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Drug discovery with data

In the second part of our series on computational chemistry,

Siân Harris looks at the role of modelling and high-quality data in the pharmaceutical industry


n recent years the pharmaceutical industry has relied more and more on computational modelling to aid drug discovery and development.

Computer models are used in a number of

ways to tackle more challenging problems, explained Mark Mackey, CSO of Cresset. ‘For pharmaceutical and biotechnology companies, drug discovery is getting harder and harder: the easy targets are mined out; the regulatory and safety environment is increasingly tough; and the more biology we know, the more things can kill a compound before it even gets to the clinic,’ he said. ‘In this environment, efficiency in getting

compounds into the drug discovery pipeline is critical. If you can make compounds “smarter”, then you need to make fewer to get one to the clinic.’ Such needs have driven developments in modelling algorithms. ‘Understanding why

compounds have the biological activities that they do is difficult, and for too long the prevailing view on activity was fundamentally 2D,’ continued Mackey. ‘Chemists viewed compounds in terms of how they were put together, rather than what their electrostatic potentials, shapes, and other 3D properties were.’ He said that Cresset’s tools help medicinal

and computational chemists view the overall picture, from the point of view of their intended protein targets, of what molecules look like. ‘Te extra insights that this view brings can be invaluable when looking at a series of compounds and trying to decide what to make next,’ he explained.‘We’re improving our core molecular alignment and scoring algorithms, which lets us determine more accurately when two molecules have similar 3D properties and hence are likely to have similar biological activities.’

Te company’s recently released Activity

Miner soſtware enables modellers to analyse a dataset of molecules and their biological activities, to find pairs of molecules that are very similar to each other yet have quite different biological activities. ‘Tese pairs have a high information content; they give information about what a protein likes or doesn’t like, when it binds to these molecules,’ he explained. Cresset is also working on an extension

to enable analysis of more than one set of biological activities simultaneously. In addition, the company is looking at extending its similarity algorithms from ligand to



proteins. ‘If this works, we will be able to compare and cluster the available human protein structures on their 3D electrostatic and shape properties, which would be a huge boost to industrial drug discovery,’ explained Mackey. ‘It would allow computational chemists to jump in at the very start of a drug discovery project and say “OK, we want a compound that’s active against protein X, but protein Y is very similar to protein X so we need to keep an eye on that.”’ He also sees a shiſt from ligand-based drug

Activity Miner’s innovative activity view allows you to explore the SAR around one compound. 28 SCIENTIFIC COMPUTING WORLD

design (LBDD), where modellers look at the small molecules, analyse them, compare them, and extract as much information as possible out of them to guide a medicinal

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