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called ‘Drug Guru’ (drug generation using rules), developed by a team at Abbott Laboratories7 and has also been applied in other platforms such as Pareto Ligand Designer8 and StarDrop™9. This approach works by applying a set of medicinal chemistry ‘transformation rules’ to an initial ‘par- ent’ molecule to generate related ‘child’ structures. These transformations are based on collective medicinal chemistry experience and examples of transformation rules range from simple substitu- tions or functional group replacements to more dra- matic modifications of the molecular framework such as ring opening or closing. This approach ensures that a high proportion of the compound structures generated are relevant; typically 90%- 95% are acceptable to medicinal chemists, while encoding a wide range of different chemistries. The transformations do not have to correspond to specific chemical reactions or synthetic routes; they are intended to describe changes to molecules that a medicinal chemist might consider in the course of an optimisation project. A single trans- formation might require multiple synthetic steps or the synthesis of new building blocks. However, the transformations are typically not major rearrange- ments – they are relatively feasible moves in chem- ical space.

Applying many transformations iteratively to generate multiple ‘generations’ of compound ideas can result in very large numbers of molecules. Therefore, it is important to allow the user to exert some control on the generation process. For exam- ple, it may be desirable to specify a region of the parent compound that must not be modified, to limit the number and types of transformations that are applied or to specify a property criterion against which to select a subset of the compounds in each generation to control the growth of com- pound numbers. An example of such a workflow is shown in Figure 1.

How much can we trust predictive models?

In order to prioritise the large number of gener- ated compound ideas and understand which are most likely to have appropriate properties, it is important to use in silico predictive models of the key properties. However, it is reasonable to ask how reliable the predictions from these mod- els are. All in silico models have a high degree of statistical uncertainty in the values they predict and limitations to the range of chemistries to which they are applicable. It is important that models explicitly indicate the uncertainties in their predictions and that these are taken into

Drug Discovery World Fall 2011 17

account to understand when a user can confi- dently distinguish between compounds based on these predictions.

It is not yet possible to design or select a specif- ic molecule in a computer, confident that it will have the properties required when synthesised and tested. Instead, it is vital to use the information provided by predictive models to focus on the com- pounds with the highest likelihood of success and bias the odds of finding a high quality compound in our favour10.

Given the uncertainty in the assessment of the quality of compound ideas, it is also important to explore a range of diverse compounds. It doesn’t make sense to focus too heavily on one group of closely related compounds, as it is possible that they may all fail for a common reason. Wherever possible, users should look for a balance of qual- ity with chemical diversity when choosing com- pounds, to mitigate risk, validate the predicted hypothesis and better understand the relation- ship between the compound structures and their properties. Automatic generation of compound ideas helps to ‘look outside the box’ and expand the search for a diverse range of options for fur- ther investigation.

Bringing together the data to prioritise compound ideas

The large volume of compound ideas and associ- ated property data that may be generated is impossible for a person to examine ‘manually’. A successful compound must achieve a balance of multiple, often conflicting, property requirements and the objective is to identify compound ideas that are likely to meet this property profile. Furthermore, each drug discovery project with different therapeutic objectives is likely to require a different profile of properties. Therefore, priori- tisation of the compound ideas to reduce the

Figure 2

An example scoring profile for a project with the objective of identifying suitable compounds for a serotonin reuptake inhibitor, showing the properties of interest, the desired value ranges and the relative importance of each criterion. For example, the most important property was inhibition of the serotonin transporter, for which a

predicted Ki of less than 10nM (log Ki < 1) was required. This was followed by an aqueous solubility of greater than

10µM (logS > 1) and a positive prediction for human intestinal absorption

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