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Machine-OlF-Action: a unified framework for developing and interpreting machine-learning models for chemosensory

Anku Gupta1, Mohit Choudhary1, Sanjay Kumar Mohanty2

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Machine Learning-based chemoinformatics tools are advancing drug discovery. We developed Machine-OlF-Action (MOA), a user-friendly framework to identify biologically relevant molecules for G-Protein Coupled Receptors (GPCRs) research.

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Area of Science:

  • Chemoinformatics
  • Computational Chemistry
  • Machine Learning in Drug Discovery

Background:

  • Machine learning (ML) offers advanced methods for identifying biologically relevant molecules in large databases.
  • Challenges in chemoinformatics include limited availability and high computational literacy requirements for ML techniques, particularly in G-Protein Coupled Receptors (GPCRs) research.
  • Existing methods often lack user-friendliness, hindering wider adoption in chemosensory research.

Purpose of the Study:

  • To develop a user-friendly, open-source computational framework, Machine-OlF-Action (MOA), for ML-based chemical compound analysis.
  • To facilitate the identification of biologically relevant molecules, particularly agonists and non-agonists for GPCRs.
  • To enhance the accessibility of advanced chemoinformatics tools for researchers.

Main Methods:

  • MOA utilizes user-supplied SMILES (simplified molecular input line entry system) strings and activation status to build classification models.
  • Integrates multiple chemical databases containing approximately 103 million chemical entities.
  • Employs the LIME (Local Interpretable Model-agnostic Explanations) framework for molecule embedding based on local neighborhood similarity.

Main Results:

  • Demonstrated MOA's utility in identifying novel agonists for human olfactory receptor OR1A1 and mouse olfactory receptor MOR174-9.
  • Successfully leveraged chemical features of known agonists and non-agonists to predict new active compounds.
  • Validated the framework's capability in performing supervised learning tasks on chemical datasets.

Conclusions:

  • Machine-OlF-Action (MOA) provides an accessible, ML-powered platform for supervised learning tasks involving chemical compounds.
  • The framework addresses the need for user-friendly tools in GPCR-associated chemosensory research.
  • MOA facilitates efficient and effective identification of biologically relevant molecules, accelerating drug discovery and research in chemosensory science.