<div>Due to its desirable properties, natural products are an important ligand class for medicinal chemists. However, due to their
structural distinctiveness, traditional cheminformatic approaches, like ligand-based virtual screening, often perform worse
for natural products. Based on our recent work, we evaluated the ability of neural networks to generate fingerprints more
appropriate for the use with natural products. A manually curated dataset of natural products and synthetic decoys was used
to train a multi-layer perceptron network and an autoencoder-like network. An in-depth analysis showed that the extracted
natural product specific neural fingerprints outperforms traditional as well as natural product specific fingerprints on three
datasets. Further, we explore how the activation from the output layer of a network can work as a novel natural product
likeness score. Overall two natural product specific datasets were generated, which are publicly available together with the
code to create the fingerprints and the novel natural product likeness score.<br></div>
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