laboratory informatics


or analyses “match pairs” or “activity cliffs” to understand the structure activity relationships in a chemical series. Most of these methods produce a great big table of numbers. Poring over that table and trying to understand what they are telling you about the structural modifications, or impact on properties and so on, is very challenging.’ ‘We pioneered a visualisation tool we call


‘Card View – and, as the name suggests, it represents each compound on a card that is arranged on an infinite desktop that provides a very nice way to show the relationships between compounds,’ said Segall. He explained that, in many cases, a chemist


is interested not in a single compound but a series that they can take forward. ‘Tis algorithm tries to group compounds that represent a series and the card view represents each of these series or clusters of compounds as a stack of cards. ‘Te output of these complex algorithms


Drug Discovery (CDD), Te Edge and Certara. Teir platforms are being used for ELN or storage of databases to gather, reduce and store data for drug discovery projects. We work with them to ensure that our soſtware works seamlessly with theirs,’ said Segall. To maximise this integration, Optibrium


aims to provide integration with their soſtware and that of partner’s organisations, removing the need to manually correct data formats, data exportation and formatting. ‘Tat is a big part of our philosophy as well being very agnostic to where people will get their data – we want to make that process as easy as possible,’ said Segall.


Visualisation is not enough Managing all this data requires sophisticated data visualisation tools that can more intuitively display complicated data that is produced or collated throughout drug development projects. While many companies have their way of visualising data, Optibrium has decided to employ a card system that allows users to quickly group compounds with similar properties. However, Segall stressed that it is not just the visualisation tools themselves but the combination of visualisation in tandem with support for decision making and data analysis that creates the most benefit for users. ‘If you think about five parameters you


might be interested in, you could have a three-dimensional scatter plot: X, Y, Z. You end up with these incredibly complex 3D plots that look really great, but frankly, when


www.scientific-computing.com l @scwmagazine


THE GOAL THERE IS TO PRIORITISE COMPOUNDS AND TO UNDERSTAND THE STRUCTURE ACTIVITY RELATIONSHIPS THAT ARE DRIVING ACTIVITY AND OTHER PROPERTIES WITHIN THE CHEMISTRY


you do this with real data it is very hard to make a decision – even before you take the uncertainty into account,’ said Segall. Tis is further complicated by the level of


expertise of the user, as increasingly these projects include non-computational experts that may have little experience with this kind of data analysis. ‘Oſten it is a medicinal chemist or biologist making decisions about this data and using the tools,’ said Segall. ‘Having some very complex soſtware windows buttons or even asking scientists to work from the command line is just not good enough these days.’ Tis reality requires that soſtware


developers streamline soſtware for non- domain experts that want to access the data but do not necessarily have the programming skills or expert chemical knowledge that a computational chemist would possess. ‘Very oſten you may run a complex


algorithm that clusters compounds together


can be represented in this environment more visually, so key patterns just jump out at you. Tis could be a small change in structure that drives a big change in activity – clearly, this is something scientists need to understand in order to be able to take the next step in designing new compounds,’ said Segall. Optibrium has designed its soſtware to


be as easy to use a possible, so data can be interpreted as intuitively as possible. ‘Tis allows you to apply these methods, and it allows users to understand what those methods are telling you about your data very quickly. Tis is absolutely key to the effective use of these technologies,’ said Segall. However, it is not yet time to step back and


let the computer take over. ‘Te problem with these algorithms is they never completely agree with a chemist’s view of what a chemical series might be,’ said Segall. A crucial point here is that soſtware must


work with the expert; it may be beneficial to use a clustering algorithm to help define a particular series but it is important that an expert can still use their own experience to fine-tune soſtware predictions and further refine the overall results. ‘Tere are always artefacts of the algorithm


that do not agree with a chemist’s eye’, stated Segall. ‘In our system you can see the output but then you see two clusters that are very similar, in the chemist’s opinion part of the same series, you can simply pick it up and drop one on top of the other and the soſtware will show you the updated analysis.’ In an increasingly competitive and complex


industry, it is this synergy between expert and machine that will be crucial to future drug discovery projects. l


APRIL/MAY 2017 23


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