Potential Non-Covalent SARS-CoV-2 3C-like Protease Inhibitors Designed Using Generative Deep Learning Approaches and Reviewed by Human Medicinal Chemist in Virtual Reality

Zhavoronkov, Alex; Zagribelnyy, Bogdan; Zhebrak, Alexander; Aladinskiy, Vladimir; Terentiev, Victor A; Vanhaelen, Quentin; Bezrukov, Dmitry S.; Polykovskiy, Daniil; Shayakhmetov, Rim; Filimonov, Andrey; Bishop, Michael; McCloskey, Steve; Leija, Edgardo; Bright, Deborah; Funakawa, Keita; Lin, Yen‐Chu; Huang, S.-H.; Liao, Hsuan-Jen; Aliper, Alex; Ivanenkov, Yan A.

doi:10.26434/chemrxiv.12301457

Cited by 28 publications

(11 citation statements)

References 29 publications

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“…This will be very important in the future detection and diagnosis of COVID-19. Computer-assisted algorithms have also been used in numerous additional areas of COVID-19 research, including the taxonomic classification of COVID-19 genomes, 48 survival prediction of severe patients, 49 identification and prioritization of potential drug candidates, 50 automated detection and monitoring of the disease progression over time, 51 development of neural network classifiers for a large-scale screening of COVID-19 patients based on their respiratory patterns, 52 generation of novel drug-like compounds for COVID-19 treatment, 53 and identification of potential candidates for vaccine components. 54,55…”

Section: Discussionmentioning

confidence: 99%

The role of chest computed tomography in the management of COVID-19: A review of results and recommendations

Wong

Thai

et al. 2020

Exp Biol Med (Maywood)

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The rapid and dramatic increase in confirmed cases of COVID-19 has led to a global pandemic. Early detection and containment are currently the most effective methods for controlling the outbreak. A positive diagnosis is determined by laboratory real-time reverse transcriptase polymerase chain reaction (rRT-PCR) testing, but the use of chest computed tomography (CT) has also been indicated as an important tool for detection and management of the disease. Numerous studies reviewed in this paper largely concur in their findings that the early hallmarks of COVID-19 infection are ground-glass opacities (GGOs), often with a bilateral and peripheral lung distribution. In addition, most studies demonstrated similar CT findings related to the progression of the disease, starting with GGOs in early disease, followed by the development of crazy paving in middle stages and finally increasing consolidation in the later stages of the disease. Studies have reported a low rate of misdiagnosis by chest CT, as well as a high rate of misdiagnosis by the rRT-PCR tests. Specifically, chest CT provides more accurate results in the early stages of COVID-19, when it is critical to begin treatment as well as isolate the patient to avoid the spread of the virus. While rRT-PCR will probably remain the definitive final test for COVID-19, until it is more readily available and can consistently provide higher sensitivity, the use of chest CT for early stage detection has proven valuable in avoiding misdiagnosis as well as monitoring the progression of the disease. With the understanding of the role of chest CT, researchers are beginning to apply deep learning and other algorithms to differentiate between COVID-19 and non-COVID-19 CT scans, determine the severity of the disease to guide the course of treatment, and investigate numerous additional COVID-19 applications. Impact statement The impact of the COVID-19 pandemic has been worldwide, and clinicians and researchers around the world have been working to develop effective and efficient methods for early detection as well as monitoring of the disease progression. This minireview compiles the various agency and expert recommendations, along with results from studies published in numerous countries, in an effort to facilitate the research in imaging technology development to benefit the detection and monitoring of COVID-19. To the best of our knowledge, this is the first review paper on the topic, and it provides a brief, yet comprehensive analysis.

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Section: Discussionmentioning

confidence: 99%

The role of chest computed tomography in the management of COVID-19: A review of results and recommendations

Wong

Thai

et al. 2020

Exp Biol Med (Maywood)

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“…The most important COVID-19 the 3C-like protease for which the crystal structure is known. Insilico Medicine is using a generative chemistry pipeline which is based on deep GANs to design novel drug-like inhib itors of COVID-19 and they have started the generation on 23th o f January 2020 [32]. The research done by Zhavoronkov et al [ 32] generated various kinds of chemicals.…”

Section: Drug Developmentmentioning

confidence: 99%

“…Insilico Medicine is using a generative chemistry pipeline which is based on deep GANs to design novel drug-like inhib itors of COVID-19 and they have started the generation on 23th o f January 2020 [32]. The research done by Zhavoronkov et al [ 32] generated various kinds of chemicals. They accessed the similarity of the structures of the different chemicals generated by the network fro m the ChEM BL database using its search engine.…”

