Machine learning study of the molecular drivers of natural product prices

The price of chemicals is a very complex variable. It can be impacted by production costs but also by market and managerial factors, which may have complex relationships with molecular characteristics and the state of technology and society. In this work, we explore the extent to which molecular characteristics can help explain natural product prices with the aid of machine learning tools. We interpret models trained on molecular descriptors and molecular fingerprints. These models can explain a notable proportion of the variation in prices, suggesting that production and separation costs are a major contributor to current natural product prices. Some molecular properties stand out as key p…