Computational Chemical and Structural Biology
BCL ChemInformatics Method Development for QSAR ligand-based virtual screening
Virtual screening is a computational technique that aids the drug discovery process. The focus of this project is ligand-based screening where, in contrast to structure based screening, knowledge about the biological target structure is not necessary. This way hits of novel chemical structures can be determined that bind to a macromolecular target of interest, like receptors or enzymes.
Large compound libraries were tested in High Through-put screens for biological activity and serve as a knowledge base. Chemical structures were encoded by molecular descriptors and used as input to machine learning techniques like Artificial Neural Networks (ANNs), Support Vector Machines (SVMs) to develop quantitative structure–activity relationship (QSAR) models.
QSAR is an area of computational research that builds virtual models to seek quantitatively correlate complex non-linear relations between chemical and physical properties of a molecule with its biological response, such as activity for a specific biological target.
This technology allows rapid screening in large compound libraries of small organic molecules to find novel structures that are most likely to bind a desired drug target in silico.
The aim of this project is the method development of ChemInformatics tools for QSAR modeling in the BCL.
Figure 1: BCL::Cheminfo: A virtual high-throughput screening suite that provides a workflow from molecule preparation, data set generation, QSAR model training, cross-validation, and analysis to virtual screening of large compound libraries.