Natural Product Scores and Fingerprints Extracted from Artificial Neural Networks.

Abstract

<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>

High-Throughput Screening and Molecular Dynamics Simulation of Natural Products for the Identification of Anticancer Agents against MCM7 Protein

“…The NPASS database is a curated natural product library that contains sufficient amounts of both active and inactive natural products. It has been widely used as novel drug candidates against SARS-CoV-2 [51,52]. From these compounds with the greatest binding affinity as determined by the molecular docking score, the top four were selected i.e., NPA000018 (Lolicine A) (−11.9 kcal/mol), NPA000111 (Armochaetoglobin N) (−11.2 kcal/mol), NPA000305 (Quartromicin D2) (−11.8 kcal/mol), and NPA014826 (Pyralomicin 2b) (−14.2 kcal/mol) (Table 1).…”