A transferable deep learning approach to fast screen potential antiviral drugs against SARS-CoV-2
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Abstract
The COVID-19 pandemic calls for rapid development of effective treatments. Although various drug repurpose approaches have been used to screen the FDA-approved drugs and drug candidates in clinical phases against SARS-CoV-2, the coronavirus that causes this disease, no magic bullets have been found until now. In this study, we used directed message passing neural network to first build a broad-spectrum anti-beta-coronavirus compound prediction model, which gave satisfactory predictions on newly reported active compounds against SARS-CoV-2. Then, we applied transfer learning to fine-tune the model with the recently reported anti-SARS-CoV-2 compounds and derived a SARS-CoV-2 specific prediction model COVIDVS-3. We used COVIDVS-3 to screen a large compound library with 4.9 million drug-like molecules from ZINC15 database and recommended a list of potential anti-SARS-CoV-2 compounds for further experimental testing. As a proof-of-concept, we experimentally tested seven high-scored compounds that also demonstrated good binding strength in docking studies against the 3C-like protease of SARS-CoV-2 and found one novel compound that can inhibit the enzyme. Our model is highly efficient and can be used to screen large compound databases with millions or more compounds to accelerate the drug discovery process for the treatment of COVID-19.
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SciScore for 10.1101/2020.08.28.271569: (What is this?)
Please note, not all rigor criteria are appropriate for all manuscripts.
Table 1: Rigor
NIH rigor criteria are not applicable to paper type.Table 2: Resources
Software and Algorithms Sentences Resources Clustering process was performed with Python 3.7 and scikit-learn’s default parameters except those mentioned before. Pythonsuggested: (IPython, RRID:SCR_001658)Results from OddPub: We did not detect open data. We also did not detect open code. Researchers are encouraged to share open data when possible (see Nature blog).
Results from LimitationRecognizer: We detected the following sentences addressing limitations in the study:Due to experimental limitations, we were unable to test our predicted compounds on SARS-CoV-2 directly in our own laboratory. As an alternative experimental …
SciScore for 10.1101/2020.08.28.271569: (What is this?)
Please note, not all rigor criteria are appropriate for all manuscripts.
Table 1: Rigor
NIH rigor criteria are not applicable to paper type.Table 2: Resources
Software and Algorithms Sentences Resources Clustering process was performed with Python 3.7 and scikit-learn’s default parameters except those mentioned before. Pythonsuggested: (IPython, RRID:SCR_001658)Results from OddPub: We did not detect open data. We also did not detect open code. Researchers are encouraged to share open data when possible (see Nature blog).
Results from LimitationRecognizer: We detected the following sentences addressing limitations in the study:Due to experimental limitations, we were unable to test our predicted compounds on SARS-CoV-2 directly in our own laboratory. As an alternative experimental validation, we used our COVIDVS prediction together with protein-ligand docking to screen for potential SARS-CoV-2 3CLpro inhibitors. We performed docking of the 3,641 top-ranking compounds instead of the 4.9 million drug-like molecules from ZINC15 database and identified a new SARS-CoV-2 3CLpro inhibitor with novel chemical scaffold, which can be further optimized. Although many 3CLpro inhibitors have been reported, most of them only showed activity in in vitro enzyme assay. As our COVIDVS models were trained with antiviral activity data, compounds with in vitro 3CLpro inhibition activity and good COVIDVS prediction scores may have high probability of anti-viral activity. Similar to COVIDVS-2 and 3, a target-specific models for 3CLpro can be trained by fine-tuning COVIDVS-1 with known 3CLpro inhibitors and non-inhibitors, which is expected to increase the success rate of prediction.
Results from TrialIdentifier: No clinical trial numbers were referenced.
Results from Barzooka: We did not find any issues relating to the usage of bar graphs.
Results from JetFighter: We did not find any issues relating to colormaps.
Results from rtransparent:- Thank you for including a conflict of interest statement. Authors are encouraged to include this statement when submitting to a journal.
- Thank you for including a funding statement. Authors are encouraged to include this statement when submitting to a journal.
- No protocol registration statement was detected.
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