Conformational Sampling of Small Molecules With iCon: Performance Assessment in Comparison With OMEGA
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.
Read the paper
Related studies
- eggNOG 5.0: a hierarchical, functionally and phylogenetically annotated orthology resource based on 5090 organisms and 2502 viruses
- Online Survival Analysis Software to Assess the Prognostic Value of Biomarkers Using Transcriptomic Data in Non-Small-Cell Lung Cancer
- OptiType: precision HLA typing from next-generation sequencing data
- Nicotinic Cholinergic System and COVID-19: In Silico Identification of an Interaction between SARS-CoV-2 and Nicotinic Receptors with Potential Therapeutic Targeting Implications
- Saccharomyces cerevisiae cells have three Omega class glutathione S-transferases acting as 1-Cys thiol transferases
- Hemolysis at low blood flow rates: in-vitro and in-silico evaluation of a centrifugal blood pump
Go deeper in Evidence Hub
Evidence Hub adds a study data sheet (design, sample size, evidence level, key numbers), an AI summary and a chat grounded in the paper's text.