An Open-Source\nImplementation of the Scaffold Identification\nand Naming System (SCINS) and Example Applications

Organizing and partitioning\nsets of chemical structures\nis of considerable\npractical significance, e.g., in compound library analysis and the\npostprocessing of screening hit lists. Approaches such as unsupervised\nclustering are computationally demanding and dataset-dependent; on\nthe other hand, rule-based methods, such as those based on Murcko\nscaffolds, have linear time complexity but are often too fine-grained,\nleading to a large number of singletons or sparsely populated classes.\nAn alternative rule-based method that seeks to achieve an optimal\nbalance when grouping compounds into sets is the ‘Scaffold\nIdentification and Naming System’ (SCINS). To facilitate public\nuse of this previously published method, here, we provide an open-source\nPython implementation of SCINS, dependent only on RDKit. We show that\nSCINS can be useful in identifying sparsely and densely populated\nregions in chemical space in large databases, here exemplified with\nEnamine REAL Diverse and ChEMBL. We find that Enamine REAL Diverse\ncovers a much smaller SCINS space relative to ChEMBL, whereas the\nopposite is true when Murcko and generic Murcko scaffolds are considered.\nAdditionally, we show that SCINS can result in chemically intuitive\ngrouping of medium-sized sets of bioactive compounds, which can be\nuseful in compound selection from virtual screening campaigns as well\nas postprocessing of experimental hit lists. Hence, in this work,\nwe provide both an open-source implementation of SCINS and its characterization\nwith relevant use cases.