Cheminformatics
Combining chemists’ expertise and a computer’s advanced capabilities to generate ‘good’ ideas
One of the defining challenges of drug discovery is the need to make complex decisions regarding the design and selection of potential drug molecules based on a relative scarcity of experimental data.
A
high quality lead or drug candidate requires a balance of many properties, including potency, selectivity, absorp- tion, distribution, metabolism, elimination (ADME) and safety. Synthesising compounds and generating experimental data, even using modern high-throughput methods, is time-consuming and expensive. Therefore, the opportunity to explore new compound ideas has been limited. An experienced medicinal chemist can easily gen- erate enough ideas to keep a team of synthetic chemists and biologists busy and each idea must be carefully considered. In this scenario, the risk is that opportunities to quickly identify high quality compounds may be missed, as the ten- dency to quickly focus on a relatively small range of chemical diversity prevents a broad search of chemical possibilities.
The emergence of predictive in silico models of the properties of potential drug compounds offers the ability to quickly and inexpensively generate vast quantities of predicted data on large numbers of compounds1. Furthermore,
Drug Discovery World Fall 2011
modern ‘multi-parameter optimisation’ methods allow the potential to integrate this information and assess a large number of compound ideas against the ideal profile of properties required in a high quality lead or candidate drug2,3. In this new scenario, the limitation becomes the time and experience necessary to generate a wide diversity of compound ideas and manually enter these into a computer.
This article discusses an approach to overcome the relative scarcity of ideas in the case where it is easy to assess potential new compounds using predictive methods. The approach automatically generates chemically relevant compound ideas, assesses them against a project’s requirements and prioritises the ideas for detailed consideration by an expert. In this way, an optimal combination of the strengths of a computer and an expert user can be achieved. The computer’s ability to analyse and prioritise a large number of chemical possi- bilities complements the fact that an expert can- not possibly examine all generated structures individually. At the same time, the expert’s ability
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By Dr Matthew Segall, Edmund Champness,
Dr Chris Leeding, Dr Ryan Lilien, Dr Ramgopal Mettu and Dr Brian Stevens
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