Optibrium Expands StarDrop Availability with Apple Mac Compatibility Delivering Enhanced Functionality to Guide Decisions in All Stages of Drug Discovery
CAMBRIDGE, UK, October 18, 2011 – Optibrium, a provider of software solutions for drug discovery, today launches StarDrop 5.1, an upgraded version of its innovative support tool which helps drug discovery scientists to guide key decisions in drug discovery and quickly achieve successful project outcomes. As a result of this upgrade, StarDrop’s unique capabilities to guide the design and selection of high quality, novel compounds are now available on the Apple Mac, a reflection of the increasing popularity of Macs within the industry. The revolutionary StarDrop 5.1 is a powerful yet user friendly software package which combines predictive capabilities with intuitive approaches to quickly and confidently target compounds with a good balance of properties, thereby reducing wasted effort and speeding progress to identify effective lead and candidate drugs. StarDrop’s unique approach to ‘multi-parameter optimisation’ explicitly accounts for the uncertainty in drug discovery data, whether due to experimental variability or predictive error, to provide scientists with a rigorous, objective analysis on which to make rational decisions. As well as compatibility with the Apple Mac, StarDrop 5.1 brings a number of significant enhancements to existing features of StarDrop. For example, there is a new method for creating ‘chemical space’ visualizations based on the latest machine learning algorithm for ‘visual clustering’, that helps to easily explore the diversity of a project’s chemistry and identify ‘hot spots’ of high quality chemistry for further investigation. There are also improvements to StarDrop’s plug-in modules: The Auto-Modeller™ sees the addition of the ‘Random Forests’ technique to its extensive repertoire for building predictive models tailored to a project’s chemistry and data. The addition of this new method has also led to improved QSAR models in the ADME QSAR module, which provides predictions of key ADME properties. StarDrop’s P450 metabolism module has been upgraded to provide improved prediction of regioselectivity and lability of metabolism by Cytochrome P450 enzymes, specifically around N-oxidation pathways. Finally, in response to user feedback on the new Nova™ module, further advances allow the generation of novel, relevant compounds ideas, prioritised against a project’s required property profile, starting from multiple initial structures and filtered according to user-specified rules to avoid unwanted substructures. Dr. Matthew Segall, CEO of Optibrium, explains “These latest developments within StarDrop 5.1 demonstrate Optibrium’s commitment to deliver leading-edge technology while simultaneously responding to user feedback in order to ensure that it is accessible in the most user-friendly way. We work with drug discovery scientists, project leaders and R&D directors to provide tools that support confident decision-making to focus resources and improve efficiency”. For more information about StarDrop 5.1 and Optibrium’s software solutions please visit www.optibrium.com, alternatively please call +44 (0) 1223 815 900 or email email@example.com
About Optibrium Ltd Optibrium (www.optibrium.com) is dedicated to providing software to guide decisions involving complex, uncertain data in an intuitive way. Based in Cambridge, UK, Optibrium has a global customer base ranging from top-ten pharmaceutical companies to small biotechs and academic groups. Our mission is to continue to develop new technologies that will optimise project strategy, reduce wasted molecules and experiments, shorten timelines and improve the quality of candidate compounds for our clients. Optibrium’s primary product, StarDrop, is focused on the drug discovery industry, helping guide scientists to make decisions in the design and selection of high quality drug candidates. Visit the online community at http://www.optibrium.com/community/ for further discussions on improving the productivity of drug discovery.