BBBper: A Machine Learning-based Online Tool for Blood-Brain Barrier (BBB) Permeability Prediction.

CNS & neurological disorders drug targets Pub Date : 2024-10-16 DOI:10.2174/0118715273328174241007060331

Pawan Kumar, Vandana Saini, Dinesh Gupta, Pooja A Chawla, Ajit Kumar

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Abstract

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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BBBper：基于机器学习的血脑屏障 (BBB) 渗透性预测在线工具。

目的：神经元疾病已影响到全球超过15%的人口，这表明继续设计和开发可穿过血脑屏障（BBB）的药物非常重要：背景：血脑屏障对外部化合物的渗透性有 98% 的限制，以维持和调节大脑的平衡。因此，BBB渗透性预测对于预测类药物的活性至关重要。目的：在此，我们报告了利用机器学习工具开发BBBper（血脑屏障渗透性预测）的情况：方法：我们开发了一种基于机器学习的在线监督工具，以理化参数为基础，预测特定化合物的血脑屏障渗透性。用户端网页使用 HTML 开发，并通过 python 脚本与后端服务器连接，以运行用户查询和结果：结果：BBBper 在后端使用随机森林算法，在外部数据集上显示出 97% 的准确率，而目前可用的基于网络的 BBB 渗透性预测工具的准确率为 70-92%：BBBper 网络工具可在 http://bbbper.mdu.ac.in 免费获取。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

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CNS & neurological disorders drug targets

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