Learning from Nature: From a Marine Natural Product to Synthetic Cyclooxygenase-1 Inhibitors by Automated De Novo Design.
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.
Friedrich, Lukas; Cingolani, Gino; Ko, Ying-Hui; Iaselli, Mariaclara; Miciaccia, Morena; Perrone, Maria Grazia; Neukirch, Konstantin; Bobinger, Veronika; Merk, Daniel; Hofstetter, Robert Klaus; Werz, Oliver; Koeberle, Andreas; Scilimati, Antonio; and Schneider, Gisbert, "Learning from Nature: From a Marine Natural Product to Synthetic Cyclooxygenase-1 Inhibitors by Automated De Novo Design." (2021). Department of Biochemistry and Molecular Biology Faculty Papers. Paper 192.
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This is the peer reviewed version of the following article: Friedrich, L., Cingolani, G., Ko, Y.-H., Iaselli, M., Miciaccia, M., Perrone, M. G., Neukirch, K., Bobinger, V., Merk, D., Hofstetter, R. K., Werz, O., Koeberle, A., Scilimati, A., Schneider, G., Learning from Nature: From a Marine Natural Product to Synthetic Cyclooxygenase-1 Inhibitors by Automated De Novo Design. Adv. Sci. 2021, 8, 2100832., which has been published in final form at https://doi.org/10.1002/advs.202100832.
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