{"title":"BBBper：基于机器学习的血脑屏障 (BBB) 渗透性预测在线工具。","authors":"Pawan Kumar, Vandana Saini, Dinesh Gupta, Pooja A Chawla, Ajit Kumar","doi":"10.2174/0118715273328174241007060331","DOIUrl":null,"url":null,"abstract":"<p><strong>Aims: </strong>Neuronal disorders have affected more than 15% of the world's population, signifying the importance of continued design and development of drugs that can cross the Blood-Brain Barrier (BBB).</p><p><strong>Background: </strong>BBB limits the permeability of external compounds by 98% to maintain and regulate brain homeostasis. Hence, BBB permeability prediction is vital to predict the activity of a drug-like substance.</p><p><strong>Objective: </strong>Here, we report about developing BBBper (Blood-Brain Barrier permeability prediction) using machine learning tool.</p><p><strong>Method: </strong>A supervised machine learning-based online tool, based on physicochemical parameters to predict the BBB permeability of given chemical compounds was developed. The user-end webpage was developed in HTML and linked with back-end server by a python script to run user queries and results.</p><p><strong>Result: </strong>BBBper uses a random forest algorithm at the back end, showing 97% accuracy on the external dataset, compared to 70-92% accuracy of currently available web-based BBB permeability prediction tools.</p><p><strong>Conclusion: </strong>The BBBper web tool is freely available at http://bbbper.mdu.ac.in.</p>","PeriodicalId":93947,"journal":{"name":"CNS & neurological disorders drug targets","volume":" ","pages":""},"PeriodicalIF":0.0000,"publicationDate":"2024-10-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"BBBper: A Machine Learning-based Online Tool for Blood-Brain Barrier (BBB) Permeability Prediction.\",\"authors\":\"Pawan Kumar, Vandana Saini, Dinesh Gupta, Pooja A Chawla, Ajit Kumar\",\"doi\":\"10.2174/0118715273328174241007060331\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<p><strong>Aims: </strong>Neuronal disorders have affected more than 15% of the world's population, signifying the importance of continued design and development of drugs that can cross the Blood-Brain Barrier (BBB).</p><p><strong>Background: </strong>BBB limits the permeability of external compounds by 98% to maintain and regulate brain homeostasis. Hence, BBB permeability prediction is vital to predict the activity of a drug-like substance.</p><p><strong>Objective: </strong>Here, we report about developing BBBper (Blood-Brain Barrier permeability prediction) using machine learning tool.</p><p><strong>Method: </strong>A supervised machine learning-based online tool, based on physicochemical parameters to predict the BBB permeability of given chemical compounds was developed. The user-end webpage was developed in HTML and linked with back-end server by a python script to run user queries and results.</p><p><strong>Result: </strong>BBBper uses a random forest algorithm at the back end, showing 97% accuracy on the external dataset, compared to 70-92% accuracy of currently available web-based BBB permeability prediction tools.</p><p><strong>Conclusion: </strong>The BBBper web tool is freely available at http://bbbper.mdu.ac.in.</p>\",\"PeriodicalId\":93947,\"journal\":{\"name\":\"CNS & neurological disorders drug targets\",\"volume\":\" \",\"pages\":\"\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2024-10-16\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"CNS & neurological disorders drug targets\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.2174/0118715273328174241007060331\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"CNS & neurological disorders drug targets","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.2174/0118715273328174241007060331","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
BBBper: A Machine Learning-based Online Tool for Blood-Brain Barrier (BBB) Permeability Prediction.
Aims: Neuronal disorders have affected more than 15% of the world's population, signifying the importance of continued design and development of drugs that can cross the Blood-Brain Barrier (BBB).
Background: BBB limits the permeability of external compounds by 98% to maintain and regulate brain homeostasis. Hence, BBB permeability prediction is vital to predict the activity of a drug-like substance.
Objective: Here, we report about developing BBBper (Blood-Brain Barrier permeability prediction) using machine learning tool.
Method: A supervised machine learning-based online tool, based on physicochemical parameters to predict the BBB permeability of given chemical compounds was developed. The user-end webpage was developed in HTML and linked with back-end server by a python script to run user queries and results.
Result: BBBper uses a random forest algorithm at the back end, showing 97% accuracy on the external dataset, compared to 70-92% accuracy of currently available web-based BBB permeability prediction tools.
Conclusion: The BBBper web tool is freely available at http://bbbper.mdu.ac.in.
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