[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-123687-en":3,"doc-seo-123687-105":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},123687,34359740700684,"Finn","https://ap-avatar.wpscdn.com/avatar/1f400023980c374ae676?_k=1777273430885731487",8,"Research & Report","Machine Learning-based Analysis of Electronic Properties as Predictors of Anticholinesterase Activity in Chalcone Derivatives","This study examines how electronic properties of anticholinesterase compounds relate to biological activity. A machine-learning workflow is used to improve upon established electronic-activity correlations. Twenty-two chalcone-skeleton molecules are separated into active and inactive classes using IC50 indices. Open-source Orca calculations provide geometries and electronic structures, yielding over 100 descriptors including Mulliken and Löwdin populations, molecular orbital energies, and Mayer’s free valences. Several models distinguish the two groups, with the most informative descriptors based on electronic populations and orbital energies, supporting more efficient drug development.","arXiv :2309 .07312v1 [physics .comp-ph] 13 Sep 2023  \nMachine Learning-based Analysis of Electronic  \nProperties as Predictors of Anticholinesterase Activity in Chalcone Derivatives†  \nThiago Buzelli,‡ Bruno Ipaves,∗ ,‡ Wanda Pereira Almeida,¶ Douglas Soares  \nGalvao,∗ , § and Pedro Alves da Silva Autreto ∗ ,‡  \n‡Center for Natural and Human Sciences (CCNH) of Federal University of ABC (UFABC)  \n¶Institute of Chemistry, State University of Campinas (UNICAMP)  \n§Applied Physics Department and Center for Computational Engineering and Sciences,  \nState University of Campinas (UNICAMP)  \nE-mail: ipaves.bruno@ufabc.edu.br; galvao@ifi.unicamp.br; pedro.autreto@ufabc.edu.br  \nAbstract  \nIn this study, we investigated the correlation between the electronic properties of anticholinesterase compounds and their biological activity. While the methodology of such correlation is well-established and has been effectively utilized in previous studies, we employed a more sophisticated approach: machine learning. Initially, we focused on a set of 22 molecules sharing a common chalcone skeleton and categorized them into two groups based on their IC50 indices: active and inactive. Utilizing the open-source software Orca, we conducted calculations to determine the geometries and electronic structures of these molecules. Over a hundred parameters were collected from these calculations, serving as the foundation for the features used in machine  \n†A footnote for the title  \nlearning. These parameters included the Mulliken and Lowdin electronic populations of each atom within the skeleton, molecular orbital energies, and Mayer’s free valences.  \nThrough our analysis, we developed numerous models and identified several successful candidates for effectively distinguishing between the two groups. Notably, the most informative descriptor for this separation relied solely on electronic populations and orbital energies. By understanding which computationally calculated properties are most relevant to specific biological activities, we can significantly enhance the efficiency of drug development processes, saving both time and resources.  \nIntroduction  \nDeveloping novel pharmaceutical compounds is a complex, resource-intensive, and timeconsuming process. While drugs promise to improve life expectancy and deliver effective disease treatment, they also pose the inherent risk of adverse reactions and use abuse. The pipeline development of new pharmacological products generally involves multi-sequential stages, such as: 1 . identification and discovery of compounds exhibiting therapeutic activity;  \n2. rigorous in vitro testing to assess their biological properties; 3 . comprehensive in vivo studies to investigate their dynamics in animal models; 4 . human clinical trials. 1  \nOptimizing the selection of new active compounds through computer simulations offers significant appeal for cost reduction, reagent optimization, and accelerated product development. Computational approaches, such as docking, allow the analyses of molecule-ligand interactions to identify suitable candidates that fit (energetically and geometrically) into protein pocket binding sites. 2 In principle, selecting structures with a good potential fora desired pharmaceutical activity through interaction energy values is effectively possible. Other approaches aim to correlate geometric features, chemical composition, and electronic structure data with biological activity.  \nThe use of computer-generated models has already achieved remarkable success in correlating biological activity with molecular electronic features obtained from electronic struc-  \nture calculations. For instance, the electronic structure of acids can be directly linked to the biological activity of their derivatives. 3 Additionally, there is a correlation between spectroscopy data and the biological activity of steroids, highlighting the significance of the molecules’ skeletal composition.4 Other studies established a corre","cbCaisVEziY14rrE","https://ap.wps.com/l/cbCaisVEziY14rrE","pdf",1996684,1,20,"English","en",105,"# Abstract\n# Introduction\n## Drug discovery pipeline and computational screening\n## Electronic-structure descriptors and biological activity correlations\n## Motivation for machine learning in data-driven discovery","[{\"question\":\"What is the main goal of the study?\",\"answer\":\"To determine which computed electronic properties best predict anticholinesterase biological activity in chalcone derivatives.\"},{\"question\":\"How were the chalcone derivatives grouped for modeling?\",\"answer\":\"The 22 molecules were classified as active or inactive based on their IC50 indices.\"},{\"question\":\"Which computational outputs were used as input features?\",\"answer\":\"Orca calculations produced descriptors such as Mulliken and Löwdin electronic populations, molecular orbital energies, and Mayer’s free valences.\"}]","Machine Learning-based Analysis of Electronic Properties as Predictors of Anticholinesterase Activity in Chalcone Derivatives | 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is the main goal of the study?","Question",{"text":75,"@type":76},"To determine which computed electronic properties best predict anticholinesterase biological activity in chalcone derivatives.","Answer",{"name":78,"@type":73,"acceptedAnswer":79},"How were the chalcone derivatives grouped for modeling?",{"text":80,"@type":76},"The 22 molecules were classified as active or inactive based on their IC50 indices.",{"name":82,"@type":73,"acceptedAnswer":83},"Which computational outputs were used as input features?",{"text":84,"@type":76},"Orca calculations produced descriptors such as Mulliken and Löwdin electronic populations, molecular orbital energies, and Mayer’s free 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