A bayesian deep learning-based approach to group contribution methods: A study on cannabinoids and terpenes

Abstract

Although there are several group contribution methods available for predicting thermodynamic properties, the uncertainties associated with these predictions are unknown. In this study, we present a new group contribution method approach based on bayesian neural networks (BNNs) in order to provide predictions and their associated uncertainty. Various machine learning techniques, including a fully connected neural network (FCNN) and a graph neural network (GNN) under the BNNs framework, were employed to efficiently implement this methodology. As a case of study, we focused on predicting the melting and boiling points of cannabinoids and terpenes, as well as their corresponding uncertainties. Individual analyses were carried out for boiling and melting points using databases containing 2529 and 2862 chemical compounds, respectively. Additionally, we conducted an assemble study of both properties using a database of 1503 chemical compounds. To validate the models, a database consisting of 47 cannabinoids and terpenes was employed. The models exhibited exceptional prediction results for boiling points, showcasing coefficients of determination R2≥ 0.9 for all the studied models. On the contrary, only the assemble GNN model yielded accurate melting point predictions with an R2 value of 0.94, while the R2 values obtained from the other models ranged from 0.51 to 0.66. Finally, our predictions for the melting and boiling points of two well-known cannabinoids, CBD and THC, are (70.5±45.4) °C and (419.4±25.4) °C for CBD, and (87.0±46.7) °C and (411.8±29.7) °C for THC, respectively.