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Siyun Yang1, Supratik Kar2

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Summary

Understanding the applicability domain (AD) is crucial in artificial intelligence (AI) and machine learning (ML). This chapter details AD definitions, methods, and applications in AI/ML and Quantitative Structure-Activity Relationship (QSAR) studies.

Keywords:
Applicability domainArtificial intelligenceMachine learningQSAR

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Area of Science:

  • Artificial Intelligence (AI)
  • Machine Learning (ML)
  • Computational Chemistry

Background:

  • The concept of Applicability Domain (AD) is essential for reliable AI and ML model interpretation.
  • Existing literature primarily focuses on AD within Quantitative Structure-Activity Relationship (QSAR) modeling.

Purpose of the Study:

  • To provide a comprehensive overview of AD in the context of AI and ML.
  • To explore various methodologies and measures for defining and assessing AD.
  • To highlight the diverse applications of AD across different scientific fields.

Main Methods:

  • Definition and theoretical foundations of AD.
  • Exploration of AD measures including DA index (κ, γ, δ), class probability estimation, local vicinity, boosting, classification neural networks, and subgroup discovery (SGD).
  • Review of AD methods specific to QSAR modeling.

Main Results:

  • Detailed examination of AD's role and theoretical underpinnings in AI/ML.
  • Identification of diverse quantitative and qualitative methods for AD assessment.
  • Discussion of the broad applicability of AD across various sectors.

Conclusions:

  • A thorough understanding of AD is vital for informed research and decision-making in AI and ML.
  • The methodologies discussed provide a framework for evaluating model reliability.
  • AD's application extends beyond QSAR, impacting broader AI/ML practices.