UFSRAT: Ultra-Fast Shape Recognition with Atom Types -The Discovery of Novel Bioactive Small Molecular Scaffolds for FKBP12 and 11 beta HSD1

作者：Shave Steven; Blackburn Elizabeth A; Adie Jillian; Houston Douglas R; Auer Manfred; Webster Scott P; Taylor Paul; Walkinshaw Malcolm D^*

来源：PLos One, 2015, 10(2): e0116570.

DOI：10.1371/journal.pone.0116570

Motivation
Using 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.
Results
Computational 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 11 beta-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 K-D, (app) of 281 mu M and contains a substructure present in the query compound. Success was also achieved running solely the UFSRAT technique to identify new actives for 11 beta-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 Implementation
A web-based implementation of the algorithm is freely available at http://opus.bch.ed.ac.uk/ufsrat/.

• 出版日期2015-2-6