[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-120302-en":3,"doc-seo-120302-105":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},120302,1374391975076,"Riley","https://ap-avatar.wpscdn.com/avatar/14000253ca4ec9f6853?x-image-process=image/resize,m_fixed,w_180,h_180&k=1783305029341752051",8,"Research & Report","Druggability of Pharmaceutical Compounds Using Lipinski Rules with Machine Learning","Drug discovery requires selecting pharmaceutical compounds with strong drug-like potential, yet manual evaluation of large compound sets consumes extensive time and resources. Lipinski’s Rule of Five (RO5) is a core druggability criterion, but computational early-stage methods can streamline screening and candidate selection. This study applies supervised machine learning models to predict and classify drug candidates based on RO5 adherence, comparing performance metrics and using the best model to power the DrugCheckMaster application.","Research Article  \nSciences of Pharmacy  \nDruggability of Pharmaceutical Compounds Using Lipinski Rules with Machine Learning  \nSamukelisiwe Nhlapho  , Musawenkosi Hope Lotriet Nyathi  , Brendeline Linah Ngwenya  , Thabile Dube  , Arnesh Telukdarie  , Inderasan Munien  , Andre Vermeulen, Uche A. K Chude-Okonkwo   \n[The author informations are in the declarations section. This article is published by ETFLIN in Sciences of Pharmacy, Volume 3, Issue 4, 2024, Page 177-192. [https://doi.org/10.58920/sciphar0304264](https://doi.org/10.58920/sciphar0304264)]  \nReceived: 17 July 2024  \nRevised: 18 October 2024  \nAccepted: 09 November 2024  \nPublished: 11 November 2024  \nEditor: Ernest Domanaanmwi Ganaa  \n This article is licensed under a Creative Commons Attribution 4.0 International License. © The author(s)(2024) .  \nKeywords: Drug discovery, Machine learning models, Molecular descriptors, Rule of five (RO5) .  \nAbstract: In the field of pharmaceutical research, identifying promising pharmaceutical compounds is a critical challenge. The observance of Lipinski's Rule of Five (RO5) is a fundamental criterion, but evaluating many compounds manually requires significant resources and time. However, the integration of computational techniques in drug discovery in its early stages has significantly transformed the pharmaceutical industry, enabling further efficient screening and selection of possible drug candidates. Therefore, this study explores RO5 using algorithms of Machine Learning (ML), offering a comprehensive method to predict the druggability of pharmaceutical compounds. The study developed, evaluated, and validated the performance metrics of multiple supervised machine learning models. The best model was used to build an application that can predict and classify potential drug candidates. The findings revealed promising capabilities across all models for drug classification. Among all the explored models, Random Forest (RF), Extreme Gradient Boost (XGBoost), and Decision Tree (DT) classifiers demonstrated exceptional performance, achieving near-perfect accuracy of 99.94%, 99.81% and 99.87% respectively. This highlights the robustness of ensemble learning methods in classifying compounds based on RO5 adherence. The comparative analysis of these models underscores the importance of considering balanced accuracy, precision, F1-score, recall, and Receiver Operating Characteristics-Area Under the Curve (ROC-AUC) score, interpretability, and computational efficiency when choosing between ML algorithms in drug discovery. The DrugCheckMaster application was subsequently developed using the most predictive model and is now available on Render ( [https://capstone-project-dc7w.onrender.com/](https://capstone-project-dc7w.onrender.com/)).  \nIntroduction  \nThe process of drug discovery includes identifying drug candidates, synthesizing, characterizing, and screening them for therapeutic efficacy (1) . It includes target discovery, lead discovery, lead optimization, preclinical development, three phases of clinical trials, and lastly, market launch, provided all regulations are met (2) . However, this developmental process is one of the most challenging human applications, as it demands a delicate balance between ensuring safety within an appropriate therapeutic range and maximizing efficacyin delivering health benefits (3) . The likelihood of drug candidates successfully advancing through Phase I clinical development is estimated to be only 7–11%(4) . With so many challenges inherent in drug development  \nparticularly the drug candidates’ low success rate of advancing to clinical trials, attention turns to strategies aimed at enhancing the likelihood of success at each stage . One of the strategies is druggability assessments which are done to improve the candidate’s drug-like qualities and raise the likelihood that their clinical development will be successful. Compounds that exhibit promise in this assessment are often chosen for further optimization, incl","cbCaid0yjqu5siph","https://ap.wps.com/l/cbCaid0yjqu5siph","pdf",1373685,1,16,"English","en",105,"# Introduction\n## Drug discovery challenges and druggability assessment\n## Lipinski’s Rule of Five and the need for computational expansion\n## Machine learning-based virtual screening approach\n## Supervised modeling and application development","[{\"question\":\"Why is druggability assessment important in pharmaceutical research?\",\"answer\":\"It improves candidates’ drug-like qualities and increases the likelihood that they will succeed in clinical development.\"},{\"question\":\"How does Lipinski’s Rule of Five (RO5) relate to this study?\",\"answer\":\"RO5 provides fundamental guidelines for drug-like physicochemical properties, and the study uses it to guide machine learning predictions of druggability.\"},{\"question\":\"Which machine learning models showed the best performance for drug classification?\",\"answer\":\"Random Forest, Extreme Gradient Boost (XGBoost), and Decision Tree classifiers achieved near-perfect accuracy, with reported accuracies of 99.94%, 99.81%, and 99.87% respectively.\"}]","Druggability of Pharmaceutical Compounds Using Lipinski Rules with Machine Learning | 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is druggability assessment important in pharmaceutical research?","Question",{"text":75,"@type":76},"It improves candidates’ drug-like qualities and increases the likelihood that they will succeed in clinical development.","Answer",{"name":78,"@type":73,"acceptedAnswer":79},"How does Lipinski’s Rule of Five (RO5) relate to this study?",{"text":80,"@type":76},"RO5 provides fundamental guidelines for drug-like physicochemical properties, and the study uses it to guide machine learning predictions of druggability.",{"name":82,"@type":73,"acceptedAnswer":83},"Which machine learning models showed the best performance for drug classification?",{"text":84,"@type":76},"Random Forest, Extreme Gradient Boost (XGBoost), and Decision Tree classifiers achieved near-perfect accuracy, with reported accuracies of 99.94%, 99.81%, and 99.87% 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