Bioinformatics: A Boon for Drug Discovery

Bioinformatics is computational biology, where knowledge of computer science is used to explore insights of biology that involves gene sequencing.

It has led to the formation of a database of nucleic acid and protein sequences along with their structures. Bioinformatics has led to the development of new branch Computer-aided drug design (CADD).

Drug discovery involves many sequential steps and it is altogether very tedious. So, there was a necessity to find the shortest approach to drug discovery.

The drug should be efficient and it should produce minimal side effects, it should be highly targeted towards the proposed effect. It should also possess fewer chances of drug resistance.

Conventional drug discovery involves steps like identification of disease, isolating protein involved in disease, then finding appropriate drug against the disease, followed by lead optimization and lastly preclinical and clinical studies which involves a minimum of ten years duration.

In CADD involving bioinformatics, there is firstly identification of drug targets for a particular design to define drug action. Then against those defined targets or proteins, virtual screening is done to predict activity. High throughput screening (HTS) aids in finding new leads. Quantitative structure-activity relationship (QSAR) helps to improve the structure of the lead compound with some modifications. CADD related software names are discovery studio, lead IT, open eye, Schrodinger which involves many modules like ligand fitting, QSAR studies etc. Further, with the application of computer software, animal testing can be replaced by computer models. Softwares like TOPKAT, Bioprint, Drug Matrix, C2-ADME, Gastroplus, CLOG P and Ab Slov are used to predict bioavailability and bioactivity of drugs beforehand their preclinical and clinical studies.

A flowchart depicting the Bioinformatics drug discovery process is shown in the below figure.

Bioinformatics is boon for drug discovery as it has decreased time span for drug discovery. It has also made the drug discovery process less costly with decreased risk of side effects and failures.

The whole process has become automated, which have reduced the errors due to human interference.

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