chemical modelling The company’s StarDrop


platform takes predictions for virtual compounds and assesses them against the profile of properties that a particular project is trying to achieve, explains Segall, and then rapidly focuses the time, effort and intellectual input of the chemist on those areas of chemistry that are most likely to resolve whatever problems have been identified. ‘It’s a brainstorming tool to propose lots of new ideas – far more than a typical chemist can hold in their head at any one time – about how they might change a molecule in order to achieve their goals. If you have 10,000 potential molecules, you can put them through 10 predictive models and generate 100,000 data points,’ he continues.


Data, data, everywhere Molecules can be combined in many different ways – couple that with computers that offer the ability to test out theories without the need to run tens of thousands of physical experiments, and the data sets being generated become increasingly vast. Visualisation plays a major part in understanding what is being presented. Segall believes that, when faced with a spreadsheet featuring so many data points, people are simply going to ask


also phase-two studies where the knowledge gained can be fed back into the organisation later on.’ The management of this information can mean the difference between a successful laboratory study, and one that fails to reap adequate results. So modelling tools need not only to be capable of delivering data quickly, but must do so in a clear, precise and user-friendly manner. One answer that has come


with the increase in computing power is the ability to have representations presented in three dimensions. This enables quick visualisation and provides the user with simple ways of controlling and mining the data, as Ivarsson explains. When dealing with large data sets it can often be difficult to discover meaningful patterns, but by having the data presented in 3D format that can be rotated and explored, researchers stand a better chance of interpreting results. Analysis is best handled by the individuals who understand the area best, as Ivarsson says: ‘The main message is that by allowing chemists, biologists and medical researchers to analyse the data themselves, they will generate new and valid results, and ensure that obvious things in the data aren’t being missed.’ A visual context representation


YOU CAN PUT THEM THROUGH 10 PREDICTIVE MODELS AND GENERATE 100,000 DATA POINTS


what good that information is to them, and how they can make a decision to target the best chemistry for their project. Carl-Johan Ivarsson,


president of Qlucore, agrees that a drawback to the advance in chemical modelling has been the growth of the data. ‘New techniques and methods generate so much data that we simply have to have more efficient tools in order to understand and manage it,’ he comments. ‘There is a huge investment in this data and it’s not only the data generated in specific experiments, but


www.scientific-computing.com IF YOU HAVE 10,000 POTENTIAL MOLECULES,


also makes it easier to understand the data without the need for being a statistical expert. Whatever their specialty,


researchers will look to software that has a certain level of ‘plug and play’ functionality, rather than a package that is overly complicated to use. The second concern is that as the volume of data increases, the chances of important details being overlooked can also grow. Good results that fulfil the criteria of the study is the primary goal, but Ivarsson points out that researchers tend to miss


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