[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-119047-en":3,"doc-seo-119047-105":30,"detail-sidebar-cat-0-en-105":91},{"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":4,"is_deleted":4,"is_public":20,"is_downloadable":20,"audit_status":20,"page_count":21,"language":22,"language_code":23,"site_id":24,"html_lang":23,"table_of_contents":25,"faqs":26,"seo_title":27,"seo_description":14,"update_tm":28,"read_time":29},119047,137441390410,"Hazel","https://ap-avatar.wpscdn.com/avatar/2000252f4ab5702993?_k=1776741390130283984",8,"Research & Report","Machine Learning-Powered Prediction of molecule Solubility - Paving the Way for environmental and energy applications","Aqueous solubility prediction is essential for selecting materials in pharmaceuticals, environmental science, and renewable energy. Solubility strongly influences drug development, including chemical and synthetic route planning, and determines bioavailability and efficacy. Conventional experimental assays are labor-intensive, while many computational methods struggle with accuracy and scalability. This study applies machine learning to predict molecular solubility using PyCaret, training and comparing multiple linear regression models with metrics including R², RMSLE, MAE, MSE, MAPE, and RMSE. Results show the effectiveness of ML in cheminformatics and clarify how molecular features relate to solubility for more efficient, lower-cost discovery.","Machine Learning-Powered Prediction of molecule Solubility: Paving the Way for environmental, and energy applications  \nImane Aitouhanni1, Yassine Mouniane2, *, and Amine Berqia1  \n1Mohammed V University in Rabat, ENSIAS, SSLAB, Rabat, Morocco  \n2Natural Resources and Sustainable Development laboratory, Faculty of Sciences, Ibn Tofail  \nUniversity, B.P 242, Kenitra, Morocco  \nAbstract. Predicting aqueous solubility is pivotal for selecting materials in  \npharmaceuticals, environmental, and renewable energy fields. For instance,  \nit plays a vital role in drug development and the design of chemical and  \nsynthetic routes. In the realm ofCheminformatics, the accurate prediction of  \nmolecule solubility is indispensable for drug discovery and development.  \nTraditional methods often rely on labor-intensive experimental assays,  \npresenting challenges in terms of time and cost. To address these limitations,  \nthis study leverages advanced machine learning techniques to predict  \nmolecule solubility with exceptional accuracy. Using the PyCaret library, a  \nversatile low-code machine learning tool, we develop and evaluate a diverse  \nset of linear regression models. Key performance metrics, including R²,  \nRMSLE, MAE, MSE, MAPE, and RMSE, are employed to assess model  \nperformance comprehensively. Through rigorous model comparison and  \nevaluation, we identify the optimal model for predicting molecule solubility.  \nOur findings not only demonstrate the efficacy of machine learning in  \nCheminformatics but also offer insights into the complex relationship  \nbetween molecular features and solubility. This study contributes to the  \nadvancement of computational chemistry by bridging the gap between  \ntheory and practice. By elucidating the predictive capabilities of machine  \nlearning models, we pave the way for more efficient and cost-effective drug  \ndiscovery processes.  \n1 Introcuction  \nThe solubility of molecules in water is a critical factor affecting the effectiveness of numerous applications. Anticipating aqueous solubility is essential for material selection across pharmaceuticals, environmental, and renewable energy sectors. Notably, it is integral to drug development as well as the planning of chemical and synthetic pathways [1] . In the domain of Cheminformatics, a convergence of computational techniques and chemical sciences, the quest for enhancing our understanding of the critical physicochemical properties in drug discovery and development has taken center stage [2] . Among these properties, the solubility of molecules stands out as a pivotal determinant of their bioavailability and efficacy  \n* [Corresponding author: ](Corresponding author: yassine.mouniane@uit.ac.ma)[yassine.mouniane@uit.ac.ma](Corresponding author: yassine.mouniane@uit.ac.ma)  \n© The Authors, published by EDP Sciences. This is an open access article distributed under the terms of the Creative Commons Attribution License 4.0 ([https://creativecommons.org/licenses/by/4.0/](https://creativecommons.org/licenses/by/4.0/)).  \n[3]. The ability to accurately predict molecule solubility is therefore of paramount importance in pharmaceutical research and development. However, despite significant advancements in computational chemistry and machine learning, predicting molecule solubility remains a formidable challenge due to the complex interplay of molecular structure and environmental factors. Traditional methods often rely on labor-intensive experimental assays, which are time-consuming and costly. Moreover, existing computational approaches may lack the accuracy and scalability required for real-world applications.  \nTo address these challenges, this study embarks on a journey to harness the power of advanced machine learning techniques in predicting molecule solubility. Motivated by the ambition to reproduce linear regression models with exceptional performance, we leverage the capabilities of PyCaret [4], a versatile low-code machine learning library tha","cbCaitSqdQUcT0fu","https://ap.wps.com/l/cbCaitSqdQUcT0fu","pdf",564311,1,10,"English","en",105,"# Introduction\n## Motivation and background\n## Challenges of traditional and computational methods\n## Study approach and tools","[{\"question\":\"Why is predicting aqueous solubility important across applications?