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

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.

Neural Networks for Pulmonary Disease Diagnosis using Auditory and Demographic Information

Pulmonary diseases impact millions of lives globally and annually. The recent outbreak of the pandemic of the COVID-19, a novel pulmonary infection, has more than ever brought the attention of the research community to the machine-aided diagnosis of respiratory problems. This paper is thus an effort to exploit machine learning for classification of respiratory problems and proposes a framework that employs as much correlated information (auditory and demographic information in this work) as a dataset provides to increase the sensitivity and specificity of a diagnosing system. First, we use deep convolutional neural networks (DCNNs) to process and classify a publicly released pulmonary auditory dataset, and then we take advantage of the existing demographic information within the dataset and show that the accuracy of the pulmonary classification increases by 5% when trained on the auditory information in conjunction with the demographic information. Since the demographic data can be extracted using computer vision, we suggest using another parallel DCNN to estimate the demographic information of the subject under test visioned by the processing computer. Lastly, as a proposition to bring the healthcare system to users' fingertips, we measure deployment characteristics of the auditory DCNN model onto processing components of an NVIDIA TX2 development board.