Biological activity-based modeling identifies antiviral leads against SARS-CoV-2

Huang, Ruili; Xu, Miao; Zhu, Hongguang; Chen, Catherine Z.; Zhu, Wei; Lee, Emily M.; He, Shihua; Zhang, Li; Zhao, Jinghua; Shamim, Khalida; Bougie, Danielle; Huang, Wenwei; Xia, Menghang; Hall, Mathew D.; Lo, Donald C.; Simeonov, Anton; Austin, Christopher P.; Qiu, Xiangguo; Tang, Hengli; Zheng, Wei

doi:10.1038/s41587-021-00839-1

Cited by 40 publications

(46 citation statements)

References 46 publications

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“…We retrieved these datasets from the NCATS COVID-19 portal 5 . The NCATS team is committed to performing a range of COVID-19-related viral and host target assays, as well as analysing the results 6 . A more exhaustive description of each assay is provided in the Methods.…”

mentioning

confidence: 99%

A machine learning platform to estimate anti-SARS-CoV-2 activities

Bocci

Verma

et al. 2021

Nat Mach Intell

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Strategies for drug discovery and repositioning are urgently need with respect to COVID-19. Here we present REDIAL-2020, a suite of computational models for estimating small molecule activities in a range of SARS-CoV-2-related assays. Models were trained using publicly available, high-throughput screening data and by employing different descriptor types and various machine learning strategies. Here we describe the development and use of eleven models that span across the areas of viral entry, viral replication, live virus infectivity, in vitro infectivity and human cell toxicity. REDIAL-2020 is available as a web application through the DrugCentral web portal (http://drugcentral.org/Redial). The web application also provides similarity search results that display the most similar molecules to the query, as well as associated experimental data. REDIAL-2020 can serve as a rapid online tool for identifying active molecules for COVID-19 treatment.

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confidence: 99%

A machine learning platform to estimate anti-SARS-CoV-2 activities

Bocci

Verma

et al. 2021

Nat Mach Intell

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“…Compounds MLS000699212-03 and NCGC00100647 were discovered using biological activity-based modeling approach, in which compound activity profiles established across multiple assays were used as signatures to predict compound activity in other assays or against a new target. 219 Although the idea of using activity data in the modeling is not new, 220 the authors validated its utility by achieving ∼30% success rate in the discovery of novel antivirals. Consensus of docking techniques resulted in repurposing of nafamostat and camostat, 221 manidipine and boceprevir, 222 and subsequent discovery of two antioxidant polyhydroxy-1,3,4-oxadiazole compounds CoViTris2020 and ChloViD2020 with high activities in vitro , 222 and several perampanel analogs 223 (identified using free-energy perturbation method) as novel, non-covalent M pro inhibitors with 20 nM–5 μM IC 50 values in a kinetic assay for M pro .…”

Section: Discussionmentioning

confidence: 99%

“…Progressive growth of assay- and robotic capabilities has enabled large-scale screening campaigns against SARS-CoV-2. For example, a recent study 23 used biological activity-based modeling to identify 311 chemicals, of which 99 demonstrated in vitro activity against the virus. In another notable large-scale study, 12 000 clinical stage or FDA approved compounds from the ReFRAME library were evaluated in a Vero cell assay.…”

Section: Critical Assessment Of Early Experimental Clinical and Computational Studies On Drug Repurposing Against Covid-19mentioning

confidence: 99%

A critical overview of computational approaches employed for COVID-19 drug discovery

Amaro²,

et al. 2021

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“…As ML techniques can be applied to the predictive scenario based on previous knowledges and well-known patterns, some recent studies have contributed to the development of ML-based CADD methods targeting M pro . For example, Huang et al [ 121 ] developed a biological activity-based modeling (BABM) approach, by which the compound activity can be predicted for a new target or other assays by using profiles across multiple well-defined assays. This model obtained 311 compounds against SARS-CoV-2, 32% of which showed antiviral activity in a cell culture live virus assay.…”

Section: Cadd Against Sars-cov-2: Targeting M Promentioning

confidence: 99%

A survey on computational methods in discovering protein inhibitors of SARS-CoV-2

Liu

Wan

Wang

2021

Briefings in Bioinformatics

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The outbreak of acute respiratory disease in 2019, namely Coronavirus Disease-2019 (COVID-19), has become an unprecedented healthcare crisis. To mitigate the pandemic, there are a lot of collective and multidisciplinary efforts in facilitating the rapid discovery of protein inhibitors or drugs against COVID-19. Although many computational methods to predict protein inhibitors have been developed [ 1– 5], few systematic reviews on these methods have been published. Here, we provide a comprehensive overview of the existing methods to discover potential inhibitors of COVID-19 virus, so-called severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). First, we briefly categorize and describe computational approaches by the basic algorithms involved in. Then we review the related biological datasets used in such predictions. Furthermore, we emphatically discuss current knowledge on SARS-CoV-2 inhibitors with the latest findings and development of computational methods in uncovering protein inhibitors against COVID-19.

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Biological activity-based modeling identifies antiviral leads against SARS-CoV-2

Cited by 40 publications

References 46 publications

A machine learning platform to estimate anti-SARS-CoV-2 activities

A machine learning platform to estimate anti-SARS-CoV-2 activities

A critical overview of computational approaches employed for COVID-19 drug discovery

A survey on computational methods in discovering protein inhibitors of SARS-CoV-2

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