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<p>The possibilities and limitations of applying machine learning methods to predict the antiviral activity of small molecules were described. Both classical and non-classical machine learning techniques were used to solve the binary classification problem in order to separate active and inactive molecules according to their biological activity values. Such predictive models could be used prior to the expected synthesis of molecules and evaluation of their biological potential, which undoubtedly is relevant for the development of new anti-influenza drugs.</p>

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Prediction of the small molecule selectivity index against influenza virus strain A/H1N1 using machine learning methods

  • 期刊论文

  • 发表日期：

    2025年3月1日

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  • A. D. Egorov,
  • Ya. V. Gorohov,
  • M. M. Kuznetsov,
  • S. S. Borisevich

摘要

The possibilities and limitations of applying machine learning methods to predict the antiviral activity of small molecules were described. Both classical and non-classical machine learning techniques were used to solve the binary classification problem in order to separate active and inactive molecules according to their biological activity values. Such predictive models could be used prior to the expected synthesis of molecules and evaluation of their biological potential, which undoubtedly is relevant for the development of new anti-influenza drugs.