BitBIRCH clustering gives a scalable way to find, or steer clear of, activity cliffs
- Two complementary methods build on the BitBIRCH clustering algorithm to handle activity cliffs in very large compound collections.
- One navigates the structure-activity landscape to locate cliffs quickly; the other avoids them by finding the smoothest regions of chemical space.
- The abstract gives no benchmark or validation details, so the main claim is scalability.
- Code is freely available on GitHub.
💡 Points Based on the abstract
The claim here is scalability, and the abstract gives nothing to test it against. Pairwise-similarity cliff searches are the obvious comparator, and speed alone does not show the cliffs found are real rather than assay noise. Before adopting it, run it on a series where your team already knows the cliffs and see whether they are recovered.