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Computational modelling helps chemists predict how potential drugs would interact with proteins

chemistry programme, towards structure- based drug design (SBDD). ‘Five or 10 years ago, vast swathes of

drug discovery (ion channels, GPCRs, etc) were exclusively the province of LBDD. Te increasing availability of high-quality X-ray data for many of these targets has given us all new insights into how these protein families work and how our compounds are behaving when they bind to them, and we need to be able to fold that data into our existing ligand- based expertise,’ he explained. Adrian Stevens, senior manager, predictive sciences marketing (life sciences) for Accelrys, l

has also observed changes in the approaches and applications of computational modelling for more complicated drug discovery. One such trend is the increased interest in polypharmacology, the treatment of diseases by modulating more than one target. He also said that as the same compounds

are screened again and again over time, it is possible to build up more understanding about drug viability. ‘You can start designing a problem out before it gets to clinical trials,’ he explained, adding that modelling also enables researchers to try to new ways to repurpose research and new patent opportunities.


Timing issues Timing is also important, he observed. He noted how the length of time to run simulations was a bottleneck for pharmaceutical companies. In the past, he explained, ‘modelling

required someone with deep statistical knowledge and also deep understanding of the product.’ He noted that two statisticians might have taken two weeks to do a model for each project but that a big pharmaceutical company could be running 150 projects at once globally, creating a huge demand for the statisticians’ skills and time. Meanwhile

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