Structure-based view of the druggable genome

Wang, Jiayan; Yazdani, Setayesh; Han, Ana; Schapira, Matthieu

doi:10.1016/j.drudis.2020.02.006

Cited by 26 publications

(22 citation statements)

References 34 publications

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“…One possible reason for the low similarity to drugs is that the protease inhibitors tend to have higher molecular weight and thus their molecular structures showed large variety. Another reason would be that a majority of the protease inhibitory drugs are targeted toward serine or zinc proteases [68,69]. Also, the expected drug lopinavir/ritonavir has been designed for HIV protease, which is aspartic protease.…”

Section: Discussionmentioning

confidence: 99%

Knowledge‐based structural models of SARS‐CoV‐2 proteins and their complexes with potential drugs

et al. 2020

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Edited by John BriggsThe World Health Organization (WHO) has declared the coronavirus disease 2019 (COVID-19) caused by the novel coronavirus SARS-CoV-2 a pandemic. There is, however, no confirmed anti-COVID-19 therapeutic currently. In order to assist structure-based discovery efforts for repurposing drugs against this disease, we constructed knowledge-based models of SARS-CoV-2 proteins and compared the ligand molecules in the template structures with approved/experimental drugs and components of natural medicines. Our theoretical models suggest several drugs, such as carfilzomib, sinefungin, tecadenoson, and trabodenoson, that could be further investigated for their potential for treating COVID-19.

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Section: Discussionmentioning

confidence: 99%

Knowledge‐based structural models of SARS‐CoV‐2 proteins and their complexes with potential drugs

et al. 2020

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“…•Druggability. We used the recently published method of Wang and coauthors to assess ligand binding ability. , We extracted the 236 InterPro domains listed as ligandable and searched PlasmoDB for proteins with these domains.…”

Section: Future Perspectivesmentioning

confidence: 99%

Prioritization of Molecular Targets for Antimalarial Drug Discovery

et al. 2021

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There is a shift in antimalarial drug discovery from phenotypic screening toward target-based approaches, as more potential drug targets are being validated in Plasmodium species. Given the high attrition rate and high cost of drug discovery, it is important to select the targets most likely to deliver progressible drug candidates. In this paper, we describe the criteria that we consider important for selecting targets for antimalarial drug discovery. We describe the analysis of a number of drug targets in the Malaria Drug Accelerator (MalDA) pipeline, which has allowed us to prioritize targets that are ready to enter the drug discovery process. This selection process has also highlighted where additional data are required to inform target progression or deprioritization of other targets. Finally, we comment on how additional drug targets may be identified.

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“…One possible reason of the low similarity to drugs is that the protease inhibitors tend to have higher-molecular weight and thus their molecular structures showed large variety. Another reason would be that a majority of the protease inhibitory drugs are targeted toward serine or zinc proteases [66,67]. Also, the expected drug lopinavir/ritonavir has been designed for HIV-protease, which is aspartic protease.…”

Section: Discussionmentioning

confidence: 99%

Knowledge-Based Structural Models of SARS-CoV-2 Proteins and Their Complex with Potential Drugs

Hijikata¹,

Shionyu-Mitsuyama²,

Nakae³

et al. 2020

Preprint

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<div>The World Health Organization (WHO) has declared a pandemic of the 2019 novel cornavirus SARS-CoV-2 infection (COVID-19). There is, however, no confirmed anti-COVID-19 therapeutic currently. In order to assist structure-based discovery of repurposing drugs against this disease, knowledge-based models of SARS-CoV-2 proteins were constructed using MODELLER software, and their models were refined by PHENIX and COOT. The model quality was assessed with MolProbity. The ligand molecules in the template structures were compared with approved/experimental drugs and components of natural medicines from the KEGG and KNApSAcK databases. The models suggested several drugs, such as carfilzomib, sinefungin, tecadenoson, and trabodenoson, as potential drugs for COVID-19.</div><div><br></div>

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Structure-based view of the druggable genome

Cited by 26 publications

References 34 publications

Knowledge‐based structural models of SARS‐CoV‐2 proteins and their complexes with potential drugs

Knowledge‐based structural models of SARS‐CoV‐2 proteins and their complexes with potential drugs

Prioritization of Molecular Targets for Antimalarial Drug Discovery

Knowledge-Based Structural Models of SARS-CoV-2 Proteins and Their Complex with Potential Drugs

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