  Terpenes are a widespread class of natural products with significant chemical
and biological diversity and many of these molecules have already made their
way into medicines. Given the thousands of molecules already described, the
full characterization of this chemical space can be a challenging task when
relying in classical approaches. In this work we employ a data science-based
approach to identify, compile and characterize the diversity of terpenes
currently known in a systematic way. We worked with a natural product database,
COCONUT, from which we extracted information for nearly 60000 terpenes. For
these molecules, we conducted a subclass-by-subclass analysis in which we
highlight 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 data
for clustering and classification tasks. For clustering, we have applied and
compared k-means with agglomerative clustering, both to the original data and
following a step of dimensionality reduction. To this end, PCA, FastICA, Kernel
PCA, t-SNE and UMAP were used and benchmarked. We also employed a number of
methods for the purpose of classifying terpene subclasses using their
physico-chemical descriptors. Light gradient boosting machine, k-nearest
neighbors, random forests, Gaussian naiive Bayes and Multilayer perceptron,
with the best-performing algorithms yielding accuracy, F1 score, precision and
other metrics all over 0.9, thus showing the capabilities of these approaches
for the classification of terpene subclasses.

Machine Learning;Applications