Figure shows the basic usage to construct a pipeline for building a prediction model from a SMILES data set. The pipeline converts the SMILES into molecules using the Auto2Mol pipeline element. Then, the molecules are standardized with the ElementFilter removing molecules with uncommon chemical elements that are often hard to process in subsequent steps. In addition, the SaltRemover strips commonly used counterions, such as Na + or Cl -from molecules. Next, Morgan fingerprints are computed from the standardized molecules with MolToMorgan, which are then used as the input feature vectors to the RandomForestRegressor from scikit-learn. The number of cores for parallel execution can be specified with the n jobs argument. Finally, the created pipeline can be fitted with a data set and used for predictions as scikit-learn's estimator interface defines. The example in Figure shows how to use MolPipeline's MurckoScaffoldClustering estimator, a custom estimator for scaffold clustering of molecules implementing scikit-learn's interface. On construction, MurckoScaffoldClustering can be parameterized to use Murcko scaffolds or generic scaffolds (all atoms are considered carbons and all bonds are considered single bonds) using the make generic option. In addition, the handling of linear molecules, which would not yield a scaffold, can be specified with the parameter linear molecules strategy.