Abstract
Data generation in pharmaceutical research has been industrialized without our capacity to manage, disseminate, analyze and base decisions upon these data keeping pace. Like most scientific disciplines, medicinal chemistry is becoming increasingly data intensive and dependent on our capacity to manage and exploit growing data resources. Appropriate data-intensive strategies are required to ensure most value can be gained from all new scientific endeavors by using information technology to improve experimental design, data management, data analysis and communication. Fundamental is the need for drugdiscovery organizations to enable its drug hunters [1] to make decisions informed by the content of their internally generated data and their integration with external data [2]. Addressing these requirements is commonly referred to as the challenge of big data [3], referring to the analysis of datasets too large, unstructured, diverse or rapidly changing to be analyzed conventionally [2]. While synonymous with predictive (data) analytics, big data do not refer to any specific technology or solution, but rather a new scientific environment in which we all work. Disregarded by some as hype, there can be little doubt that our increasing data resources provide rich opportunity, but also numerous challenges such as:
Published Version (
Free)
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
