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NP-NFP_Menke-Massa-Koch.pdf (10.8 MB)

Natural Product Scores and Fingerprints Extracted from Artificial Neural Networks.

preprint
submitted on 31.03.2021, 19:08 and posted on 01.04.2021, 12:49 by Janosch Menke, Joana Massa, Oliver Koch
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.

Funding

DFG, Priority Program „Algorithms for Big Data", SPP 1736, Grant No. KO 4689/2-2

DFG, GRK 2515: Chemical biology of ion channels (Chembion)

History

Email Address of Submitting Author

Oliver.Koch@Uni-Muenster.de

Institution

University of Münster, Institute of Pharmaceutical and Medicinal Chemistry

Country

Germany

ORCID For Submitting Author

0000-0001-9228-217X

Declaration of Conflict of Interest

There are no conflicts to declare.

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