The Geometry of Activity Cliffs: Representation Dependence and Multi-Scale Characterization of Activity Landscapes
arXiv:2605.30831v1 Announce Type: cross Abstract: Activity cliffs, structurally similar compounds with large potency differences, are widely treated as intrinsic features of chemical datasets. We argue that apart from target biology, much of our cliff understanding is a consequence of the geometry induced by the chosen molecular representation, not a property of a molecule pair itself. We designed a six-step pipeline to systematically test this hypothesis. The pipeline consists of: assessing pairwise distance geometry, cliff enrichment, activity gradient distribution, persistent homology of th
This paper, published on arXiv, indicates ongoing academic research into fundamental aspects of drug discovery and materials science, where efficient molecular characterization is critical.
Understanding the 'geometry of activity cliffs' impacts the efficiency and interpretability of drug discovery and materials design processes, potentially accelerating the development of new compounds.
The proposed pipeline and hypothesis challenge current assumptions about molecular representation in chemical datasets, which could lead to more robust and less biased methods for identifying promising compounds.
- · Pharmaceutical R&D
- · Computational Chemists
- · AI/ML for Drug Discovery
- · Materials Science
- · Companies relying on outdated molecular analysis techniques
Improved methods for predicting drug activity and material properties based on molecular structure.
Faster discovery of novel compounds with desired characteristics, reducing R&D costs and timelines.
Enhanced ability to navigate complex chemical spaces, potentially leading to breakthroughs in previously intractable problems in medicine and engineering.
This signal links to a primary source. Continuum Brief monitors and indexes it as part of the live intelligence stream — we do not republish source content.
Read at arXiv cs.LG