ArticleJournal of healthcare engineering2021
Deep Learning Approach for Discovery of In Silico Drugs for Combating COVID-19.
Article in Journal of healthcare engineering, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 10 papers, 1 of them a synthesis that pooled it.
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Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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Who cites it
10 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Combating COVID-19 Crisis using Artificial Intelligence (AI) Based Approach: Systematic Review.Current topics in medicinal chemistry · 2024Pooled it
- Smiles-based bioactivity prediction through molecular encoder selection and data augmentation.Journal of cheminformatics · 2026Article
- Deep learning model for diagnosing lupus erythematosus in cardiac patients using ECG and audio spectrograms.Scientific reports · 2025Article
- Drug repurposing targeting COVID-19 3CL protease using molecular docking and machine learning regression approaches.Scientific reports · 2025Article
- Article
- Bio-activity prediction of drug candidate compounds targeting SARS-Cov-2 using machine learning approaches.PloS one · 2023Article
- Hybrid Approach to Identifying Druglikeness Leading Compounds against COVID-19 3CL Protease.Pharmaceuticals (Basel, Switzerland) · 2022Article
- Automated detection of COVID-19 cases from chest X-ray images using deep neural network and XGBoost.Radiography (London, England : 1995) · 2022Article
- COV-ADSX: An Automated Detection System using X-ray Images, Deep Learning, and XGBoost for COVID-19.Software impacts · 2022Article
- A Promising and Challenging Approach: Radiologists' Perspective on Deep Learning and Artificial Intelligence for Fighting COVID-19.Diagnostics (Basel, Switzerland) · 2021Review
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
8 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Early diagnosis of pandemic diseases such as COVID-19 can prove beneficial in dealing with difficult situations and helping radiologists and other experts manage staffing more effectively. The application of deep learning techniques for genetics, microscopy, and drug discovery has created a global impact. It can enhance and speed up the process of medical research and development of vaccines, which is required for pandemics such as COVID-19. However, current drugs such as remdesivir and clinical trials of other chemical compounds have not shown many impressive results. Therefore, it can take more time to provide effective treatment or drugs. In this paper, a deep learning approach based on logistic regression, SVM, Random Forest, and QSAR modeling is suggested. QSAR modeling is done to find the drug targets with protein interaction along with the calculation of binding affinities. Then deep learning models were used for training the molecular descriptor dataset for the robust discovery of drugs and feature extraction for combating COVID-19. Results have shown more significant binding affinities (greater than -18) for many molecules that can be used to block the multiplication of SARS-CoV-2, responsible for COVID-19.
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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.