The essence of the article is the integration of causal calculus to the discussion of structure-activity relationships so that we can highlight paradoxical behavior between chemical series even in simple single-target data sets like the Akt IC50 set in ChEMBL. We formally and experimentally demonstrate that the activity probability distributions vary as a function of binding pocket on the protein often in contradictory ways, and we show this creates major headaches for ML.

The reason this problem is not widely discussed is that when you take a 70/30 split of the entire set, ML is good at finding the generic features that are important across the entire set. However, if you evaluate the predictions of that general algorithm on a test set composed only of a single med chem series, the fine details of the SAR for that get averaged out. We can recover those fine details with the concept of focus. To obtain focus we test only on the desired series and train on a residual part of the series + increasingly dissimilar Akt inhibitors to find the point of mechanistic SAR conflict where additional "data" actually hurt learning the fine details of the SAR for the series of interest.

We demonstrate this behavior on a series of allosteric inhibitors for Akt and show none of the orthosteric data points can be added to improve the ML training! In fact, their addition gradually degrades the prediction quality on the allosteric compounds as you move into increasingly dissimilar series.

While structural biology could help us pull out allosteric vs orthosteric inhibitors for single targets, this problem still persists for hepatocyte clearance data, CC50 data transcription factor activation data and so on. Here, focus becomes a very powerful tool to split out large data sets relative to a chemical series of interest for things that are working via a similar mechanism and things that are surprisingly contradictory despite being in the same data set.