Abstract
MotivationUsing molecular similarity to discover bioactive small molecules with novel chemical scaffolds can be computationally demanding. We describe Ultra-fast Shape Recognition with Atom Types (UFSRAT), an efficient algorithm that considers both the 3D distribution (shape) and electrostatics of atoms to score and retrieve molecules capable of making similar interactions to those of the supplied query.ResultsComputational optimization and pre-calculation of molecular descriptors enables a query molecule to be run against a database containing 3.8 million molecules and results returned in under 10 seconds on modest hardware. UFSRAT has been used in pipelines to identify bioactive molecules for two clinically relevant drug targets; FK506-Binding Protein 12 and 11β-hydroxysteroid dehydrogenase type 1. In the case of FK506-Binding Protein 12, UFSRAT was used as the first step in a structure-based virtual screening pipeline, yielding many actives, of which the most active shows a KD, app of 281 µM and contains a substructure present in the query compound. Success was also achieved running solely the UFSRAT technique to identify new actives for 11β-hydroxysteroid dehydrogenase type 1, for which the most active displays an IC50 of 67 nM in a cell based assay and contains a substructure radically different to the query. This demonstrates the valuable ability of the UFSRAT algorithm to perform scaffold hops.Availability and ImplementationA web-based implementation of the algorithm is freely available at http://opus.bch.ed.ac.uk/ufsrat/.
Highlights
In the case of FK506-Binding Protein 12, Ultra-fast Shape Recognition with Atom Types (UFSRAT) was used as the first step in a structure-based virtual screening pipeline, yielding many actives, of which the most active shows a KD, app of 281 μM and contains a substructure present in the query compound
The concept of molecular similarity has been exploited in most chemical fields and has been used to great effect in the pharmaceutical industry to reduce the massive cost of drug development [1,2,3]
In this paper we describe the use of our UFSRAT algorithm in virtual screening pipelines to identify inhibitors of two unrelated enzymes; FK506-Binding Protein 12 (FKBP12) and 11β-hydroxysteroid dehydrogenase type 1 (11β-HSD1)
Summary
The concept of molecular similarity has been exploited in most chemical fields and has been used to great effect in the pharmaceutical industry to reduce the massive cost of drug development [1,2,3]. When molecular similarity is employed in ligand-based virtual screening it offers the ability to carry out searches for actives where little is known about the drug receptor, only molecules which bind to it [4,5,6,7,8]. Successful scaffold hopping methodologies commonly describe the virtual compound in a way that encodes both the 3D shape of the molecule and the electrostatic and hydrophobic properties. This is key to successful lead discovery because electrostatic and van der Waals interactions are very sensitive to bond geometry and distance. We have developed the idea of capturing molecular shape using parameters determined from the interatomic distance distributions first proposed by Ballester and Richards [11, 12] and incorporate these pre-calculated molecular descriptors into a searchable database of available compounds [13]
Sign-in/Register to view highlights & summary Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