\",\"answer\":\"Aqueous solubility is critical for material selection in pharmaceuticals, environmental work, and renewable energy. It also plays a key role in drug development and designing chemical and synthetic pathways.\"},{\"question\":\"What limitations do traditional and existing computational methods face?\",\"answer\":\"Traditional approaches often rely on labor-intensive experiments that are time-consuming and costly. Existing computational methods may not achieve the accuracy and scalability needed for real-world use due to the complex link between molecular structure and environmental factors.\"},{\"question\":\"How does the study predict molecule solubility and evaluate model performance?\",\"answer\":\"The work uses PyCaret to build and compare multiple linear regression models. Performance is assessed using metrics such as R², RMSLE, MAE, MSE, MAPE, and RMSE, allowing identification of the best-performing model.\"}]","Machine Learning-Powered Prediction of molecule Solubility - Paving the Way for environmental and energy applications | PDF",1785722076,25,{"code":4,"msg":31,"data":32},"ok",{"site_id":24,"language":23,"slug":33,"title":13,"keywords":34,"description":14,"schema_data":35,"social_meta":86,"head_meta":88,"extra_data":90,"updated_unix":28},"machine-learning-powered-prediction-of-molecule-solubility-paving-the-way-for-environmental-and-energy-applications","",{"@graph":36,"@context":85},[37,54,68],{"@type":38,"itemListElement":39},"BreadcrumbList",[40,44,48,51],{"item":41,"name":42,"@type":43,"position":20},"https://docshare.wps.com","Home","ListItem",{"item":45,"name":46,"@type":43,"position":47},"https://docshare.wps.com/document/","Document",2,{"item":49,"name":12,"@type":43,"position":50},"https://docshare.wps.com/document/research-report/",3,{"item":52,"name":13,"@type":43,"position":53},"https://docshare.wps.com/document/machine-learning-powered-prediction-of-molecule-solubility-paving-the-way-for-environmental-and-energy-applications/119047/",4,{"url":52,"name":13,"@type":55,"author":56,"headline":13,"publisher":58,"fileFormat":61,"inLanguage":23,"description":14,"dateModified":62,"datePublished":62,"encodingFormat":61,"isAccessibleForFree":63,"interactionStatistic":64},"DigitalDocument",{"name":9,"@type":57},"Person",{"url":41,"name":59,"@type":60},"DocShare","Organization","application/pdf","2026-08-03",true,{"@type":65,"interactionType":66,"userInteractionCount":4},"InteractionCounter",{"@type":67},"ViewAction",{"@type":69,"mainEntity":70},"FAQPage",[71,77,81],{"name":72,"@type":73,"acceptedAnswer":74},"Why is predicting aqueous solubility important across applications?","Question",{"text":75,"@type":76},"Aqueous solubility is critical for material selection in pharmaceuticals, environmental work, and renewable energy. It also plays a key role in drug development and designing chemical and synthetic pathways.","Answer",{"name":78,"@type":73,"acceptedAnswer":79},"What limitations do traditional and existing computational methods face?",{"text":80,"@type":76},"Traditional approaches often rely on labor-intensive experiments that are time-consuming and costly. Existing computational methods may not achieve the accuracy and scalability needed for real-world use due to the complex link between molecular structure and environmental factors.",{"name":82,"@type":73,"acceptedAnswer":83},"How does the study predict molecule solubility and evaluate model performance?",{"text":84,"@type":76},"The work uses PyCaret to build and compare multiple linear regression models. Performance is assessed using metrics such as R², RMSLE, MAE, MSE, MAPE, and RMSE, allowing identification of the best-performing model.","https://schema.org",{"og:url":52,"og:type":87,"og:title":13,"og:site_name":59,"og:description":14},"article",{"robots":89,"canonical":52},"index,follow",{"doc_id":7,"site_id":24},{"code":4,"msg":5,"data":92},[93,97,101,105,110,115,120,123,128,131,134],{"id":20,"doc_module":4,"doc_module_name":46,"category_name":94,"show_sort_weight":95,"slug":96},"Story & Novel",90,"story-novel",{"id":47,"doc_module":4,"doc_module_name":46,"category_name":98,"show_sort_weight":99,"slug":100},"Literature",80,"literature",{"id":53,"doc_module":4,"doc_module_name":46,"category_name":102,"show_sort_weight":103,"slug":104},"Exam",70,"exam",{"id":106,"doc_module":4,"doc_module_name":46,"category_name":107,"show_sort_weight":108,"slug":109},5,"Comic",60,"comic",{"id":111,"doc_module":4,"doc_module_name":46,"category_name":112,"show_sort_weight":113,"slug":114},6,"Technology",50,"technology",{"id":116,"doc_module":4,"doc_module_name":46,"category_name":117,"show_sort_weight":118,"slug":119},7,"Healthcare",40,"healthcare",{"id":11,"doc_module":4,"doc_module_name":46,"category_name":12,"show_sort_weight":121,"slug":122},30,"research-report",{"id":124,"doc_module":4,"doc_module_name":46,"category_name":125,"show_sort_weight":126,"slug":127},9,"Religion & Spirituality",20,"religion-spirituality",{"id":126,"doc_module":4,"doc_module_name":46,"category_name":129,"show_sort_weight":126,"slug":130},"World Cup","world-cup",{"id":21,"doc_module":4,"doc_module_name":46,"category_name":132,"show_sort_weight":21,"slug":133},"Lifestyle","lifestyle",{"id":135,"doc_module":4,"doc_module_name":46,"category_name":136,"show_sort_weight":106,"slug":137},19,"General","general"]