The (α, β)-k Boolean Signatures of Molecular Toxicity: Microcystin as a Case Study

BackgroundThe (, {beta})-k-Feature Set Problem is a combinatorial problem, that has been proven as alternative to typical methods for reducing the dimensionality of large datasets without compromising the performance of machine learning classifiers. ResultWe present a case study that shows that solutions of the (, {beta})-k-Feature Set Problem help to identify molecular substructures related to toxicity. The dataset investigated in this study is based on the inhibition of ser/thr-proteinphosphatases by Microcystin (MC) congeners. MC congeners are a class of structurally similar cyanobacterial toxins, which are critical to human consumption. ConclusionWe show that it is possible to identify biologically meaningful toxicity signatures by applying the (, {beta})-k feature sets on extended connectivity fingerprint representations of MC congeners. Boolean rules were derived from the feature sets to classify toxicity and can be mapped on the chemical structure, leading to insights on the absence/presence of substructures that can explain toxicity. The presented method can be applied on any other molecular data set and is therefore transferrable to other use cases.