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molecular modelling: drug development

Speed, integration and interactivity

Siân Harris reports on the important role of

computing in drug discovery P

harmaceuticals play a crucial role in managing health problems of all types and there is a huge impetus to find and improve drugs. But pharmaceutical

development is neither cheap nor simple. As Lea Tøgersen, senior bioinformatics scientist at CLC bio in Denmark, observes: ‘Te cost of bringing a new drug to market is huge. Tis is, in part, due to money and time spent taking a potential drug to the clinical trial phase, only to see it fail at this late stage. Te challenge for the pharmaceutical industry is to speed up the process of selecting a good candidate drug, and to secure a higher success rate when a drug reaches the clinical trials.’ Te traditional image of drug discovery

involves chemists, biologists, pharmacologists and other ‘wet’ scientists experimenting in labs. However there is another increasingly crucial piece to the puzzle: the role played by computational scientists throughout the process in helping to address this challenge. ‘Modelling today is an integrated part of the

drug discovery process. It is used for virtual screenings of compound libraries, but also for hypothesis testing and gaining an understanding of why one compound binds strongly to a target and another does not. Docking simulations are a cheap way to explore chemical space without doing wet-lab experiments, and help decide which compounds seem worthwhile to synthesise,’ continues Tøgersen. Of course, this is not a simple task, she says:

‘Te challenges faced by researchers using small-molecule docking as a tool for drug design have been the same for many years now; namely accommodating the flexibility of the protein target in the model and acquiring a binding affinity prediction that allows not just a proper ranking of small-molecule binders, but also a reasonable estimate of the binding strength.’ CLC bio (including the Molegro business,

which was bought in September 2012) offers Molegro Virtual Docker, promising high- speed and high-accuracy docking for virtual screening of small-molecule libraries. It


CLC Bio’s Molegro Virtual Docker provides visualisation tools for analysing protein/ligand interactions

also has a graphical user interface where the interactions between the target protein and the potential drug candidates can be explored. To describe target protein flexibility, induced-fit docking is offered, allowing for rearrangements of side-chains in the binding pocket to best fit the docked molecule. Visualisation tools for analysing protein-ligand interactions and identifying hot-spots for favourable types of interactions are also provided. Many challenges relating to integration with other tools and processes are being tackled by CLC bio as part of the integration of Molegro with the company’s other services and products.


Tøgersen says a common request from

customers is support for an interactive flow from sketching small molecules, docking them against a protein target and then manipulating the molecules inside the protein binding pocket. Tey also want a real-time response from the soſtware relating to the implications the changes have on binding quality. She observes that customers are always looking for a better prediction of binding affinity too. ‘Te dream would be to find the perfect drug from a virtual screening of all synthesisable molecules.

However, there is a limitation as to the level of modelling precision, and a modelling approach such as docking will have its built-in limitations with respect to accuracy,’ she says.

An ongoing process Shi-Yi Liu, SVP of marketing at Schrödinger, notes that refining and improving models and their application to drug discovery needs to be a continual process. ‘We are devoted to ongoing research and are constantly engaged in understanding it from a physics perspective, trying to improve the accuracy of the process,’ she explains. ‘It’s ongoing – it’s still theoretical and we are always making approximations.’ Such models and approximations can be

assisted by experimental data. Liu says: ‘We use any and all data we can get our hands on, especially new data. We systematically improve our tools, using experimental data to feed in to models, and we have a large group of scientists working on methods development.’ Schrödinger is fortunate in this regard because, as well as developing soſtware, the company provides services to pharmaceutical groups. ‘Not everyone is willing to share data, but the services side of our business enables us to work on proprietary data sets. Tese are useful for testing and refining the simulation tools,’ adds Liu. ‘If there are differences between the experimental results and the models, this is like an alert to re-evaluate the models. Tese datasets are also good training sets when we are refining models.’ Modelling and simulation tools are used in

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