论文标题
COVID-19
Machine-Learning Driven Drug Repurposing for COVID-19
论文作者
论文摘要
将机器学习方法的整合到生物信息学中为确定一种在一种情况下有效的治疗方法在未知的临床背景下可能具有效用，从而为您提供了特殊的好处。我们旨在发现通过使用神经网络模型有效地对抗它们的病毒蛋白与抗病毒疗法之间的潜在关联。使用国家生物技术信息病毒蛋白数据库和药病毒数据库，该中心提供了广谱抗病毒剂（BSAAS）的全面报告，并抑制了病毒，我们培训了ANN模型，其病毒蛋白序列是输入和抗病毒剂，并将其视为安全的抗病毒剂，将其视为Humans-Humans as Touptas。模型训练不包括SARS-COV-2蛋白，仅包括II，III，IV和批准的水平药物。使用SARS-COV-2的序列（导致Covid-19的冠状病毒）作为对训练的模型的输入，可产生用于治疗Covid-19的暂定性安全抗病毒候选者的输出。我们的结果表明多个候选药物，其中一些补充了值得注意的临床研究的最新发现。我们对药物重新利用的智能方法有望确定其他病毒的新药物和治疗方法。
The integration of machine learning methods into bioinformatics provides particular benefits in identifying how therapeutics effective in one context might have utility in an unknown clinical context or against a novel pathology. We aim to discover the underlying associations between viral proteins and antiviral therapeutics that are effective against them by employing neural network models. Using the National Center for Biotechnology Information virus protein database and the DrugVirus database, which provides a comprehensive report of broad-spectrum antiviral agents (BSAAs) and viruses they inhibit, we trained ANN models with virus protein sequences as inputs and antiviral agents deemed safe-in-humans as outputs. Model training excluded SARS-CoV-2 proteins and included only Phases II, III, IV and Approved level drugs. Using sequences for SARS-CoV-2 (the coronavirus that causes COVID-19) as inputs to the trained models produces outputs of tentative safe-in-human antiviral candidates for treating COVID-19. Our results suggest multiple drug candidates, some of which complement recent findings from noteworthy clinical studies. Our in-silico approach to drug repurposing has promise in identifying new drug candidates and treatments for other viruses.
