ArticleBMC chemistry2021
De novo design and bioactivity prediction of SARS-CoV-2 main protease inhibitors using recurrent neural network-based transfer learning.
Article in BMC chemistry, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 26 papers.
What it found
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Who cites it
26 citing papers in PubMed, 74 citations in OpenAlex.
- Developing muscarinic receptor M1 classification models utilizing transfer learning and generative AI techniques.Scientific reports · 2025Article
- Screening of multi deep learning-based de novo molecular generation models and their application for specific target molecular generation.Scientific reports · 2025Article
- A systematic review of deep learning chemical language models in recent era.Journal of cheminformatics · 2024Review
- Hybrid deep learning technique for COX-2 inhibition bioactivity detection against breast cancer disease.Biomedical engineering letters · 2024Article
- Identification of SARS-CoV-2 Mpro inhibitors through deep reinforcement learning forRSC medicinal chemistry · 2024Article
- Machine learning combines atomistic simulations to predict SARS-CoV-2 Mpro inhibitors from natural compounds.Molecular diversity · 2024Article
- Cluster energy prediction based on multiple strategy fusion whale optimization algorithm and light gradient boosting machine.BMC chemistry · 2024Article
- VGAE-MCTS: A New Molecular Generative Model Combining the Variational Graph Auto-Encoder and Monte Carlo Tree Search.Journal of chemical information and modeling · 2023Article
- Staying Ahead of the Game: How SARS-CoV-2 has Accelerated the Application of Machine Learning in Pandemic Management.BioDrugs : clinical immunotherapeutics, biopharmaceuticals and gene therapy · 2023Review
- Practical guidelines for the use of gradient boosting for molecular property prediction.Journal of cheminformatics · 2023Article
- In Silico Identification of Potential Inhibitors of the SARS-CoV-2 Main Protease among a PubChem Database of Avian Infectious Bronchitis Virus 3CLPro Inhibitors.Biomolecules · 2023Article
- Magicmol: a light-weighted pipeline for drug-like molecule evolution and quick chemical space exploration.BMC bioinformatics · 2023Article
- Discovery of Potential Inhibitors of SARS-CoV-2 Main Protease by a Transfer Learning Method.Viruses · 2023Article
- Bio-activity prediction of drug candidate compounds targeting SARS-Cov-2 using machine learning approaches.PloS one · 2023Article
- ReMODE: a deep learning-based web server for target-specific drug design.Journal of cheminformatics · 2022Article
- Conditional reduction of the loss value versus reinforcement learning for biassing a de-novo drug design generator.Journal of cheminformatics · 2022Article
- A transfer learning approach for reaction discovery in small data situations using generative model.iScience · 2022Article
- How can natural language processing help model informed drug development?: a review.JAMIA open · 2022Review
- Machine learning prediction of 3CLComputational biology and chemistry · 2022Article
- Machine learning assessment of the binding region as a tool for more efficient computational receptor-ligand docking.Journal of molecular liquids · 2022Article
Corrections and comments
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Authors and funding
2 authors at 1 institution in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
The global pandemic of coronavirus disease (COVID-19) caused by SARS-CoV-2 (severe acute respiratory syndrome coronavirus 2) created a rush to discover drug candidates. Despite the efforts, so far no vaccine or drug has been approved for treatment. Artificial intelligence offers solutions that could accelerate the discovery and optimization of new antivirals, especially in the current scenario dominated by the scarcity of compounds active against SARS-CoV-2. The main protease (M
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.