Computational Chemistry

Lead discovery and optimization:

We augment cell-based and whole animal discovery efforts with computational lead discovery. High-content screening hits are followed up with ligand-based approaches in order to identify analogous compounds and/or different, chemically more tractable, scaffolds that preserve the shape and surface properties of original hits. Using state-of-the-art modeling software on our departmental computing cluster, we evaluate virtual libraries composed of millions of purchasable compounds within days. By expanding a novel chemical series with in silico hits, we establish a quantitative structure-activity relationship that helps medicinal chemistry for optimization of the hits.

When target structural data are available, we model the interactions of hit compounds using molecular docking software integrated our models and tools for modeling protein flexibility developed at the University of Pittsburgh. This approach helps in illuminating the molecular mechanism of action of hits, e.g., allosteric or orthosteric inhibition. A validated docking model and comparative analysis of structures of other target family members further helps us to optimize efficacy and selectivity of hit compounds.

A wide range of computational tools are also available for Public and Contract/Consortium use from the Xie Research Group at Xie Lab Core Technologies.

Department of Computational and Systems Biology