Regional Explanations and Diverse Molecular Representations in Cheminformatics: A Comparative Study
In cheminformatics, the explainability of machine learning models is important for interpreting complex chemical data, deriving new chemical insights, and building trust in predictive models. However, cheminformatics datasets often exhibit clustered distributions, while traditional explanation metho...
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| Main Authors: | , , |
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| Format: | Article |
| Language: | English |
| Published: |
American Association for the Advancement of Science (AAAS)
2025-01-01
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| Series: | Intelligent Computing |
| Online Access: | https://spj.science.org/doi/10.34133/icomputing.0126 |
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