At the recent Boston ACS, Herman Skolnik Awardee Jürgen Bajorath described the concept of Matched Molecular Pairs (MMPs) as one of the most powerful ideas in medicinal chemistry. However, I would argue that the more general concept of Matched Molecular Series that he himself has developed puts MMPs in the shade.
In my own presentation at the meeting I described what I called the “Matched Pair Mentality” which prevented for some years the realisation that the same methodology applies to series longer than two. By describing the concept in terms of pairs, chemists could not think beyond two molecules as the word “pair” is somewhat special and cannot easily be replaced with a term for three: would this be a triplet, a triad, or a trio? Furthermore, the concept of matched pairs has become synonymous for many with “a matched pair transformation” (that is, a replacement of a terminal R Group), and this cemented the idea of two R groups as a fundamental concept rather than just a specific instance of a general case. Overall, this puts me in mind of the inhabitants of Flatland unable to conceive of a 3rd or higher dimension.
My talk was part of the “Visualizing Chemistry Data to Guide Optimization” symposium organised by Matt Segal and Erin Davis, and focused on the interface we developed for our matched series prediction method, Matsy. This is a visual interface based around R groups as first-class objects (see slide 14 below for example). One advantage of this approach is that it makes it clear that predictions are based solely on the R groups and not the scaffold. It should also help break the matched pair mentality by illustrating that matched pairs are just a subset of matched series: drag down one R group and the predictions are based on matched pairs, drag down another and the predictions are based on series of length 3, and so on. Finally, this interface makes it easy to play around with series, swapping the order, adding new R groups in, and moving between predictions for improving a property versus making it worse.
The elephant in the room is that this may not be the interface you want, despite my attempts to convince you that this is the One True Way. You may indeed want a dataset-centric approach and enough of this malarkey. If so, we’ve got that base covered too as we’ve partnered with Optibrium to introduce this to StarDrop. Their approach integrates both Matsy predictions and predictions from SAR transfer into a single interface, and shows the underlying series from which the predictions come. You can see a demo of this in the webinar linked to at the top of an earlier blogpost.
I would argue that the bigger idea is that we should perceive the behaviour of compounds in terms of relationships between chemical species. Matched molecular pairs (and series) fit into this framework but so do activity cliffs, neighborhood analysis, molecular similarity, bioisosterism, group efficiency, free energy perturbation. I would also argue that the idea of associating differences in chemical behaviour with specific structural relationships has been around for a very long time (e.g Hammett constants).
I agree that it can be very useful to visualize data (and information) but the quantitative/statistical basis of using visualizations to make decisions in an objective manner does not appear to be established.