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Computational Drug Discovery and Designing

In general, Drug discovery and design is a process by which new candidate drugs are discovered. This process can be described as the identification and validation of a disease target; discovery and development of a chemical compound to interact with that target. Traditional drug discovery follows well-established pharmacology and chemistry-based drug designing approaches and faces various difficulties in finding new drugs. So, It's lengthy,

expensive, difficult, and inefficient process and for the development of new drug requires a technological expertise, human resources and huge investment.

Because of the dramatic increase in the availability of biological macromolecules and small molecule information, the applicability of computational drug discovery has been extended and broadly applied to nearly every stage in the drug discovery and development workflow, including target identification and validation, lead discovery and optimization. The emergence of bioinformatics in last 25 years became successful in a short period of time with low cost that uses computational approaches to discover, develope and analyze drugs and similar biologically active molecules.

Over the past decades, Computational Drug Discovery methods such as molecular docking, pharmacophore modeling and mapping, De novo design and sequence-based virtual screening have been greatly improved. The core components of modern drug discovery process include molecular modelling, pattern discovery, chemical structure prediction. Each step of drug discovery and design Viz target identification, target validation, lead selection, lead optimization and other different Computational methods.

Bioinformatic analysis can not only accelerate drug target identification and drug candidate screening and refinement, but also facilitate characterization of side effects and predict drug resistance. High-throughput data such as genomics, epigenetics, genome architecture, cistromics, transcriptomics, proteomics, and ribosome profiling data have all made significant contribution to mechanism-based drug discovery and drug repurposing. 

Vaccines are the pharmaceutical products that offer the best cost‐benefit ratio in the prevention or treatment of diseases. Vaccine development and production are costly and it takes years for this to be accomplished. Several Bioinformatics methods and approaches have been applied to reduce the times and costs of vaccine development, mainly focusing on the selection of appropriate antigens or antigenic structures, carriers, and adjuvants. Bioinformatics plays an important role in vaccine design and development.

Reverse vaccinology, immunoinformatics, and structural vaccinology are helpful in the design and development of specific vaccines against infectious diseases caused by bacteria, viruses, and parasites. These include some emerging or re‐emerging infectious diseases, as well as therapeutic vaccines to fight cancer, allergies which have been facilitated and improved by using bioinformatics tools under the incorporation of bioinformatics strategies. 

BioCode is going to conduct an online "Computational Drug Discovery and design" ten days training workshop that will cover the basic of drug designing and vaccine designing to the computational methods and techniques. Register here.

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