[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-125936-en":3,"doc-seo-125936-105":31,"detail-sidebar-cat-0-en-105":84},{"code":4,"msg":5,"data":6},0,"success",{"doc_id":7,"user_id":8,"nickname":9,"user_avatar":10,"doc_module":4,"category_id":11,"category_name":12,"doc_title":13,"doc_description":14,"doc_content":15,"file_id":16,"file_url":17,"file_type":18,"file_size":19,"view_count":20,"is_deleted":4,"is_public":21,"is_downloadable":21,"audit_status":21,"page_count":22,"language":23,"language_code":24,"site_id":25,"html_lang":24,"table_of_contents":26,"faqs":27,"seo_title":28,"seo_description":14,"update_tm":29,"read_time":30},125936,2336474459895,"Aria","https://ap-avatar.wpscdn.com/avatar/22000baeef7a5ed0655?x-image-process=image/resize,m_fixed,w_180,h_180&k=1786071322749376916",8,"Research & Report","Understanding predictions of drug profiles using explainable machine learning models - Research Open Access","The analysis of absorption, distribution, metabolism, and excretion (ADME) molecular properties underpins drug design because these properties shape effectiveness at target sites. The study predicts ADME properties with explainable machine learning models, identifying which molecular features matter most and how strongly they influence each prediction. Feature relevance is assessed via permutation-based importance and quantified through SHAP additive explanations, delivering interpretable contributions. Findings highlight key molecular descriptors per ADME property and support more transparent drug-candidate selection.","König and Vellido BioData Mining (2024) 17:25 [https://doi.org/10.1186/s13040-024-00378-w](https://doi.org/10.1186/s13040-024-00378-w)  \nBioData Mining  \nRESEARCH Open Access  \nUnderstanding predictions of drug profiles   using explainable machine learning models  \nCaroline König1,2* and Alfredo Vellido1,2  \n*Correspondence: [ckonig@cs.upc.edu](ckonig@cs.upc.edu)  \n1 Intelligent Data Science and Artificial Intelligence (IDEAI-UPC) Research Centre, Universitat Politècnica de Catalunya (UPC Barcelona Tech), Jordi Girona 1-3, Barcelona 08034, Catalonia, Spain  \n2 Department of Computer Science, Universitat Politècnica de Catalunya (UPC Barcelona Tech), Jordi Girona 1-3, Barcelona 08034, Catalonia, Spain  \nAbstract  \nPurpose: 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.  \nMethods: 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.  \nResults: The study reveals the relevance of specific molecular descriptors for each ADME property and quantifies their impact on the ADME property prediction. Conclusion: 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.  \nKeywords: ADME properties, Explainable machine learning, Molecular descriptors, Drug design  \nIntroduction  \nThe analysis of Absorption, Distribution, Metabolism, and Excretion (ADME) properties is of great interest in early drug design as they directly determine the drug’s effectiveness at its target location. Over the last decades, significant progress has been made in developing machine learning (ML)-based predictive models for quantitative structure-activity relationship (QSAR) [1, 2], in general with important contributions to ADME property prediction [3–5].  \nThe availability of publicly accessible experimental data is paramount for the advancesin such ML-based QSAR prediction for ADME properties [6–9]. From an ML point of view, the prediction of chemical properties can be accomplished with either conventional methods, which use a fixed-size feature representation, usually calculated from  \n© The Author(s) 2024. Open Access This article is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License, which permits any non-commercial use, sharing, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if you modified the licensed material. You do not have permission under this licence to share adapted material derived from this article or parts of it. The images or other third party material in this article are included in the article’s Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit [http://creativecommons.org/licenses/by-nc-nd/4.0/](http://creativecommons.org/licenses/by-nc-nd/4.0/.)[.](h","cbCaibsUeSM5Kte7","https://ap.wps.com/l/cbCaibsUeSM5Kte7","pdf",3578026,3,1,25,"English","en",105,"# Abstract\n## Purpose\n## Methods\n## Results\n## Conclusion\n# Introduction","[{\"question\":\"What do the results reveal about molecular descriptors?\",\"answer\":\"The results identify specific molecular descriptors that are relevant to each ADME property. They also quantify how strongly those descriptors affect predictions, linking model reasoning to molecular characteristics.\"}]","Understanding predictions of drug profiles using explainable machine learning models - Research Open Access | PDF",1785902122,63,{"code":4,"msg":32,"data":33},"ok",{"site_id":25,"language":24,"slug":34,"title":13,"keywords":35,"description":14,"schema_data":36,"social_meta":79,"head_meta":81,"extra_data":83,"updated_unix":29},"understanding-predictions-of-drug-profiles-using-explainable-machine-learning-models-research-open-access","",{"@graph":37,"@context":78},[38,54,69],{"@type":39,"itemListElement":40},"BreadcrumbList",[41,45,49,51],{"item":42,"name":43,"@type":44,"position":21},"https://docshare.wps.com","Home","ListItem",{"item":46,"name":47,"@type":44,"position":48},"https://docshare.wps.com/document/","Document",2,{"item":50,"name":12,"@type":44,"position":20},"https://docshare.wps.com/document/research-report/",{"item":52,"name":13,"@type":44,"position":53},"https://docshare.wps.com/document/understanding-predictions-of-drug-profiles-using-explainable-machine-learning-models-research-open-access/125936/",4,{"url":52,"name":13,"@type":55,"author":56,"headline":13,"publisher":58,"fileFormat":61,"inLanguage":24,"description":14,"dateModified":62,"datePublished":63,"encodingFormat":61,"isAccessibleForFree":64,"interactionStatistic":65},"DigitalDocument",{"name":9,"@type":57},"Person",{"url":42,"name":59,"@type":60},"DocShare","Organization","application/pdf","2026-08-23","2026-08-05",true,{"@type":66,"interactionType":67,"userInteractionCount":20},"InteractionCounter",{"@type":68},"ViewAction",{"@type":70,"mainEntity":71},"FAQPage",[72],{"name":73,"@type":74,"acceptedAnswer":75},"What do the results reveal about molecular descriptors?","Question",{"text":76,"@type":77},"The results identify specific molecular descriptors that are relevant to each ADME property. 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