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The similarity levels among different compounds are based on the considered feature set and the measures used to define the similarity. A comprehensive analysis of the effects of features and similarity definitions on identifying activity cliffs and the resultant impact on the data set modelability is warranted. The knowledge of activity cliffs may also be exploited to explore better the structural features responsible for compound potencies across different target sets. Further application of machine learning methods in the identification of activity cliffs is also recommended.

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Activity Cliffs and Dataset Modelability: Future Roadmaps

  • 章节

  • 发表日期：

    2025年

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  • Kunal Roy,
  • Arkaprava Banerjee

摘要

The similarity levels among different compounds are based on the considered feature set and the measures used to define the similarity. A comprehensive analysis of the effects of features and similarity definitions on identifying activity cliffs and the resultant impact on the data set modelability is warranted. The knowledge of activity cliffs may also be exploited to explore better the structural features responsible for compound potencies across different target sets. Further application of machine learning methods in the identification of activity cliffs is also recommended.