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. Synthesizing 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, until recently, been limited, leading to a focus on the iterative exploration of a relatively small number of closely related compounds. One risk of this is that opportunities to identify high-quality compounds may be missed, as the tendency to quickly focus on a relatively small range of chemical diversity prevents a broad search of the chemical possibilities.
With the use of in silico predictive methods it is possible to consider a much larger range of ideas before choosing those on which to focus intellectual, synthetic, and experimental efforts.
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. However, the recent development of computational methods to automatically generate new, chemically relevant compound ideas can dramatically increase the range of ideas that can be considered.
One approach to generating new compound ideas is to apply medicinal chemistry “transformation rules” to one or more initial compounds to create related structures. A transformation rule is a structural modification that might typically be considered by a medicinal chemist in the optimization of a compound. Transformations do not necessarily correspond to specific synthetic routes or reactions, but represent relatively tractable steps in chemical space.
Transformations may be derived from the medicinal chemistry literature or medicinal chemists’ personal experiences and may include simple substitutions, functional group replacements, or larger changes such as modification, addition, or removal of ring systems.
This concept was originally introduced in the Drug Guru platform developed at Abbott and also employed in Pareto Ligand Designer and Optibrium’s StarDrop™.
A suite of software for guiding decisions in drug discovery using predictive models and multiparameter optimization, StarDrop includes a plug-in module called Nova™ that specifically enables medicinal and computational chemists to automatically generate new compound ideas by applying transformations to their molecules in order to improve their properties.
The advantage of this approach is that, because the transformations are based on structural modifications with precedence in medicinal chemistry, the resulting structures are more likely to be relevant. There is little value in proposing compound structures that are unstable, infeasible, or chemically nonsensical. This was one of the reasons for the limited success of early approaches to computational generation of new compound structures.
In practice, it is difficult to reduce the proportion of unacceptable compounds generated below 5% without losing the generality of the transformations, reducing the range of ideas that can be explored. Furthermore, although a small number of poor structures may be a minor distraction, they may also stimulate ideas for similar compounds that are chemically feasible.
These transformations can be applied iteratively to generate multiple generations of compounds, as illustrated in Figure 1. This permits a very large number of chemically relevant new ideas to be easily generated, but leads to a new problem; how to assess these ideas to identify those most likely to be of interest to a drug discovery project?