An alternative approach, employed by StarDrop, is to prioritize compounds with the best balance of properties relative to an ideal profile, as defined by a drug discovery project team (an example is shown in Figure 3). Examples of such methods include the calculation of a score for each compound considering the desirability of its property values or a probabilistic scoring approach that prioritizes compounds with the highest chance of success against the required profile.
This conjures a utopian vision (at least for some!) of a computer that automatically generates and explores new compound ideas before selecting a small number for synthesis and testing, confident in the knowledge that these will yield a high-quality drug. This would dramatically reduce the time and effort required for drug discovery, and increase the chance of finding a high-quality candidate drug by exploring a very large diversity of possibilities.
Alas, the realization of such a vision remains remote in practice. This is because the accuracy of predictive methods is insufficient to identify a compound and say with confidence that it will achieve the requirements for a high-quality outcome. The accuracies of predictive models range from a factor of two to ten in prediction of binding affinities or other biological properties. Therefore, while models can be very useful in discarding poor compounds, they can only identify those compounds most likely to meet the ideal criteria. A computer cannot yet design a perfect compound.
Furthermore, other requirements of a good compound, such as synthetic tractability and novelty from an IP perspective, remain subjective factors that rely on the expertise and creativity of a medicinal chemist. Therefore, the ideal approach combines a chemist’s expertise with the capabilities of a computer. The computer can help to explore a wide range of possibilities and prioritize those most likely to be of interest for detailed consideration, while the expert will make the final decision on the strategy to be adopted.