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2D structures of structurally diverse bioisosteres both active at PDE3, cAMP (the natural substrate) and SKF93741, a PDE3 inhibitor. The field patterns of the compounds reveal that they are biologically similar and share the same activity.

chemical developments in the lab mean there are always new compounds to screen. ‘Te industry reached a point where the statisticians couldn’t keep up with all the products,’ he observed. Accelrys therefore worked with Glaxo

Smith Kline to develop faster tools for modelling. He described the result as user- friendly and powerful. Te aim is ‘helping the expert modeller to be more productive in their work,’ he continued. ‘If new data comes in, you could just save

the steps you have done and rerun with the new data. It has not taken the statistician out of the equation but allowed them to work more meaningfully with their colleagues,’ he explained. ‘Tey can work with local data and get more precise models and more sophisticated

Standardising data

High-quality data is an important requirement for chemical modelling. But it is not just the accuracy of the data that is important. To be able to draw meaningful conclusions from a range of datasets there is a need for good structure and appropriate integration.

‘Integration of different data

sources is not a solved problem. We still have data sitting in different repositories but what’s better understood is how to use those data sources better,’ observed Adrian

Stevens of Accelrys. ‘There is always a danger of disconnect if you don’t know the data source. You should always treat data as discrete groups but look for general trends. Until we get to the stage where everyone works to the same standards, we are going to have this problem.’ There are a number of initiatives to attempt to bring standardisation to the way that data is organised. One such initiative is OpenPHACTS, a cross-industry project to deliver an online platform with a set of


integrated, publicly available, pharmacological data. The initiative promises that: ‘Throughout the project, a series of recommendations will be developed in conjunction with the community, building on open standards, to ensure wide applicability of the approaches used for integration of data.’

‘OpenPHACTS is trying to curate in a standard way,’ said Stevens. ‘With OpenPHACTS, the potential for impact is still being learnt. It includes data from a wide range of

sources and gives you the chance of ask questions such as: “what other compounds might I hit similar to my target?” and “how do I get to druggable targets?”’

‘OpenPHACTS is helping us, as much as we’re helping them,’ noted Meeuwis van Arkel of Elsevier. ‘It is defining standards to make the sharing of data more easy. We always tend to participate in these initiatives; it makes our data more accessible.’

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visualisation of ligand docking,’ he said. ‘Fiſteen years ago, people wrote on paper and listened to modellers in meetings. Now modellers are integrated in the teams.

Relying on data Such developments in modelling approaches and applications all rely on another important factor: the availability and quality of chemical data. ‘Computational chemists today wouldn’t

dream of working on a project before looking at the available data,’ noted Stevens, who added that ‘if there are different standards of doing screening it hard to know if things are equivalent.’ Good data is something that Meeuwis van

Arkel is focused on. He is VP for product development at Elsevier Information Systems

GmbH. ‘Modelling is an increasingly important tool and the modeller wants huge quantity of data in terms of depth and breadth, including patents,’ he observed. Elsevier’s Reaxys database indexes relevant

organic, inorganic and organometallic data from across the industry and, according to van Arkel, captures on average 400 fields of information for each compound, including properties such as melting point, spectral data, biological activity and literature citations. He said that the database currently contains

24 million different compounds and is focused on life sciences and drug discovery. Such ‘real-world’ data feeds into

pharmaceutical research models. ‘Te data on which they model needs to be very highly- structured, uniform and high quality. All


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