Benchmarking explainable AI methods for toxicophore detection and toxicity prediction

Dina Khasanova; Igor V. Tetko

doi:10.1039/d5dd00576k

January 1, 2026Digital DiscoveryOpen Access

Benchmarking explainable AI methods for toxicophore detection and toxicity prediction

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Key Points

This research aims to evaluate the performance of explainable AI (XAI) methods in detecting toxicophores and predicting toxicity accurately.
Utilized high-accuracy models for toxicophore detection.
Assessed multiple XAI methods for stability and chemical relevance.
Identified functional groups across various prediction tasks.
High-accuracy models demonstrated stable XAI explanations.
Multiple XAI methods consistently identified toxicophores across prediction tasks.
Functional groups were accurately detected in toxicity prediction.

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Implication

Study benchmarks explainable AI methods for toxicity prediction, indicating stability and accuracy.

Abstract

High-accuracy models yield stable and chemically meaningful XAI explanations, with multiple methods consistently identifying functional groups and toxicophores across prediction tasks.

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Cite This Study

Khasanova et al. (2026) studied this question.

synapsesocial.com/papers/6a0809d7a487c87a6a40ba76 https://doi.org/10.1039/d5dd00576k

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Benchmarking explainable AI methods for toxicophore detection and toxicity prediction

Key Points

Ask about this paper

Implication

Abstract

Discussion

Cite This Study

Benchmarking explainable AI methods for toxicophore detection and toxicity prediction

Key Points

Ask about this paper

Implication

Abstract

Discussion

Cite This Study

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