2021
DOI: 10.1038/s41467-021-23443-y
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Exploring protein hotspots by optimized fragment pharmacophores

Abstract: Fragment-based drug design has introduced a bottom-up process for drug development, with improved sampling of chemical space and increased effectiveness in early drug discovery. Here, we combine the use of pharmacophores, the most general concept of representing drug-target interactions with the theory of protein hotspots, to develop a design protocol for fragment libraries. The SpotXplorer approach compiles small fragment libraries that maximize the coverage of experimentally confirmed binding pharmacophores … Show more

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Cited by 39 publications
(47 citation statements)
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“…Therefore, it is imperative to develop specific and safe antiviral drugs against SARS-CoV-2. The global research community has launched diverse de novo efforts, including worldwide collaborations such as the COVID Moonshot consortium for the discovery of SARS-CoV-2 main protease inhibitors [8], and large fragment screens against multiple viral targets [9,10] using innovative compound libraries [11,12]. In addition to bottom-up approaches, existing chemical entities can be further exploited by repurposing approved drugs [13,14], clinical trial drug candidates or natural products.…”
Section: Introductionmentioning
confidence: 99%
“…Therefore, it is imperative to develop specific and safe antiviral drugs against SARS-CoV-2. The global research community has launched diverse de novo efforts, including worldwide collaborations such as the COVID Moonshot consortium for the discovery of SARS-CoV-2 main protease inhibitors [8], and large fragment screens against multiple viral targets [9,10] using innovative compound libraries [11,12]. In addition to bottom-up approaches, existing chemical entities can be further exploited by repurposing approved drugs [13,14], clinical trial drug candidates or natural products.…”
Section: Introductionmentioning
confidence: 99%
“…Very recently, Bajusz et al. have reported a fragment, SX013, that blocks the SARS‐CoV‐2 replication in Vero E6 cells with an EC 50 of 304 μM, [46] which is double of that for F01 in Vero‐81 cells. The ligand efficiency of F01 is 0.29–0.30 kcal mol −1 heavy atom −1 showing that F01 is a good fragment lead and deserved to be optimized in order to increase its potency and other drug related properties.…”
Section: Resultsmentioning
confidence: 99%
“…Such strategy helps overcome drug discovery challenges that limit pharmacokinetic performances and drug formulation option. For instance, the prodrugs Ivemend and Gilenya were reported to improve solubility and enhance permeation, respectively ( 5 ). Therefore, a number of prodrugs were first collected by reviewing PubMed literatures ( 34 ) using various keywords such as ‘prodrug’, ‘pro-drug’, etc.…”
Section: Prodrugsmentioning
confidence: 99%
“…Drug discovery is promoted by not only the knowledge of drugs ( 1 ) and their therapeutic targets ( 2–4 ), but also the comparative data with respect to other bioactive agents and other targets. Such comparative data include the knowledge of poor binders or non-binders of individual target that are useful for developing drug discovery tool of enhanced performance ( 5–7 ); the information of prodrugs that facilitates drug design by improving pharmacokinetic/pharmacodynamic features ( 8 ); the co-targets of therapeutic targets that facilitate the investigations of multi-target strategies ( 9 ), off-target ( 10 , 11 ) & undesired effect ( 9 ); the collective structure-activity landscapes of drugs against individual target that reveal important pharmaceutical features such as activity cliffs ( 12 ); and the drugs’ profiles of their drug-like properties that provide drug-likeness landscapes of the explored bioactive chemical space for therapeutic targets ( 13 ). Particularly, there is a rapid trend of the discovery of Artificial Intelligence (AI) tools for the drug discovery ( 14 , 15 ), including the AI tools for identifying bioactive compounds, and the construction of such tools requires data of poor binders and non-binders of a specific target ( 16 ).…”
Section: Introductionmentioning
confidence: 99%
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