[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-124397-en":3,"doc-seo-124397-105":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},124397,1374391974564,"Clementine","https://ap-avatar.wpscdn.com/avatar/14000253aa45c000a9e?x-image-process=image/resize,m_fixed,w_180,h_180&k=1779874745381141002",8,"Research & Report","Explainable Machine Learning and Deep Learning Models for Predicting TAS2R-Bitter Molecule Interactions - Research overview","This work develops explainable machine-learning and deep-learning models to predict interactions between bitter molecules and TAS2R bitter taste receptors using experimentally validated data. Bitter taste is mediated by G protein-coupled receptors, TAS2R, which also function in extra-oral tissues and relate to multiple diseases. Because in-vitro screening of candidate TAS2R ligands is costly and time-consuming, the proposed models aim to support ligand and target selection for experiments. High performance and easy applicability are combined with synergistic ML–DL integration to improve interpretability, enabling better understanding of bitter compound characteristics and guiding the design of novel bitterants for specific TAS2Rs.","Explainable Machine Learning and Deep Learning Models for Predicting TAS2R-Bitter Molecule Interactions  \nFrancesco Ferri1,2†, Marco Cannariato1†, Lorenzo Pallante1, Eric A. Zizzi1, and Marco A. Deriu1*  \n1 Politecnico di Torino, PolitoBIOMedLab, Department of Mechanical and Aerospace Engineering, Torino, 10129, Italy  \n2 Leibniz Institute for Food Systems Biology at the Technical University of Munich, 85354 Freising, Germany  \n† These authors contributed equally to this work  \n*  marco.deriu@polito.it  \nAbstract  \nThis work aims to develop explainable models to predict the interactions between bitter molecules and TAS2Rs via traditional machine-learning and deep-learning methods starting from experimentally validated data. Bitterness is one of the five basic taste modalities that can be perceived by humans and other mammals. It is mediated by a family of G protein-coupled receptors (GPCRs), namely taste receptor type 2 (TAS2R) or bitter taste receptors. Furthermore, TAS2Rs participate in numerous functions beyond the gustatory system and have implications for various diseases due to their expression in various extra-oral tissues. For this reason, predicting the specific ligand-TAS2Rs interactions can be useful not only in the field of taste perception but also in the broader context of drug design. Considering that in-vitro screening of potential TAS2R ligands is expensive and time-consuming, machine learning (ML) and deep learning (DL) emerged as powerful tools to assist in the selection of ligands and targets for experimental studies and enhance our understanding of bitter receptor roles. In this context, ML and DL models developed in this work are both characterized by high performance and easy applicability. Furthermore, they can be synergistically integrated to enhance model explainability and facilitate the interpretation of results. Hence, the presented models promote a comprehensive understanding of the molecular characteristics of bitter compounds and the design of novel bitterants tailored to targetspecific TAS2Rs of interest.  \nIntroduction  \nTaste perception is a crucial determinant of food intake and consumption patterns (Glendinning, 1994), with substantial consequences for human nutrition and health (Shahbandi et al., 2018) . Several pathological conditions can impair the sense of taste and lead to alterations in quality of life and body weight regulation (Risso et al., 2020) . The molecular mechanisms of bitter taste perception involve a subfamily of 25 G protein-coupled receptors (GPCRs), called type 2 taste receptors (TAS2Rs) (Pallante et al., 2021) . These proteins are also expressed in various extraoral tissues, where they may perform additional physiological functions, such as modulating inflammatory response, controlling upper respiratory immunity and others (Behrens & Lang, 2022; Behrens & Meyerhof, 2011) . TAS2Rs exhibit a remarkable diversity and specificity in their ligand recognition and activation. Some TAS2Rs are defined as promiscuous as they can bind to multiple and structurally distinct bitter compounds, while others are highly selective and respond  \nto only a few known ligands (Di Pizio & Niv, 2015) . Conversely, some bitter compounds can activate several TAS2Rs, while others are specific for individual TAS2Rs. The chemical nature of bitter compounds is extremely heterogeneous and encompasses peptides, saponins, alkaloids, polyphenols, and salts (Di Pizio et al., 2019) . In this context, understanding the molecular interactions and predicting the specific association between bitter molecules and relative TAS2Rs could impact several fields of applications. For example, this line of research can: (i) help in the adherence to therapies based on bitter drugs (Mennella et al., 2013); (ii) improve the understanding regarding side e`ects or a`ect undiscovered biological pathways since TAS2Rs are present in extraoral tissues (Shaik et al., 2016); (iii) assist the design of alternative bitterants to improve the","cbCailWD26ZPbLhs","https://ap.wps.com/l/cbCailWD26ZPbLhs","pdf",2770622,1,33,"English","en",105,"# Abstract\n# Introduction\n## Taste perception and TAS2R biology\n## Applications and limitations of current assays\n## Computational prediction approaches\n## Machine learning and deep learning models","[{\"question\":\"What problem do the explainable ML and DL models address?\",\"answer\":\"They predict ligand-TAS2R interactions for bitter molecules using experimentally validated data, reducing reliance on costly and time-consuming in-vitro screening.\"},{\"question\":\"Why is TAS2R interaction prediction useful beyond taste perception?\",\"answer\":\"TAS2Rs are expressed in extra-oral tissues and can influence physiological functions, so predicting specific interactions can support broader contexts such as understanding disease mechanisms and drug design.\"},{\"question\":\"How do the models improve interpretability of results?\",\"answer\":\"They combine traditional machine-learning and deep-learning approaches in a synergistic way, aiming for both high performance and explainability to facilitate interpretation of molecular and receptor-related findings.\"}]","Explainable Machine Learning and Deep Learning Models for Predicting TAS2R-Bitter Molecule Interactions - 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