Selected article for: "learning model and machine learning model"
Author: Rishikesh Magar; Prakarsh Yadav; Amir Barati Farimani
Title: Potential Neutralizing Antibodies Discovered for Novel Corona Virus Using Machine Learning
  • Document date: 2020_3_20
    • ID: fn7l93wh_21
    Snippet: We have developed a machine learning model for high throughput screening of synthetic antibodies to discover antibodies that potentially inhibit the COVID-19. Our approach can be widely applied to other viruses where only the sequences of viral coat protein-antibody pairs can be obtained. The ML models were trained on 14 different virus types and achieved over 90% fivefold test accuracy. The out of class prediction is 100% for SARS and 84.61% for.....
    Document: We have developed a machine learning model for high throughput screening of synthetic antibodies to discover antibodies that potentially inhibit the COVID-19. Our approach can be widely applied to other viruses where only the sequences of viral coat protein-antibody pairs can be obtained. The ML models were trained on 14 different virus types and achieved over 90% fivefold test accuracy. The out of class prediction is 100% for SARS and 84.61% for Influenza, demonstrating the power of our model for neutralization prediction of antibodies for novel viruses like COVID-19. Using this model, the neutralization of thousands of hypothetical antibodies was predicted, and 18 antibodies were found to be highly efficient in neutralizing COVID-19. Using MD simulations, the stability of predicted antibodies were checked and 8 stable antibodies were found that can neutralize COVID-19.

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