Abstract
Herein we present the algorithm and performance assessment of our newly developed conformer generator iCon that was implemented in LigandScout 4.0. Two data sets of high-quality X-ray structures of drug-like small molecules originating from the Protein Data Bank (200 ligands) and the Cambridge Structural Database (481 molecules) were used to validate iCon's performance in the reproduction of experimental conformations. OpenEye's conformer generator OMEGA was subjected to the same evaluation and served as a reference software in this analysis. We tested several setting patterns in order to identify the most suitable and efficient ones for conformational sampling with iCon; equivalent settings were also tested on OMEGA in order to compare the results obtained from the two programs and better assess iCon's performance. Overall, this study proved that iCon is able to generate reliable representative conformational ensembles of drug-like small molecules, yielding results comparable to those showed by OMEGA, and thus is ready to serve as a valuable tool for computer-aided drug design.
Highlights
Conformer generation still represents a remarkably important topic within the Computer-Aided Molecular Design (CAMD) field
These data sets comprise a total of 681 structures (200 for the Protein Data Bank (PDB) data set and 481 for the Cambridge Structural Database (CSD) data set) and were selected by Hawkins and co-workers to validate the performance of their conformer generator OMEGA (Hawkins and Nicholls, 2012)
In the CSD data set about 95% of compounds had less than 7 rotatable bond (RB) and no molecules with more than 9 rotors were found, whereas 29% of PDB ligands presented more than 7 RBs and 15% of compounds showed an average of 13 rotors
Summary
Conformer generation still represents a remarkably important topic within the Computer-Aided Molecular Design (CAMD) field. The exploration of the conformational space of small molecules is a challenging task that is required for different applications ranging from the search for the molecule conformation at its global energy minimum to the generation of conformational ensembles that properly represent all possible low-energy spatial dispositions that molecules are allowed to assume This latter analysis constitutes a fundamental step in many in-silico studies comprising pharmacophore modeling and pharmacophore-based virtual screening (VS) (Güner et al, 2004; Wolber and Langer, 2005), shape-based similarity searches (Hawkins et al, 2007; Sastry et al, 2011), docking and other VS methods (Cross et al, 2010; McGann, 2012), as well as different approaches like 3D and 4D QSAR modeling (Shim and MacKerell, 2011). In this context, automated clustering algorithms have been recently applied for resampling conformational
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