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The Rise of Data Sciences in Drug Discovery

Drug discovery and development are experiencing a profound transformation for many reasons, including the changing economic environment in which it operates. The main reason of this silent revolution though still lies at the discovery step. The pharma industry has the obligation to move away from the semi-empirical, simplistic and wasteful way of developing new candidate therapies particularly at a time when so much data is both either already available or far less expensive to produce. Understandably, the management of the logistics and infrastructure required to extract relevant biological information from such large sets of various sources is far from trivial.

Drug companies are faced with the challenge of allocating the resources closer to their core expertise while still taking advantage of this wealth of data. While there is no time to waste, these organizations can benefit from the skills and creativity of start-ups founded on the premise of implementing cost-effective ways of mining data. The confluence of affordable computing, an eco-system of open-source technologies and rapid progress in big data analytics allowed the emergence of new outfits dedicated to address the needs of drug companies.

Companies like Arrayo, OnRamp Bioinformatics, Inc. and SolveBio are taking the lead in decisively building on their capacity to take on the most challenging data mining projects for organizations in the life sciences. In such a recently budding market, one challenge for the growth of these companies is to connect with the right partner from the traditional drug R&D industry. Conversely, the latter are searching for the right source to procure their growing needs in data sciences., whose primary mission is to promote faster and cost-effective drug discovery, bridges the gap. Relying on its years of expertise in operating and growing its life sciences research marketplace, it becomes an enabler of this transformation of R&D. Earlier this year, a large pharma laboratory faced challenges in integrating critical data sources to its internal data store, so it turned to to procure its advanced and specialized data sciences requirements. It was able to find a bioinformatics supplier to deliver complex data analysis, in silico studies and database development. This type of collaboration will undoubtedly be the difference maker in successful drug discovery in the coming years.