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Journal of Medicinal Chemistry
Article
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PubMed Central
Other literature type . 2020
Data sources: PubMed Central
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Mining Natural Products for Macrocycles to Drug Difficult Targets

Authors: Fabio Begnini; Vasanthanathan Poongavanam; Björn Over; Marie Castaldo; Stefan Geschwindner; Patrik Johansson; Mohit Tyagi; +5 Authors

Mining Natural Products for Macrocycles to Drug Difficult Targets

Abstract

Lead generation for difficult-to-drug targets that have large, featureless, and highly lipophilic or highly polar and/or flexible binding sites is highly challenging. Here, we describe how cores of macrocyclic natural products can serve as a high-quality in silico screening library that provides leads for difficult-to-drug targets. Two iterative rounds of docking of a carefully selected set of natural-product-derived cores led to the discovery of an uncharged macrocyclic inhibitor of the Keap1-Nrf2 protein-protein interaction, a particularly challenging target due to its highly polar binding site. The inhibitor displays cellular efficacy and is well-positioned for further optimization based on the structure of its complex with Keap1 and synthetic access. We believe that our work will spur interest in using macrocyclic cores for in silico-based lead generation and also inspire the design of future macrocycle screening collections.

Country
Sweden
Keywords

Models, Molecular, Biological Products, Kelch-Like ECH-Associated Protein 1, Databases, Factual, NF-E2-Related Factor 2, Drug Evaluation, Preclinical, Läkemedelskemi, Molecular Docking Simulation, Structure-Activity Relationship, Solubility, Drug Discovery, Microsomes, Liver, Data Mining, Humans, Computer Simulation, Polycyclic Compounds, Medicinal Chemistry, PROTEIN-PROTEIN INTERACTION; SMALL MOLECULES; KELCH DOMAIN; PREDICTION; DISCOVERY; INHIBITORS; FRAGMENTS; PROGRAM; BIND; RULE

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    selected citations
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    This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
    41
    popularity
    This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network.
    Top 10%
    influence
    This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
    Top 10%
    impulse
    This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network.
    Top 10%
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selected citations
These citations are derived from selected sources.
This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Citations provided by BIP!
popularity
This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network.
BIP!Popularity provided by BIP!
influence
This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Influence provided by BIP!
impulse
This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network.
BIP!Impulse provided by BIP!
41
Top 10%
Top 10%
Top 10%
Green
hybrid