Section: Drug Developmentmentioning

confidence: 99%

AI and IoT Solutions for Tackling COVID-19 Pandemic

Ghimire

Thapa

Jha

et al. 2020

2020 4th International Conference on Electronics, Communication and Aerospace Technology (ICECA)

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Coronavirus disease 2019 (COVID-19), caused by severe acute respiratory syndrome coronavirus 2 (SARS -CoV-2), has become an unprecedented public health crisis. To tackle this crisis in an effecti ve way di fferent computational soluti ons invol vi ng artificial intelligence and machine learning have been propounded by researchers across the worl d. Artificial Intelligence has changed the landscape of the healthcare industry and is being used by many corporations and g overnments around the worl d to tackle health care issues and hence, it finds applications in these troubli ng times as well. The internet speciall y google scholar scoured for relevant and accurate applications of machine learning and deep learning in sol vi ng the issues of this pandemic. The di fferent applications include diagnosis, mortality rate pre dic tion, vaccine de velopment, dr ug de velopme nt, sentiment analysis regarding COVID-19 comments and misinformation detection. A systematic study presents the best working models in the respective field.

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“… 29 VR frameworks for protein visualization, such as Nanome 30 and ProteinVR, 31 are being used for the SARS-CoV-2 Mpro. 32 , 33 While VR is undoubtedly a useful tool for visualizing complex structures in 3D, many such representations are static and do not include protein dynamics. Narupa, an open-source software framework, allows users to manipulate rigorous, physics-based atomistic MD simulations within a VR environment, a method which we call ‘interactive molecular dynamics in virtual reality’ (iMD-VR).…”

Section: Introductionmentioning

confidence: 99%

Interactive Molecular Dynamics in Virtual Reality Is an Effective Tool for Flexible Substrate and Inhibitor Docking to the SARS-CoV-2 Main Protease

Deeks

Walters

Barnoud

et al. 2020

J. Chem. Inf. Model.

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The main protease (Mpro) of the SARS-CoV-2 virus is one focus of drug development efforts for COVID-19. Here, we show that interactive molecular dynamics in virtual reality (iMD-VR) is a useful and effective tool for creating Mpro complexes. We make these tools and models freely available. iMD-VR provides an immersive environment in which users can interact with MD simulations and so build protein complexes in a physically rigorous and flexible way. Recently, we have demonstrated that iMD-VR is an effective method for interactive, flexible docking of small molecule drugs into their protein targets ( 32160194 PLoS One 2020 15 e0228461 ). Here, we apply this approach to both an Mpro inhibitor and an oligopeptide substrate, using experimentally determined crystal structures. For the oligopeptide, we test against a crystallographic structure of the original SARS Mpro. Docking with iMD-VR gives models in agreement with experimentally observed (crystal) structures. The docked structures are also tested in MD simulations and found to be stable. Different protocols for iMD-VR docking are explored, e.g., with and without restraints on protein backbone, and we provide recommendations for its use. We find that it is important for the user to focus on forming binding interactions, such as hydrogen bonds, and not to rely on using simple metrics (such as RMSD), in order to create realistic, stable complexes. We also test the use of apo (uncomplexed) crystal structures for docking and find that they can give good results. This is because of the flexibility and dynamic response allowed by the physically rigorous, atomically detailed simulation approach of iMD-VR. We make our models (and interactive simulations) freely available. The software framework that we use, Narupa, is open source, and uses commodity VR hardware, so these tools are readily accessible to the wider research community working on Mpro (and other COVID-19 targets). These should be widely useful in drug development, in education applications, e.g., on viral enzyme structure and function, and in scientific communication more generally.

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Potential Non-Covalent SARS-CoV-2 3C-like Protease Inhibitors Designed Using Generative Deep Learning Approaches and Reviewed by Human Medicinal Chemist in Virtual Reality

Cited by 28 publications

References 29 publications

The role of chest computed tomography in the management of COVID-19: A review of results and recommendations

The role of chest computed tomography in the management of COVID-19: A review of results and recommendations

AI and IoT Solutions for Tackling COVID-19 Pandemic

Interactive Molecular Dynamics in Virtual Reality Is an Effective Tool for Flexible Substrate and Inhibitor Docking to the SARS-CoV-2 Main Protease

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