The client sought a partner in research informatics, to collaborate in the development of a new virtual predictive tool which would rationalize the process of identifying and validating lead compounds to aid in the development of new drugs.

 

Key Challenges

The traditional process of identifying lead compounds

 

Available tools had proved unsatisfactory in terms of sensitivity resulted in systemic errors.

The Impact

Rationalization of drug design addressing issues of safety and efficacy

 

Fast and accurate predictive high throughput screening (HTS)

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The Solution

Novel algorithm for the virtual predictive tool, resulting in cycle time reduction for lead identification.

 
   

The novel algorithm from Infosys for the Ligand Identification and Matching tool improved sensitivity while lowering computational costs.

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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.

Validation through correction of false positive experimental data by deterministic methods