Connectivity Enhanced Structure Activity Relationship (ceSAR): A Novel Approach to Increase Accuracy and Speed of Virtual Drug Discovery

Abstract

Experimental drug discovery and repurposing is a costly and time‐consuming process. Here, a novel approach, dubbed connectivity enhanced structure activity relationship (ceSAR) is introduced. ceSAR combines traditional virtual screening methodologies with cheminformatics and Library of Integrated Network‐Based Cellular Signatures (LINCS) gene expression signatures of genetic and chemical perturbations. This methodology combines structural similarity of small molecules with transcriptional connectivity and virtual screening‐predicted specific interacting molecules for the enhancement of structure activity relationship (SAR) and lead compound discovery. The transcriptional connectivity aspect of ceSAR is implemented as an R Shiny package, titled Sig2Lead, that employs connectivity between chemical and genetic signatures included in the LINCS library to identify small molecule inhibitors of user‐targeted pathways. Chemical libraries are reduced with ceSAR by first measuring concordance of transcriptomic signatures between genetic and chemical perturbation, identifying putative pathway inhibitors, followed by traditional docking approaches to re‐score the remaining library to identify compounds likely to interact with the target of interest. This combination identifies compounds that are both pathway inhibitors and have shape complementarity to the target of interest, making the most likely scenario that the molecules are directly interacting with the target protein of interest to modulate the pathway. Through integration of these orthogonal approaches, both an increased accuracy and speed can be observed, with the speed increase being several orders of magnitude.Support or Funding InformationThis work was supported in part by the National Institutes of Health grants U54 HL127624ceSAR combines transcriptional connectivity and virtual screening to identify novel inhibitors from a screened chemical library. The library is first reduced using transcriptional connectivity to a knockdown of the target gene to restrict the search to putative pathway inhibitors. These pathway inhibitors are then reduced further using traditional virtual screening approaches to find compounds with shape complementarity to the protein of interest.Figure 1Comparison of results of an early drug screen using a combination of docking with AutoDock4.2.6 and Sig2Lead shows a dramatic reduction in compounds without losing the best‐performing compounds. If used in this case, the ceSAR method would have excluded 51 of the 147 tested compounds, of which 45 had no observable IC50 via a Fluorescence Polarization assay, thus driving library enrichment. This reduction would have saved time and reagents through preventing testing of as many spurious compounds.Figure 2