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Challenges in Computer-Aided Drug Discovery and development

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Medicilon offers fully integrated pharmaceutical services for the global scientific community. We focus on providing an exceptional client-centered experience and advancing the drug discovery process.

Email: Marketing@medicilon.com.cn               Website: www.medicilon.com

Drug discovery and development is an expensive and challenging process. Over the past ten years the cost of drug research and development has approximately doubled while the average number of Food and Drug Administration (FDA) approved drugs is largely unchanged. (Paul, Mytelka et al. ; Service 2004; Aronovitz 2006) The high cost of drug discovery has led to the so called “Block Buster” syndrome in which corporate drug discovery efforts focus on finding treatments for diseases with high profit values such as those that require continuing treatment or that have a high occurrence in the developed world. (Mrazek and Mossialos 2003; Service 2004; Stirner 2008) The effect of the “Block Buster” syndrome is that many serious diseases in the developing world are not targeted for drug development. (Mrazek and Mossialos 2003; Service 2004; Stirner 2008) As an alternative to traditional drug discovery methods, computational drug discovery tools provide a potentially lower cost approach to drug discovery. Lower drug discovery costs allow academic and charitable organization to perform drug discovery projects focused on diseases with a greater humanitarian burden than profitability, such as malaria, leishmania, and dengue fever. Additionally, lower drug discovery costs allow for increased corporate efficiency in drug development thereby increasing treatment options.

A good computational drug discovery and development tool should identify bioactive compounds from a chemical library, in a variety of different systems, in a timely and efficient manner. The goal of this work is to improve computational drug discovery tools by improving the success rate in docking based virtual screening, focusing on the prediction of binding. Improved success rates reduces the total number of compounds tested at the bench top, thus decreasing drug discovery cost and increasing the probability of lead generation and future drug development.

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