atomic compatibilities to prune the search and increase its physico-chemical relevance. We show, on 47 experimentally validated active compounds across five protein targets having different specificities, that for single compound search, the approach is able to recover on average 52% of the co-actives in the top 1% of the ranked list which is better than gold standards of the field. Moreover, the combination of several runs on a single protein target using different query active compounds shows a remarkable improvement in enrichment. Such Results demonstrate LigCSRre as a valuable tool for ligand-based screening.ConclusionLigCSRre constitutes a new efficient and generic approach to the 3D similarity screening of small compounds, whose flexible design opens the door to many enhancements. The program is freely available to the academics for non-profit research at: http://bioserv.rpbs.univ-paris-diderot.fr/LigCSRre.html.

Dataset Information

Discovery of High-Affinity Cannabinoid Receptors Ligands through a 3D-QSAR Ushered by Scaffold-Hopping Analysis.

Publications

Discovery of High-Affinity Cannabinoid Receptors Ligands through a 3D-QSAR Ushered by Scaffold-Hopping Analysis.

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