Abstract
Bioinformatics approaches are becoming ever more essential in translational drug discovery both in academia and within the pharmaceutical industry. Computational exploitation of the increasing volumes of data generated during all phases of drug discovery is enabling key challenges of the process to be addressed. Here, we highlight some of the areas in which bioinformatics resources and methods are being developed to support the drug discovery pipeline. These include the creation of large data warehouses, bioinformatics algorithms to analyse ‘big data’ that identify novel drug targets and/or biomarkers, programs to assess the tractability of targets, and prediction of repositioning opportunities that use licensed drugs to treat additional indications.
Highlights
Recent estimates suggest that it takes approximately 13 years and a ‘capitalized’ cost of approximately US$1.8 billion to bring a new drug to the market [1]
In 2015, a large Genome-wide association studies (GWAS) by the Malaria Genomic Epidemiology Network found that approximately 33% protection against severe malaria is provided by genetic variants at a novel genetic locus, which is either in or close to genes encoding the production of glycophorins [66]
The proteins appear indistinguishable to the eye, the analysis identifies that BRD1 has a bromodomain-binding pocket more likely to bind a small molecule
Summary
Bioinformatics approaches are becoming ever more essential in translational drug discovery both in academia and within the pharmaceutical industry. Computational exploitation of the increasing volumes of data generated during all phases of drug discovery is enabling key challenges of the process to be addressed. We highlight some of the areas in which bioinformatics resources and methods are being developed to support the drug discovery pipeline. These include the creation of large data warehouses, bioinformatics algorithms to analyse ‘big data’ that identify novel drug targets and/or biomarkers, programs to assess the tractability of targets, and prediction of repositioning opportunities that use licensed drugs to treat additional indications
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