{"title":"利用可解释的机器学习模型了解药物概况预测","authors":"Caroline König, Alfredo Vellido","doi":"10.1186/s13040-024-00378-w","DOIUrl":null,"url":null,"abstract":"The analysis of absorption, distribution, metabolism, and excretion (ADME) molecular properties is of relevance to drug design, as they directly influence the drug’s effectiveness at its target location. This study concerns their prediction, using explainable Machine Learning (ML) models. The aim of the study is to find which molecular features are relevant to the prediction of the different ADME properties and measure their impact on the predictive model. The relative relevance of individual features for ADME activity is gauged by estimating feature importance in ML models’ predictions. Feature importance is calculated using feature permutation and the individual impact of features is measured by SHAP additive explanations. The study reveals the relevance of specific molecular descriptors for each ADME property and quantifies their impact on the ADME property prediction. The reported research illustrates how explainable ML models can provide detailed insights about the individual contributions of molecular features to the final prediction of an ADME property, as an effort to support experts in the process of drug candidate selection through a better understanding of the impact of molecular features.","PeriodicalId":48947,"journal":{"name":"Biodata Mining","volume":"45 1","pages":""},"PeriodicalIF":4.0000,"publicationDate":"2024-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Understanding predictions of drug profiles using explainable machine learning models\",\"authors\":\"Caroline König, Alfredo Vellido\",\"doi\":\"10.1186/s13040-024-00378-w\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"The analysis of absorption, distribution, metabolism, and excretion (ADME) molecular properties is of relevance to drug design, as they directly influence the drug’s effectiveness at its target location. This study concerns their prediction, using explainable Machine Learning (ML) models. The aim of the study is to find which molecular features are relevant to the prediction of the different ADME properties and measure their impact on the predictive model. The relative relevance of individual features for ADME activity is gauged by estimating feature importance in ML models’ predictions. Feature importance is calculated using feature permutation and the individual impact of features is measured by SHAP additive explanations. The study reveals the relevance of specific molecular descriptors for each ADME property and quantifies their impact on the ADME property prediction. The reported research illustrates how explainable ML models can provide detailed insights about the individual contributions of molecular features to the final prediction of an ADME property, as an effort to support experts in the process of drug candidate selection through a better understanding of the impact of molecular features.\",\"PeriodicalId\":48947,\"journal\":{\"name\":\"Biodata Mining\",\"volume\":\"45 1\",\"pages\":\"\"},\"PeriodicalIF\":4.0000,\"publicationDate\":\"2024-08-01\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Biodata Mining\",\"FirstCategoryId\":\"99\",\"ListUrlMain\":\"https://doi.org/10.1186/s13040-024-00378-w\",\"RegionNum\":3,\"RegionCategory\":\"生物学\",\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q1\",\"JCRName\":\"MATHEMATICAL & COMPUTATIONAL BIOLOGY\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Biodata Mining","FirstCategoryId":"99","ListUrlMain":"https://doi.org/10.1186/s13040-024-00378-w","RegionNum":3,"RegionCategory":"生物学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"MATHEMATICAL & COMPUTATIONAL BIOLOGY","Score":null,"Total":0}
引用次数: 0
摘要
吸收、分布、代谢和排泄(ADME)分子特性的分析与药物设计息息相关,因为它们直接影响药物在靶点的有效性。本研究利用可解释的机器学习(ML)模型对其进行预测。研究的目的是找出与预测不同 ADME 特性相关的分子特征,并衡量它们对预测模型的影响。通过估算特征在 ML 模型预测中的重要性来衡量各个特征与 ADME 活性的相对相关性。特征重要性通过特征排列来计算,特征的个体影响则通过 SHAP 相加解释来衡量。该研究揭示了特定分子描述符对每种 ADME 特性的相关性,并量化了它们对 ADME 特性预测的影响。所报告的研究说明了可解释的 ML 模型如何能够提供有关分子特征对 ADME 特性最终预测的个别贡献的详细见解,从而通过更好地了解分子特征的影响,在候选药物选择过程中为专家提供支持。
Understanding predictions of drug profiles using explainable machine learning models
The analysis of absorption, distribution, metabolism, and excretion (ADME) molecular properties is of relevance to drug design, as they directly influence the drug’s effectiveness at its target location. This study concerns their prediction, using explainable Machine Learning (ML) models. The aim of the study is to find which molecular features are relevant to the prediction of the different ADME properties and measure their impact on the predictive model. The relative relevance of individual features for ADME activity is gauged by estimating feature importance in ML models’ predictions. Feature importance is calculated using feature permutation and the individual impact of features is measured by SHAP additive explanations. The study reveals the relevance of specific molecular descriptors for each ADME property and quantifies their impact on the ADME property prediction. The reported research illustrates how explainable ML models can provide detailed insights about the individual contributions of molecular features to the final prediction of an ADME property, as an effort to support experts in the process of drug candidate selection through a better understanding of the impact of molecular features.
期刊介绍:
BioData Mining is an open access, open peer-reviewed journal encompassing research on all aspects of data mining applied to high-dimensional biological and biomedical data, focusing on computational aspects of knowledge discovery from large-scale genetic, transcriptomic, genomic, proteomic, and metabolomic data.
Topical areas include, but are not limited to:
-Development, evaluation, and application of novel data mining and machine learning algorithms.
-Adaptation, evaluation, and application of traditional data mining and machine learning algorithms.
-Open-source software for the application of data mining and machine learning algorithms.
-Design, development and integration of databases, software and web services for the storage, management, retrieval, and analysis of data from large scale studies.
-Pre-processing, post-processing, modeling, and interpretation of data mining and machine learning results for biological interpretation and knowledge discovery.