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Terpenes are a widespread class of natural products with significant chemicaland biological diversity and many of these molecules have already made theirway into medicines. Given the thousands of molecules already described, thefull characterization of this chemical space can be a challenging task whenrelying in classical approaches. In this work we employ a data science-basedapproach to identify, compile and characterize the diversity of terpenescurrently known in a systematic way. We worked with a natural product database,COCONUT, from which we extracted information for nearly 60000 terpenes. Forthese molecules, we conducted a subclass-by-subclass analysis in which wehighlight several chemical and physical properties relevant to several fields,such as natural products chemistry, medicinal chemistry and drug discovery,among others. We were also interested in assessing the potential of this datafor clustering and classification tasks. For clustering, we have applied andcompared k-means with agglomerative clustering, both to the original data andfollowing a step of dimensionality reduction. To this end, PCA, FastICA, KernelPCA, t-SNE and UMAP were used and benchmarked. We also employed a number ofmethods for the purpose of classifying terpene subclasses using theirphysico-chemical descriptors. Light gradient boosting machine, k-nearestneighbors, random forests, Gaussian naiive Bayes and Multilayer perceptron,with the best-performing algorithms yielding accuracy, F1 score, precision andother metrics all over 0.9, thus showing the capabilities of these approachesfor the classification of terpene subclasses.

The chemical space of terpenes: insights from data science and AI