Cheminformatics
of this technology, which include: aiding the rigor- ous exploration of chemistry around early hits, to identify those hits most likely to yield high quality lead series; helping to find strategies to overcome problems with compound properties in lead opti- misation; and identifying patent busting opportu- nities by expanding the chemistry around existing development candidates or drugs to search for compounds with improved properties.
DDW
Figure 5
The chemical space of compounds generated from the initial lead that gave rise to Duloxetine. The points
corresponding to compounds are coloured by score, from
the lowest (0.29) in red to the highest (0.69) in yellow. The initial lead is shown as a dark blue diamond, Duloxetine as a light blue diamond. The top-
three scoring compounds are shown as green diamonds
Conclusion
In order to get the most out of predictive methods, they should be used to evaluate a wide range of ideas and prioritise the best for detailed considera- tion by an expert. This achieves the best combina- tion of experienced scientists, who can define the desired property profile and scrutinise the top- ranked compounds, with a computer’s capability to generate and objectively analyse large quantities of data.
However, while generating property data and prioritising ideas is inexpensive and quick, the bottleneck comes creating the ideas and entering them into the computer. Here too, computational approaches can help by encoding and applying the rules used by medicinal chemists to modify and optimise molecules. This, again, achieves a synergy between the chemists’ expertise – defin- ing the transformations to be applied and con- trolling their application – with the computer’s capability to store and apply more transforma- tions than an individual.
This approach may also be used as a tool to cap- ture and share knowledge or even as an education- al resource for less experienced scientists, as trans- formations may be shared and organised into groups tailored to specific objectives, such as improving metabolic stability or reducing plasma protein binding.
There are a wide range of potential applications 22
Dr Matthew Segall is Director and CEO of Optibrium Ltd. Matt has a Master of Science in computation from the University of Oxford and a PhD in theoretical physics from the University of Cambridge. As Associate Director at Camitro (UK), ArQule Inc and then Inpharmatica, he led a team developing predictive ADME models and state-of-the-art intuitive decision-support and visu- alisation tools for drug discovery. In January 2006, he became responsible for management of Inpharmatica’s ADME business, including experi- mental ADME services and the StarDrop software platform. Following acquisition of Inpharmatica, Matt became Senior Director responsible for BioFocus DPI’s ADMET division and in 2009 led a management buyout of the StarDrop business to found Optibrium.
Edmund Champness is Director and CSO of Optibrium Ltd. After graduating with a degree in Mathematics in 1995, Ed joined GlaxoWellcome working as part of a pioneering team building pre- dictive pharmaceutical tools. He developed the first graphical user-interfaces for working with pre- dictive models which were adopted globally within GlaxoWellcome. He was a core member of the team which established the UK operation of Camitro in 2001 and remained with that company (now operating within BioFocus DPI following merger and acquisition) until 2008. During this time he designed and built the StarDrop software and, in 2009, co-founded Optibrium.
Dr Chris Leeding is the Product Manager, respon- sible for the StarDrop software platform, at Optibrium. Chris received a PhD in Chemistry from King’s College London and has more than 10 years’ experience in software development roles. In 2006, Chris joined the team responsible for StarDrop and has played a key role in its develop- ment, including implementation of the Auto- Modeller and Nova modules.
Drug Discovery World Fall 2011
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