4.8 Article

Learning from Nature: From a Marine Natural Product to Synthetic Cyclooxygenase-1 Inhibitors by Automated De Novo Design

Journal

ADVANCED SCIENCE
Volume 8, Issue 16, Pages -

Publisher

WILEY
DOI: 10.1002/advs.202100832

Keywords

chemoinformatics; computational chemistry; drug design; machine learning; natural product

Funding

  1. ETH RETHINK initiative
  2. Novartis FreeNovation grant AI in Drug Discovery
  3. NIH [R01 GM100888, S10 OD017987, S10 OD023479]
  4. Deutsche Forschungsgemeinschaft (DFG, German Research Foundation) [316213987, SFB 1278 PolyTarget, 239748522, SFB 1127 ChemBioSys]
  5. National Cancer Institute Cancer Center Support Grant [P30 CA56036]

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The combination of natural products and synthetic molecules provides a new approach for drug discovery based on machine intelligence, creating new molecular designs inspired by bioactive natural products. Experimental results demonstrate that this method can successfully design molecules with specific target activities.
The repertoire of natural products offers tremendous opportunities for chemical biology and drug discovery. Natural product-inspired synthetic molecules represent an ecologically and economically sustainable alternative to the direct utilization of natural products. De novo design with machine intelligence bridges the gap between the worlds of bioactive natural products and synthetic molecules. On employing the compound Marinopyrrole A from marine Streptomyces as a design template, the algorithm constructs innovative small molecules that can be synthesized in three steps, following the computationally suggested synthesis route. Computational activity prediction reveals cyclooxygenase (COX) as a putative target of both Marinopyrrole A and the de novo designs. The molecular designs are experimentally confirmed as selective COX-1 inhibitors with nanomolar potency. X-ray structure analysis reveals the binding of the most selective compound to COX-1. This molecular design approach provides a blueprint for natural product-inspired hit and lead identification for drug discovery with machine intelligence.

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