Scholarlink

Using modern technologies such as machine learning (ML) and artificial intelligence (AI), we can accurately predict the biochemical properties of chemical compounds. The successful application of these approaches depends on both the chosen algorithms and the way of representing the molecular characteristics of the compounds, which can establish important biochemical and biophisics properties. The aim of the current research was to evaluate models based on molecular descriptors for predicting interactions with cannabinoid compounds. The models have demonstrated high accuracy for both regression and classification tasks. It is possible to use the characteristics obtained in this study for future research on cannabinoid compounds by focusing on key properties for compound optimization and the design of new drug molecules. Machine learning and artificial intelligence techniques are used as new approaches to support molecular docking and virtual screening of compounds. This study provides valuable information about the use of machine learning and artificial intelligence in drug design.

错误：搜索内容不能为空，请输入英文关键词
错误：关键词超出字数限制，请精简
高级检索

Intelligent Machine Learning Techniques for Advancing Modern Drug Discovery and Design

  • 会议论文

  • 发表日期：

    2025年

查看全文
  • Fatima Sapundzhi,
  • Metodi Popstoilov,
  • Slavi Georgiev,
  • Meglena Lazarova,
  • Venelin Todorov

摘要

Using modern technologies such as machine learning (ML) and artificial intelligence (AI), we can accurately predict the biochemical properties of chemical compounds. The successful application of these approaches depends on both the chosen algorithms and the way of representing the molecular characteristics of the compounds, which can establish important biochemical and biophisics properties. The aim of the current research was to evaluate models based on molecular descriptors for predicting interactions with cannabinoid compounds. The models have demonstrated high accuracy for both regression and classification tasks. It is possible to use the characteristics obtained in this study for future research on cannabinoid compounds by focusing on key properties for compound optimization and the design of new drug molecules. Machine learning and artificial intelligence techniques are used as new approaches to support molecular docking and virtual screening of compounds. This study provides valuable information about the use of machine learning and artificial intelligence in drug design.