Discovery Decisions – Collaborating in Data Management
This paper appeared in the Winter 2018 edition of EBR.
From initial hit to development candidate, drug discovery is an iterative process. At each stage, the latest results are reviewed in the context of all the project data, to choose compounds for progression or identify key structure-activity relationships (SAR) that guide the design of new compounds for synthesis. These activities are usually supported by software for data analysis, visualisation and predictive modelling.
However, obstacles remain to the effective use of such software: different applications are often used for each function; scientists may use one to retrieve data from their database, another to visualise their results and a third for predicting properties of new compounds they are considering for synthesis. Just moving and reformatting data for each software application can be time consuming and error-prone. Furthermore, scientists need to learn multiple user interfaces, each with a different ‘look and feel’. Some software, for example visualising protein-ligand interactions in 3 dimensions, may be available only to expert computational chemists, leading to delays while waiting for an expert to be available and the potential for important details to be ‘lost in translation’.
In this article, we will discuss the requirements for a platform to overcome these challenges and support effective decision-making from data to design. Bringing together all of the information revealed by different analyses may reveal new insights and will foster collaboration between different disciplines, leading to more rapid progress and higher quality compounds.
You can download this presentation as a PDF.
This article is taken from European Biopharmaceutical Review January 2018, pages 66-69. © Samedan Ltd