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a number of ways depending on the stage of the process and the needs of the client. ‘It is most highly used in virtual screening, but lead optimisation is also of key importance,’ says Liu. ‘We’ve also heard that, once customers have found a candidate compound, they find it useful to use the soſtware to find a back-up.’ Such a range of applications require different

ways of using the tools, particularly with regard to the trade-off between accuracy and speed of response. ‘As a company, we offer a range of tools across the speed-versus-accuracy spectrum. Customers need to exercise their judgement,’ says Liu. ‘At the start there are many millions of ideas. If you are constrained by the number of licences, or number of computer cores, then you will err on the side of speed.’ However, the issues are different as researchers

progress a drug-discovery project. ‘As you go through the process, there are fewer compounds you are looking at, so you can throw more computing power at each,’ says Liu. Working in the cloud is something Liu

recommends: ‘We provide our licences to be run on the cloud, working with Cycle Computing, but the option only has a minority take-up at the moment, which is surprising.’ She attributes the lack of cloud take-up to

security concerns, but notes that even banks use the cloud. ‘With pharmaceutical companies it is probably more of a cultural trend and we expect that to change. My personal view is that the cloud is just as secure as any cluster,’ she argues. Aside from the speed versus accuracy trade-

off, Liu says that the company hears more and more that researchers are becoming happier with the predictive power of modelling soſtware. However, they are overwhelmed with the sheer volume of data in different silos. ‘Te problem is the data avalanche – not

just from computational models, but also from assays, crystallographic data and other sources. How do you make informed decisions, rather than just looking at the latest bit of data to land on your desk?’ she says. ‘People making decisions need real-time access to all the data – and that’s something that, as a field, we’ve not done well.’ One of the company’s goals

now is to bring all this data together. ‘At Schrödinger we have been working on this over the past few years and are in the prototyping and designing phase. It is not solved yet, but we are hiring really bright people and in a year’s time we will probably have a very exciting update.’

Outside of major, established companies there is also plenty of innovative research that aims to help drug discovery in the future. Victor Guallar and colleagues at the Barcelona Supercomputing Center in Spain have developed a new technique for protein dynamics and interactions with drugs. Teir approach is based on Monte Carlo techniques rather than traditional Newtonian methods. Tis, said Guallar, makes the approach significantly faster – an attribute that is a key part of commercial plans that the group has for its tool: ‘We envision that, in the future, we will combine it with multimedia functionality and enable on-the-fly interacting with results – on a device like an iPad, perhaps. Seeing real-time changes and mutations could be really powerful.’

Interactive simulations Being able to watch simulations and interact with them would mean that researchers could adapt conditions as a simulation progresses rather than needing to wait for the output at the end. ‘I’d like to make it really interactive’ Guallar says. ‘You could start a simulation and see the movement of the ligand and protein in real time and then try things like moving ligands or changing the temperature.’ Today, he says, people send requests to the

server and leave it for a few hours or days. ‘I’d say about 50 per cent of simulations are wrong and need to be tweaked and done again.’ Te tool that the group is working on currently

takes around two to three minutes for every step, although these steps are bigger than those with Newtonian approaches, he adds. He predicts that a new CPU would reduce this to perhaps less than a minute but says that the group’s goal is to enable, maybe, 2,000 steps per minute and make it really interactive. ‘A simulation can be done in about three

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hours, but the next generation will do it in minutes, so you don’t have to leave it and come back,’ he says, adding ‘I think this will make a big difference but you need parallel computing and fast enough soſtware.’ Te tool is named PELE,

Barcelona Supercomputing Center

CLC bio

Cycle Computing

Nimbus Discovery


which stands for Protein Energy Landscape Exploration; Guallar notes that any similarity with a famous footballer is a coincidence and this won’t be criteria for naming future products. Te goal is to have a version of PELE that is commercially available to the pharmaceutical industry, although Guallar adds it will be distributed free of charge to universities and not-for-profit organisations.

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