When machine learning models learn chemistry II: applying WISP to real-world examples

Kerrin Janssen; Jan M. Wollschläger; Jonny Proppe; Andreas H. Göller

doi:10.1039/d5dd00399g

January 1, 2026Digital DiscoveryOpen Access

When machine learning models learn chemistry II: applying WISP to real-world examples

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Key Points

The aim is to apply WISP to real-world chemistry problems to evaluate explainability methods in machine learning models.
Utilized WISP to assess explainability for various machine learning models in chemistry.
Identified matched molecular pairs for evaluation of model performance.
Conducted quantitative assessments to compare different explainability methods.
Demonstrated that WISP effectively quantifies the performance of explainability methods.
Showed improved understanding of machine learning model predictions in chemical contexts.

Implication

This work introduces WISP to evaluate explainability methods, enhancing machine learning applications in chemistry.

Abstract

In our previous work, we introduced WISP (Workflow for Interpretability Scoring using Matched Molecular Pairs), which enables users to quantitatively assess the performance of explainability methods for machine learning models.

Discussion

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Cite This Study

Janssen et al. (2026) studied this question.

synapsesocial.com/papers/698c1ca1267fb587c655f209 https://doi.org/10.1039/d5dd00399g

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When machine learning models learn chemistry II: applying WISP to real-world examples

Key Points

Implication

Abstract

Discussion

Cite This Study

When machine learning models learn chemistry II: applying WISP to real-world examples

Key Points

Implication

Abstract

Discussion

Cite This Study

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