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Cheminformatics


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Figure 1: Illustration of workflow to initiate the generation of new compound structures, as implemented in StarDrop™: (a) Specify the input structure. A region of the molecule can be chosen to be ‘frozen’ (shown in light blue), in which case no modifications will be made to this region. (b) The transformations to apply can be selected, either individually or as groups. The groups can be managed to create groups tailored to specific objectives or to add new transformations. (c) The number of generations can be specified and a criterion for selection can be defined to limit the growth of the number of compounds generated. The selection can be defined as a minimum threshold for a property or score or a maximum number or percentage of each generation that will be used as the basis for subsequent generations


to define and manage the project requirements ensures that the computational model explores the right regions of chemical space. The goal is to stimulate the creative process in hit-to-lead and lead optimisation, not necessarily to automatical- ly find the final, optimal molecule.


By helping to consider a wide diversity of possi- ble chemical strategies, this approach can also help to mitigate the risks of inherent biases in the way that people make decisions about potential courses of action4. In particular, one common bias, called ‘confirmation bias’, reflects the tendency of people to focus on experiments that will tend to confirm, rather than challenge, their existing hypothesis. In the context of drug discovery, this can lead to pre- mature narrowing of the scope of exploration, with the potential to miss valuable opportunities to find high quality compounds. Automatic genera- tion and prioritisation of new compound ideas can highlight alternative strategies and help to ‘think outside the box’.


The next section highlights an approach to gen- erating relevant compound ideas that are interest- ing and acceptable to medicinal chemists. Scientists can consider how in silico models can be effective- ly applied in this scenario, despite the inherent uncertainties in the data generated by computa- tional methods, and how all of this data can be brought together to prioritise the compound ideas. Finally, the article will describe an illustrative example of the application of these methods to explore chemistry which is based on the lead com- pound that ultimately gave rise to the marketed serotonin reuptake inhibitor Duloxetine, before drawing some conclusions.


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Generating relevant compound ideas A successful method to generate compound ideas must satisfy a number of requirements:


l It must generate a wide diversity of chemistry, as the objective is to explore many ideas in the search for an optimal solution. l The compound structures generated must be rel- evant. In particular, the number of ‘nonsensical’, eg chemically unstable or infeasible, compounds must be kept to a minimum. l The user must be able to control the generation process; for example by specifying a group or tem- plate that must remain present or by limiting the breadth of search. l The ideas generated must tend towards ‘drug- like’ compounds.


Early approaches to computational generation of new compound structures, described under the term ‘de novo design’5, commonly worked by ‘growing’ a small fragment known to weakly bind to a biological target or linking two or more frag- ments. The newly generated molecules were chosen to fit a model of the binding pocket of the target, forming multiple interactions and hopefully result- ing in increased binding efficiency. The success of these methods was limited by the fact that the mol- ecules proposed were often chemically infeasible or did not have sufficiently ‘drug like’ physicochemi- cal and ADME properties. These limitations could, to some extent, be addressed by post-filtering of compounds to remove inappropriate compounds6. An alternative approach, that helps to meet the requirements above was pioneered by a package


Drug Discovery World Fall 2011

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