[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-122428-en":3,"doc-seo-122428-105":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":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},122428,13056703019662,"Evangeline","https://ap-avatar.wpscdn.com/avatar/be000253a8e92610077?_k=1778726343310543188",8,"Research & Report","Novel machine learning approach toward classification model of HIV-1 integrase inhibitors","A novel machine learning workflow is developed to classify compounds inhibiting HIV-1 integrase and to screen repurposing candidates. The study applies a two-stage strategy: selecting the most informative fingerprint or molecular descriptor type via a Wilcoxon signed-rank test and cross-validated evaluation across 10 machine-learning models, followed by model construction, data preprocessing, outlier handling, normalization, feature selection, model selection, external validation, and optimization. An XGBoost model using RDK7 fingerprint shows strong performance (precision and F1 scores) and is supported by docking assessment and retrospective AUC-based screening.","University of Southern Denmark  \nNovel machine learning approach toward classification model of HIV-1 integrase inhibitors  \nPhan, Tieu Long; Trinh, The Chuong; To, Van Thinh; Pham, Thanh An; Van Nguyen, Phuoc Chung; Phan, Tuyet Minh; Truong, Tuyen Ngoc  \nPublished in: RSC Advances  \nDOI:  \n10.1039/d4ra02231a  \nPublication date: 2024  \nDocument version:  \nFinal published version  \nDocument license: CC BY  \nCitation for pulished version (APA):  \nPhan, T. L. , Trinh, T. C. , To, V. T. , Pham, T. A. , Van Nguyen, P. C. , Phan, T. M. , & Truong, T. N. (2024) . Novel machine learning approach toward classification model of HIV-1 integrase inhibitors. RSC Advances, 14(21), 14506-14513. [https://doi.org/10.1039/d4ra02231a](https://doi.org/10.1039/d4ra02231a)  \nGo to publication entry in University of Southern Denmark's Research Portal  \nTerms of use  \nThis work is brought to you by the University of Southern Denmark.  \nUnless otherwise specified it has been shared according to the terms for self-archiving.  \nIf no other license is stated, these terms apply:  \n• You may download this work for personal use only.  \n• You may not further distribute the material or use it for any profit-making activity or commercial gain  \n• You may freely distribute the URL identifying this open access version  \nIf you believe that this document breaches copyright please contact us providing details and we will investigate your claim. Please direct all enquiries to [puresupport@bib.sdu.dk](puresupport@bib.sdu.dk)  \nDownload date: 03. Aug. 2026  \nOpen Access Art 2024. Down on 5/22/2024icle . Published on 02 May loaded 7:57:05 AM .  \nhicle is licensed under a Creative C mmons A 3 0 U d[ttr .](ttr .)ibution npor e nce.  \nRSC Advances  \nPAPER  \nView Article Online View Journal | View Issue  \nCite this: RSC Adv., 2024, 14, 14506  \nReceived 23rd March 2024  \nAccepted 22nd April 2024  \nDOI: 10.1039/d4ra02231a[rsc.li/rsc-advances](rsc.li/rsc-advances)  \nNovel machine learning approach toward  \nclassification model of HIV-1 integrase inhibitors†  \nTieu-Long Phan, ab The-Chuong Trinh,  c Van-Thinh To, d Thanh-An Pham, d Phuoc-Chung Van Nguyen, d Tuyet-Minh Phan d and Tuyen Ngoc Truong  *d  \nHIV-1 (human immunodeficiency virus-1) has been causing severe pandemics by attacking the immune system of its host. Left untreated, it can lead to AIDS (acquired immunodeficiency syndrome), where death is inevitable due to opportunistic diseases. Therefore, discovering new antiviral drugs against HIV-1 is crucial. This study aimed to explore a novel machine learning approach to classify compounds that inhibit HIV-1 integrase and screen the dataset of repurposing compounds. The present study had two main stages: selecting the best type of fingerprint or molecular descriptor using the Wilcoxon signedrank test and building a computational model based on machine learning. In the first stage, we calculated 16 different types of fingerprint or molecular descriptors from the dataset and used each of them as input features for 10 machine-learning models, which were evaluated through cross-validation. Then, a meta-analysis was performed with the Wilcoxon signed-rank test to select the optimal fingerprint or molecular descriptor types. In the second stage, we constructed a model based on the optimal fingerprint or molecular descriptor type. This data followed the machine learning procedure, including data preprocessing, outlier handling, normalization, feature selection, model selection, external validation, and model optimization. In the end, an XGBoost model and RDK7 fingerprint were identified as the most suitable. The model achieved promising results, with an average precision of 0 .928 ± 0.027 and an F1-score of 0 .848 ± 0. 041 in cross-validation. The model achieved an average precision of 0 .921 and an F1-score of 0 . 889 in external validation. Molecular docking was performed and validated by redocking for docking power and retrospective control for screening power, with the AUC metrics be","cbCaihrrVmPx5klW","https://ap.wps.com/l/cbCaihrrVmPx5klW","pdf",1782908,1,9,"English","en",105,"# Introduction\n# Materials and methods\n## Fingerprint/descriptor selection\n## Machine-learning model development and validation\n# Results and discussion\n## Model performance and feature choice\n## Docking, docking power and screening power\n## Repurposing candidates from DrugBank","[{\"question\":\"What is the main goal of the study on HIV-1 integrase inhibitors?\",\"answer\":\"To build and evaluate a machine learning classification model that can identify compounds inhibiting HIV-1 integrase, and to screen repurposing datasets for promising candidates.\"},{\"question\":\"How are fingerprints or molecular descriptors selected in the workflow?\",\"answer\":\"Sixteen types are calculated and used as input features for multiple machine-learning models, and the optimal type is chosen using the Wilcoxon signed-rank test after cross-validation.\"},{\"question\":\"Which modeling and evaluation steps are included after descriptor selection?\",\"answer\":\"The process includes data preprocessing, outlier handling, normalization, feature selection, model selection, external validation, and model optimization, followed by docking validation and retrospective screening assessment.\"}]","Novel machine learning approach toward classification model of HIV-1 integrase inhibitors | 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is the main goal of the study on HIV-1 integrase inhibitors?","Question",{"text":75,"@type":76},"To build and evaluate a machine learning classification model that can identify compounds inhibiting HIV-1 integrase, and to screen repurposing datasets for promising candidates.","Answer",{"name":78,"@type":73,"acceptedAnswer":79},"How are fingerprints or molecular descriptors selected in the workflow?",{"text":80,"@type":76},"Sixteen types are calculated and used as input features for multiple machine-learning models, and the optimal type is chosen using the Wilcoxon signed-rank test after cross-validation.",{"name":82,"@type":73,"acceptedAnswer":83},"Which modeling and evaluation steps are included after descriptor selection?",{"text":84,"@type":76},"The process includes data preprocessing, outlier handling, normalization, feature selection, model selection, external validation, and model optimization, followed by docking validation and retrospective screening 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