[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-124866-en":3,"doc-seo-124866-105":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},124866,549758252649,"Ivy","https://ap-avatar.wpscdn.com/avatar/8000253669c5317157?_k=1778319167496531819",8,"Research & Report","Machine learning acceleratespharmacophore-based virtual screening of MAO inhibitors","Machine learning accelerates pharmacophore-based virtual screening for monoamine oxidase inhibitors by predicting docking scores from learned docking results, avoiding time-consuming molecular docking at screening time. The ensemble model uses multiple molecular fingerprints and descriptors to learn binding energy proxies and reduce prediction errors. The protocol delivers 1000× faster binding energy predictions than classical docking-based screening and enables selecting preferred docking software without relying on limited, inconsistent experimental activity data. A pharmacophore-constrained ZINC screen produced 24 synthesized compounds, including weak MAO-A inhibitors near a known drug at the lowest tested concentration.","[www. nature.com/scientificreports](www. nature.com/scientificreports)  \nOPEN  \nMachine learning acceleratespharmacophore‑based virtual screening of MAO inhibitors  \nMarcin Cieślak1,2,3*, Tomasz Danel1,4, Olga Krzysztyńska‑Kuleta 5 & Justyna Kalinowska‑Tłuścik1*  \nNowadays, an efficient and robust virtual screening procedure is crucial in the drug discovery process, especially when performed on large and chemically diverse databases. Virtual screening methods, like molecular docking and classic QSAR models, are limited in their ability to handle vast numbers of compounds and to learn from scarce data, respectively. In this study, we introduce a universal methodology that uses a machine learning‑based approach to predict docking scores without the need for time‑consuming molecular docking procedures. The developed protocol yielded 1000 times faster binding energy predictions than classical docking‑based screening. The proposed predictive model learns from docking results, allowing users to choose their preferred docking software without relying on insufficient and incoherent experimental activity data. The methodology described employs multiple types of molecular fingerprints and descriptors to construct an ensemble model that further reduces prediction errors and is capable of delivering highly precise docking score values for monoamine oxidase ligands, enabling faster identification of promising compounds. An extensive pharmacophore‑constrained screening of the ZINC database resulted in a selection of 24 compounds that were synthesized and evaluated for their biological activity. A preliminary screen discovered weak inhibitors of MAO‑A with a percentage efficiency index close to a known drug atthe lowest tested concentration. The approach presented here can be successfully applied to other biological targets as target‑specific knowledge is not incorporated at the screening phase.  \nKeywords Machine learning, Virtual screening, Monoamine oxidase inhibitors, Molecular descriptors, Molecular docking  \nExploration of a large chemical space1 in the search for novel lead compounds remains a challenge2. Thus, modern drug discovery campaigns require fast, robust, and efficient approaches to accelerate the design process3–5. The recent remarkable development of computational methods and algorithms has led to the successful application of virtual screening (VS)6, often based upon molecular docking procedures. It is routinely applied to assess the affinity of a ligand to the selected target protein7. The structure-based techniques constantly evolve and improve due to the increasing number of data deposited within the Protein Data Bank (PDB)8. This database is the utmost source of structural information concerning intermolecular interactions in biological systems. Through a deeper understanding of protein-ligand complex formation and stabilization, novel algorithms can be introduced and subsequently modified. Thus, as a consequence, an advantageous route to increasing the predictive power of the methods applied may be obtained. The utility of molecular docking procedures in the continued search for new lead structures is often fraught with costly computations to discover the optimal binding pose for the screened compounds. Of late, such calculations are often complimented or entirely bypassed by machine learning (ML) methods, that can derive quantitative structure-activity relationship (QSAR) models based on the ligands’ chemical structures9. These models use different classes of molecular descriptors as input and return predicted activity, e.g. estimated binding affinity or IC 􀀞values. Nevertheless, the results of QSAR models are highly dependent on the training datasets, and predictions can be unreliable when novel chemotypes are presented to the model10.  \n1Faculty of Chemistry, Jagiellonian University, Gronostajowa 2, 30-387 Kraków, Małopolska, Poland. 2Doctoral School of Exact and Natural Sciences, Jagiellonian University, Prof. S. ","cbCaior3ZuO7UQU9","https://ap.wps.com/l/cbCaior3ZuO7UQU9","pdf",2222012,1,15,"English","en",105,"# Introduction\n## Virtual screening and docking limitations\n## QSAR and machine learning advances\n## Study aim","[{\"question\":\"How does the proposed method replace traditional docking-based virtual screening?\",\"answer\":\"It trains a machine-learning ensemble to predict docking scores directly from docking-derived data, eliminating the need for time-consuming docking during screening.\"},{\"question\":\"What inputs does the model use to improve docking-score prediction accuracy?\",\"answer\":\"The method employs multiple molecular fingerprints and molecular descriptors to build an ensemble model that reduces prediction errors.\"},{\"question\":\"What were the results of applying the pharmacophore-constrained screen to the ZINC database?\",\"answer\":\"The screening selected 24 compounds for synthesis and biological evaluation, and a preliminary test identified weak MAO-A inhibitors with efficiency near a known drug at the lowest tested concentration.\"}]","Machine learning acceleratespharmacophore-based virtual screening of MAO inhibitors | 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does the proposed method replace traditional docking-based virtual screening?","Question",{"text":75,"@type":76},"It trains a machine-learning ensemble to predict docking scores directly from docking-derived data, eliminating the need for time-consuming docking during screening.","Answer",{"name":78,"@type":73,"acceptedAnswer":79},"What inputs does the model use to improve docking-score prediction accuracy?",{"text":80,"@type":76},"The method employs multiple molecular fingerprints and molecular descriptors to build an ensemble model that reduces prediction errors.",{"name":82,"@type":73,"acceptedAnswer":83},"What were the results of applying the pharmacophore-constrained screen to the ZINC database?",{"text":84,"@type":76},"The screening selected 24 compounds for synthesis and biological evaluation, and a preliminary test identified weak MAO-A inhibitors with efficiency near a known drug at the lowest tested 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