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Machine learning (ML) has recently become very popular within pharmaceutical industry (Roy et al., 2015; Sprous et al., 2010). Tasks as, e.g., building predictive models, performing virtual screening, or predicting compound activities are potential use cases for such ML applications (Li et al., 2021; Simm et al., 2018). Traditionally, ML models often rely on the quantitative structure-activity relationship (QSAR) that has been popularized by medicinal chemists and statisticians to relate bioactivities to specific functional group manipulations (Dudek et al., 2006; Verma et al., 2010). This QSAR approach decreases the dimensionality of the underlying problem and projects the molecular structure into a space spanned by the physicochemical features. While early approaches relied more on linear regression, modern approaches combine such features with non-linear ML algorithms.

kallisto: A command-line interface to simplify computational modelling and the generation of atomic features