Virtual predictive tool rationalizes lead compound identification for new drug discovery

The preliminary binding affinity results of chemical inhibition were available while the atomic details of the target active site were unknown. A consensus profile was computed from a group of known inhibitors with respect to groups of atoms or moieties best aligned among the Simplified Molecular Input Line Entry System (SMILES) strings.

The method used a cost-effective computational technique to identify homologues. Molecules, which matched either a part of, or the entire profile, were selected to re-construct sub-structures or moieties of the final lead compound, which had the optimized inhibition characteristics. Researchers then tested the binding affinity of these screened chemical homologues, and suggested combinations to improve the binding characteristics.

The novel algorithm from Infosys for the Ligand Identification and Matching tool improved sensitivity while lowering computational costs. Set up in a framework leveraging access to multiple services and applications, the tool was validated with the family of Cox-2 inhibitors.

Infosys relied on innovation in this new frontier of pharmacological research to successfully develop a novel algorithm for the virtual predictive tool, resulting in cycle time reduction for lead identification.