Natural product scores and fingerprints extracted from artificial neural networks

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 evalu- ated the ability of neural networks to generate fingerprints more appropriate for use with natural prod- ucts.

A manually curated dataset of natural products and synthetic decoys was used to train a multi-layer perceptron network and an autoencoder-like network. In-depth analysis showed that the extracted nat- ural product-specific neural fingerprint outperforms traditional as well as natural product-specific finger- prints on three datasets. Further, we explored how the activations from the output layer of a network can work as a novel natural product likeness score.