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Updated: Jun 1, 2025

A Mass Spectrometry-Based Approach to Identify Phosphoprotein Phosphatases and their Interactors

A Mass Spectrometry-Based Approach to Identify Phosphoprotein Phosphatases and their Interactors

Published on: April 29, 2022

Rationalizing Predictions of Isoform-Selective Phosphoinositide 3-Kinase Inhibitors Using MolAnchor Analysis.

Alec Lamens^1,2, Jürgen Bajorath^1,2

¹Department of Life Science Informatics and Data Science, B-IT, LIMES Program Unit Chemical Biology and Medicinal Chemistry, Rheinische Friedrich-Wilhelms-Universität, Friedrich-Hirzebruch-Allee 5/6, Bonn D-53115, Germany.

Journal of Chemical Information and Modeling

|January 17, 2025

Summary

This summary is machine-generated.

MolAnchor, a new explainable artificial intelligence method, identifies key chemical fragments for predicting phosphoinositide 3-kinase (PI3K) inhibitor selectivity. This approach provides chemically intuitive explanations, aiding drug discovery by revealing causal relationships between molecular structures and target selectivity.

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Area of Science:

Medicinal Chemistry
Computational Chemistry
Artificial Intelligence in Drug Discovery

Background:

Explaining machine learning model predictions is crucial for their adoption in drug discovery.
Phosphoinositide 3-kinase (PI3K) inhibitors are important therapeutic agents, but achieving isoform selectivity is challenging.

Purpose of the Study:

To develop and validate a novel methodology, MolAnchor, for generating chemically intuitive explanations of machine learning predictions for PI3K inhibitor isoform selectivity.
To identify specific structural fragments responsible for predicting inhibitor selectivity.

Main Methods:

Generation of a test system for predicting PI3K inhibitor isoform selectivity.
Systematic analysis of correct predictions using the MolAnchor methodology, based on explainable artificial intelligence "anchors" concept.
Comparison of MolAnchor explanations with feature importance values from other methods.

Main Results:

MolAnchor successfully identified well-defined structural fragments, often a single substructure, responsible for predicting isoform selectivity in most cases.
Distinct recurrent substructures were found for inhibitors with different isoform selectivities.
MolAnchor explanations demonstrated superior interpretability compared to feature importance values.
Two recurrent substructures were directly linked to PI3K isoform selectivity, suggesting a causal relationship.

Conclusions:

The MolAnchor methodology provides chemically intuitive and interpretable explanations for machine learning predictions in drug discovery.
Identifying specific substructures linked to selectivity can guide the design of more selective PI3K inhibitors.
This approach enhances the integration of predictive modeling in drug discovery projects by elucidating the basis of compound activity and selectivity.