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Trustworthiness, the Key to Grid-Based Map-Driven Predictive Model Enhancement and Applicability Domain Control.

Dragos Horvath¹, Gilles Marcou¹, Alexandre Varnek¹

¹Laboratory of Chemoinformatics, UMR 7140 University of Strasbourg/CNRS, 4 rue Blaise Pascal, 67000 Strasbourg, France.

Journal of Chemical Information and Modeling

|November 11, 2020

Summary

This study introduces a node trustworthiness (NT) function to assess the reliability of grid-based maps in cheminformatics. A generalizable method for defining applicability domains (AD) was developed, improving structure-activity relationship predictions.

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Area of Science:

Cheminformatics
Computational Chemistry
Machine Learning

Background:

Grid-based maps like Self-Organizing Maps (SOMs) and Generative Topographic Maps (GTMs) are used in cheminformatics to visualize molecular descriptor space.
These maps predict compound properties based on node inheritance from training data.
Assessing the reliability of these predictions and defining the applicability domain (AD) is crucial.

Purpose of the Study:

To propose a formalism for defining the trustworthiness of nodes in grid-based maps for structure-activity relationship (SAR) information.
To develop a quantitative method for establishing an applicability domain (AD) based on node trustworthiness.
To demonstrate the general applicability of the proposed AD definition across different prediction tasks and map types.

Main Methods:

Developed a four-parameter node trustworthiness (NT) function considering node density and coherence.
Introduced a trustworthiness score (T) and threshold (TT) to define the applicability domain (AD).
Monitored prediction success rates within the defined AD across various regression and classification problems.

Main Results:

Node trustworthiness (NT) and applicability domain (AD) definitions were shown to be effective in assessing prediction reliability.
Prediction success levels within the AD were highly covariant across different targets, problem types (classification/regression), and map types (GTM/SOM).
Success levels from regression problems on GTMs correlated strongly (70%) with those from unrelated classification problems on SOMs.

Conclusions:

The proposed parametric AD definition, based on node trustworthiness, offers a common and general-purpose approach for grid-based map-driven property prediction.
This method enhances the reliability and interpretability of predictions in cheminformatics.
The findings suggest a universal applicability of the trustworthiness concept for defining AD in various machine learning contexts within chemistry.