ArticlebioRxiv : the preprint server for biology2025
Efficiently finding activity cliffs.
Article in bioRxiv : the preprint server for biology, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
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Authors and funding
2 authors.
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Abstract
Activity cliffs remain a key challenge in computational drug design, defying even modern machine learning approaches. Here, we present a dual approach that allows either navigating a structure-activity landscape to quickly identify activity cliffs, or how avoid them and identify maximally smooth sectors of chemical space. Both methods are built upon the BitBIRCH clustering algorithm, so they are ideally suited to analyze very large compound libraries. The code is freely available at https://github.com/mqcomplab/BitBIRCH_AC.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.