biomolecules on a computer screen, atom by atom, has become an essential tool for drug discovery. Even though the required approximations lead to a sort of ‘reality gap’ (meaning that in silico predictions hardly ever match the real wet lab experiments exactly), none of the major pharmaceutical companies could survive without molecular modelling,’ he said.
Throughout the process Computational techniques are used at various stages of drug discovery. As Krieger noted, the first step is in identifying the 3D structure of a suitable drug-target protein. ‘If the structure of a highly-homologous protein is known, then the target structure can be predicted in silico using “homology modelling”; otherwise one needs to solve it via X-ray crystallography,’ he said. Computational tools also help in the next
step of docking ligands with the target. YASARA, for example, includes a customised version of the open-source docking soſtware AutoDock, developed at Scripps Research Institute. Setup and analysis steps are automatic in this tool, including ‘oſten missed ones, like optimising the receptor hydrogen bonding network, or generating an ensemble of receptor structures to consider receptor flexibility,’ according to Krieger. Other ways that such tools can help
include refining receptor-ligand complexes, performing ‘induced fit docking’ and allowing users to modify the drugs interactively and recalculate binding
FieldView provides a more informative view of how molecules are likely to behave in biological system
core chemistry to another while retaining the same biological activity.’ Molecular modelling tools are primarily
used by computational chemists but they are also used in different ways by medicinal chemists. As Scoffin explained: ‘You could run a model at the start that would generate ideas for a month or so and then feed back experimental results into the process. In that case the application is not used every day, but maybe once or twice a month. At the other extreme, the application might be
ONE SHOULD BE CAREFUL ABOUT USING
COMPUTATIONAL TOOLS AS A BLACK BOX. YOU NEED TO UNDERSTAND HOW TO USE THEM
energies. In addition, some tools enable users to use database knowledge about common synthetic pathways to ensure that the ligands designed by molecular modelling can be synthesised readily. Another interesting area, according to
Robert Scoffin of Cresset BioMolecular Discovery, is to allow people to scaffold hop with their compounds. Tis means generating a set of alternative compounds with different underlying structures but predicted to have the same biological activity. ‘Oſten you want a backup – a completely different chemical series in case you discover an inherent limitation with the chemical series you have been studying. Having one or more backups saves you having to go back to the start,’ he explained. ‘Our tools allow people to jump from one
46 SCIENTIFIC COMPUTING WORLD
used every day to see why one compound is active and another is not, in order to enable chemists to understand what they are seeing on the bench.’ Tis reveals an interesting issue with
molecular modelling tools: the products themselves are the starting point, but the ways that they are used vary depending on the companies using them. ‘We have customers who mix the models in quite interesting ways,’ observed Scoffin. He gave the example of one company that does high- throughput screening in the traditional way, but then uses the Cresset soſtware to try and control for false-negatives, to thereby enrich the hit rate from the confirmation screening. ‘Te error bars on single-shot wet
screening are pretty high and there are many possible factors for failure, such as a
compound degrading or interacting badly with some component of the assay. Tose compounds could be inherently positive. If you miss compounds as false negatives then you’re missing both information and leads,’ he said. In contrast, there are also situations when
wet screening reveals that a compound is promising but this was not identified with in silico screening. ‘Tese compounds are interesting in themselves,’ noted Scoffin. ‘Perhaps they bind in different ways.’
Intelligent use In the pharmaceutical industry it is important to look at the big picture, believes Sander Nabuurs of Lead Pharma. ‘We try to cover the whole spectrum of computational techniques and aim to take a look broader than other companies do early on in the process at physicochemical properties such as solubility,’ he said. ‘A trend as far as we are concerned is to
try to identify potential side effects early on to decrease the chances of a drug failing in clinical trials. We try to do molecular profiling at the early stages, looking at, for example, compound-related gene expression effects on unrelated or unwanted genes. If we do this at an early level we can steer away from undesired effects,’ added Nabuurs. Such an approach requires detailed
interaction between computational tools and the chemists using them. ‘We get the best results if we integrate experimental results with molecular modelling techniques. We gather as many experimental results
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