Efficiently Finding Activity Cliffs with BitBIRCH

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 avoiding them and identifying 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.