ArticleScientific reports2021
Potential neutralizing antibodies discovered for novel corona virus using machine learning.
Article in Scientific reports, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 45 papers, 1 of them a synthesis that pooled it.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
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
45 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Emerging role of artificial intelligence in therapeutics for COVID-19: a systematic review.Journal of biomolecular structure & dynamics · 2022Pooled it
- CBInformax: bioactivity-aware self-supervised molecular representation learning for molecular property and drug-drug interaction prediction.Molecular diversity · 2026Article
- Leveraging a Meta-Learning Strategy to Advance the Accuracy of Neutralizing Antibodies against Dengue Virus Serotype Prediction.ACS omega · 2025Article
- Accurate identification of broadly neutralizing antibodies against dengue virus based on deep stacking strategy with multi-perspective features.Scientific reports · 2025Article
- AbSet: A Standardized Data Set of Antibody Structures for Machine Learning Applications.Journal of chemical information and modeling · 2025Article
- Prediction of blood-brain barrier and Caco-2 permeability through the Enalos Cloud Platform: combining contrastive learning and atom-attention message passing neural networks.Journal of cheminformatics · 2025Article
- Nanobody screening and machine learning guided identification of cross-variant anti-SARS-CoV-2 neutralizing heavy-chain only antibodies.PLoS pathogens · 2025Article
- NABP-BERT: NANOBODY®-antigen binding prediction based on bidirectional encoder representations from transformers (BERT) architecture.Briefings in bioinformatics · 2024Article
- Machine-learning-assisted high-throughput identification of potent and stable neutralizing antibodies against all four dengue virus serotypes.Scientific reports · 2024Article
- A perspective-driven and technical evaluation of machine learning in bioreactor scale-up: A case-study for potential model developments.Engineering in life sciences · 2024Article
- Sustainable integration of artificial intelligence and machine learning approaches within the African infectious disease vaccine research and development ecosystem.Frontiers in pharmacology · 2024Article
- Position-Specific Enrichment Ratio Matrix scores predict antibody variant properties from deep sequencing data.Bioinformatics (Oxford, England) · 2023Article
- Simple and Powerful PCG Classification Method Based on Selection and Transfer Learning for Precision Medicine Application.Bioengineering (Basel, Switzerland) · 2023Article
- Integrated Approach to Interaction Studies of Pyrene Derivatives with Bovine Serum Albumin: Insights from Theory and Experiment.The journal of physical chemistry. B · 2022Article
- Forecasting COVID-19 new cases using deep learning methods.Computers in biology and medicine · 2022Article
- COVID-19 Diagnosis: A Review of Rapid Antigen, RT-PCR and Artificial Intelligence Methods.Bioengineering (Basel, Switzerland) · 2022Review
- Researchers turn to deep learning to decode protein structures.Proceedings of the National Academy of Sciences of the United States of America · 2022Article
- AI in Combating the COVID-19 Pandemic.IEEE intelligent systems · 2022Article
- Fusion of AI techniques to tackle COVID-19 pandemic: models, incidence rates, and future trends.Multimedia systems · 2022Article
- A contemporary review on the important role ofInformatics in medicine unlocked · 2022Review
Corrections and comments
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Authors and funding
3 authors.
Funding
Abstract
The fast and untraceable virus mutations take lives of thousands of people before the immune system can produce the inhibitory antibody. The recent outbreak of COVID-19 infected and killed thousands of people in the world. Rapid methods in finding peptides or antibody sequences that can inhibit the viral epitopes of SARS-CoV-2 will save the life of thousands. To predict neutralizing antibodies for SARS-CoV-2 in a high-throughput manner, in this paper, we use different machine learning (ML) model to predict the possible inhibitory synthetic antibodies for SARS-CoV-2. We collected 1933 virus-antibody sequences and their clinical patient neutralization response and trained an ML model to predict the antibody response. Using graph featurization with variety of ML methods, like XGBoost, Random Forest, Multilayered Perceptron, Support Vector Machine and Logistic Regression, we screened thousands of hypothetical antibody sequences and found nine stable antibodies that potentially inhibit SARS-CoV-2. We combined bioinformatics, structural biology, and Molecular Dynamics (MD) simulations to verify the stability of the candidate antibodies that can inhibit SARS-CoV-2.
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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.