[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-117587-en":3,"doc-seo-117587-105":30,"detail-sidebar-cat-0-en-105":92},{"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":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},117587,1374391974468,"Eden","https://ap-avatar.wpscdn.com/davatar_29158cc5080c5b710cf443261637dec0",8,"Research & Report","Machine Learning to Predict Melting Temperatures of Deep Eutectic Solvents from Molecular Descriptors - Thesis","Deep Eutectic Solvents (DESs) are presented as promising alternatives to conventional solvents due to their distinct properties, with melting temperature identified as a key parameter for industrial use. Machine learning approaches are used to predict melting temperatures of DES mixtures from molecular features, aiming to reduce expensive and time-consuming experiments. The work trains and tests multiple regression and one neural network model using code from an external research group and an expanded dataset. Feature engineering, descriptor modification, and statistical evaluation support comparison of model performance and assessment of how feature selection and dataset size affect predictive accuracy.","UNIVERSITÀ DEGLI STUDI DI PADOVA  \nDIPARTIMENTO DI BIOLOGIA  \nCorso di Laurea in Biotecnologie  \nELABORATO DI LAUREA  \nMachine Learning to Predict Melting Temperatures of Deep Eutectic Solvents from Molecular Descriptors  \nTutor  \nDott. Sergio Rampino  \nDipartimento di Scienze Chimiche  \nLaureanda: Luna Liviero  \nANNO ACCADEMICO 2024/2025  \nContents  \n1 State of the art 1  \n1.1 Deep Eutectic Solvents ...................... 2  \n1.2 Machine-Learning Basics ..................... 3  \n2 Methods 5  \n2. 1 Software environment and tools . . . . . . . . . . . . . . . . . 5  \n2.2 Codebase . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 5  \n2.2.1 Feature engineering . . . . . . . . . . . . . . . . . . . . 6  \n2.2.2 Datasets . . . . . . . . . . . . . . . . . . . . . . . . . . 7  \n2.2.3 Evaluation Metrics . . . . . . . . . . . . . . . . . . . . 8  \n2.3 Machine Learning Models .................... 9  \n2.3.1 Training and Testing ................... 9  \n2.3.2 Hyperparameter optimization . . . . . . . . . . . . . . 9  \n2.3.3 Models . . . . . . . . . . . . . . . . . . . . . . . . . . 10  \n2.4 Work昀؀ow . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 12  \n3 Results 14  \n4 Conclusion and discussion 18  \nBibliography 22  \nAbstract  \nDeep Eutectic Solvents (DESs) emerged as promising alternatives to conventional solvents for their peculiar characteristics. Among these, melting temperature is a critical parameter that deeply in昀؀uences their industrial applicability. It is possible to use machine learning (ML) models to predict the melting temperatures of DES mixtures based on their molecular features, to reduce time-consuming and costly experimental work. The approach involves training and testing a series of existing learning models, using the code developed by a separate research group, on an expanded dataset curated by a colleague. Moreover the possibility of modifying the molecular descriptors originally used as input for prediction will be explored. The impact of the changes implemented in the original code will be assessed using statistical metrics, which also allows to compare each model9s performance. The results should provide valuable insight into the in昀؀uence of feature selection and dataset size on prediction accuracy, and con昀؀rm the most e昀؀ective model for melting temperature prediction.  \n1 State of the art  \nIn the last decades, the 昀؀eld of arti昀؀cial intelligence (AI) has progressed ata blistering pace. One reason for this growth is the innumerable time- and cost-saving applications in all sectors.[1] The development of machine learning (ML) algorithms and techniques has recently yielded impactful results. Most people9s understanding of these advancements begins and ends with chatbots, planted in everyday search engines and apps, considered by the public as revolutionary, while barely understanding what machine learning truly enables, quietly, in 昀؀elds most have never even heard of. Among the most astonishing of these specialized advancements are those revolutionizing scienti昀؀c research particularly in biology and drug discovery. Notable examples include the development of AlphaFold-3 to predict protein structure and the Insilico AI platform[2], implemented to fully design a drug that entered human clinical trials. In chemistry, e昀؀orts are being made towards full in-silico laboratories, though it lies beyond our grasp for now. Nonetheless, advances have been pursued in exploring chemical space for de novo molecular design, synthesis pathway prediction, and molecular property prediction. The possible applications range from the medical to the industrial 昀؀eld; from new antibiotics to environmentally friendly solvents.  \nThe ML algorithms employed in these endeavours are various, but can be categorized in two main groups: predictive and generative. Predictive models estimate an output after discerning a pattern from a large amount of input data. Generative models create new data based on patterns learned from","cbCaiadxfhWQxYhe","https://ap.wps.com/l/cbCaiadxfhWQxYhe","pdf",428834,1,25,"English","en",105,"# Contents\n## State of the art\n## Methods\n## Results\n## Conclusion and discussion\n## Bibliography","[{\"question\":\"Why is melting temperature important for Deep Eutectic Solvents (DESs)?\",\"answer\":\"Melting temperature strongly influences DES industrial applicability, making it a critical property to predict accurately for practical deployment.\"},{\"question\":\"How is machine learning used in this thesis?\",\"answer\":\"Multiple learning models are trained and tested to predict melting temperatures from molecular features, including several regression models and one neural network.\"},{\"question\":\"What factors are evaluated to understand prediction performance?\",\"answer\":\"The thesis evaluates the impact of feature selection and dataset size on prediction accuracy using statistical metrics to compare model performance.\"}]","Machine Learning to Predict Melting Temperatures of Deep Eutectic Solvents from Molecular Descriptors - 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