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Informatics


fast becoming a core capability for these organisa- tions across the R&D disciplines, from discovery, validation, pre-clinical and translation trials, through to clinical trial design. The industry now recognises bioinformatics data science expertise as a competitive advantage that will allow businesses to gain maximum value from both their in-house and public data sources. This paradigm shift is long overdue. The revolu-


tion is based on carefully building a closer interac- tion between the scientists that make research deci- sions and the people who build their technological solutions. Bridging these fields brings the promise of change and leading-edge innovation throughout the sector over the coming years.


What does it mean to be truly data-driven? We are only just beginning to understand and appreciate the benefits that data management can provide for the pharmaceutical and biotechnology industries. The sector is on the verge of discovering new concepts that will allow organisations to steer and surf the flow of an emerging new type of dataset: a vast, all-consuming, constantly changing dataset that permeates deeply through an organisa- tion and beyond. Data management and machine learning, when


it is ‘baked’ into a data science platform from the outset and built from the ground up by life science experts, has the most realistic chance of rising to this challenge. This approach is not constrained by previous logical, structural (and even linguistic- emotional) constraints. Finding new ways to


understand and address data management chal- lenges, in partnership with the researchers who are working in a high-pressure drug development envi- ronment, can help to conquer these restrictions. Within life science organisations, data is regu-


larly siloed and unable to flow through depart- ments and groups. Repetitive analytical tasks lead to frustration and absorb time because they are labour-intensive, instead of being executed auto- matically. Traditionally, analytical pipelines for drug discovery are developed and executed using data to answer biologically pertinent questions. This is accomplished laboriously by hand, requir- ing huge resource investment and constant validity checks to ensure quality. There is a lack of feed- back and agility, and an inability to forecast progress. As a result, organisations can miss out on finding the drug candidates that present the best chance of commercial success. Most of these problems can be traced back to data analysis sys- tems that are simply not fit for purpose at the cur- rent time and present a major barrier in terms of long-term pipeline success. In a cutting-edge life sciences environment, it is


essential to create effective structures that align with the demands and challenges of research and devel- opment. Data must be structured effectively if machine learning and advanced analytics are to play a meaningful role in the drug discovery process. Diverse data sources and results from multiple


in-house experiments must be integrated and enriched with large external data repositories. For example, robust validation of hypotheses underpinning important R&D pipeline work


Figure 4 Advanced machine learning platforms provide structure and consistency across workstreams to enable data- driven decision-making


Drug Discovery World Summer 2018


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