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Explainable Fragment-Based Molecular Property Attribution
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DOI：10.1002/aisy.202200104.png)
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摘要
En 中文
AI & Drug Discovery mode has significantly promoted drug development and achieved excellent performance, especially with the rapid development of deep learning, making remarkable contributions to protecting human physiological health. However, due to the black-box characteristic of the deep learning model, the decision route and predicted results in different research stages assisted by deep models are usually unexplainable, limiting their application in practice and more in-depth research of drug discovery. Focusing on the drug molecules, an explainable fragment-based molecular property attribution technique is proposed for analyzing the influence of particular molecule fragments on properties and the relationship between the molecular properties herein. Quantitative experiments on 42 benchmark property tasks demonstrate that 325 attribution fragments, which account for 90% of the overall attribution results obtained by the proposed method, have positive relevance to the corresponding property tasks. More impressively, most of the attribution results randomly selected are consistent with the existing mechanism explanations. The discovery mentioned above provides a reference standard for assisting researchers in developing more specific and practical drug molecule studies, such as synthesizing molecule with the targeted property using a fragment obtained from the attribution method. An interactive preprint version of the article can be found at: https://www.authorea.com/doi/full/10.22541/au.165279262.29589148.
Keyword:
drug discovery
explainable machine learning
molecular properties
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6.1
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2.0K
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