Biosynfoni: a biosynthesis-informed and interpretable lightweight molecular fingerprint

Abstract

Natural products provide a rich source of bioactive molecules for a variety of applications. Molecular fingerprints are the tool of choice for systematic large-scale studies of their structures. However, current molecular fingerprints insufficiently represent characteristic features of natural products inherently, decreasing the interpretability of natural product-specific predictions. Here, we show that a natural product-specific molecular fingerprint based on a relatively small set of selected biosynthetic building blocks provides more interpretable predictions of biosynthetic distance and natural product classification. Our fingerprint Biosynfoni outperforms MACCS, Morgan, and Daylight-like fingerprints in biosynthetic distance estimation, using 39 substructure keys. Moreover, Biosynfoni’s design, compactness, and concrete substructure definition allow easy visualisation of the detected substructures and their respective biosynthetic pathway origins. Through Biosynfoni, users can gain more insights from predictions and better examine the importance of features within machine learning models. Our results show that a short fingerprint consisting of biologically significant building blocks performs on par with top-performing molecular fingerprints for natural product classification while improving prediction explainability.