基于机器学习的定量模型 (LogBB_Pred)，用于预测药物化合物的血脑屏障渗透性（logBB 值）,Bioinformatics

当前位置： X-MOL 学术 › Bioinformatics › 论文详情

Our official English website, www.x-mol.net, welcomes your feedback! (Note: you will need to create a separate account there.)

基于机器学习的定量模型 (LogBB_Pred)，用于预测药物化合物的血脑屏障渗透性（logBB 值）
Bioinformatics ( IF 4.4 ) Pub Date : 2023-09-15 , DOI: 10.1093/bioinformatics/btad577
Bilal Shaker ₁ , Jingyu Lee ₁ , Yunhyeok Lee ₁ , Myeong-Sang Yu ₁ , Hyang-Mi Lee ₁ , Eunee Lee ₂ , Hoon-Chul Kang ₃ , Kwang-Seok Oh ₄ , Hyung Wook Kim ₅ , Dokyun Na ₁

Affiliation

动机对药物化合物的血脑屏障 (BBB) 渗透能力的有效评估是中枢神经系统 (CNS) 药物发现的主要障碍之一，因为实验方法既昂贵又耗时。为了推进和提高神经治疗药物发现的成功率，有必要开发一个精确的计算定量模型来确定药物的绝对 logBB 值（大脑中药物浓度与血液中药物浓度的对数比）。候选药物。结果在这里，我们开发了一个定量模型 (LogBB_Pred)，能够预测查询化合物的 logBB 值。该模型在独立测试数据集上的 R2 为 0.61，优于其他公开的定量模型。与仅对化合物是否具有 BBB 渗透性进行分类的可用定性（分类）模型相比，我们的模型与最佳定性模型达到了相同的精度 (0.85)，并且远远优于其他定性模型（精度在 0.64 - 0.70 之间））。为了进一步评估，我们的模型、定量模型和定性模型在现实世界的中枢神经系统药物筛选库上进行了评估。我们的模型的准确度为 0.97，而其他模型的准确度在 0.29 - 0.83 范围内。因此，我们的模型可以准确地对 BBB 渗透性化合物进行分类，并预测候选药物的绝对 logBB 值。可用性和实施 Web 服务器可在 http://ssbio.cau.ac.kr/software/logbb_pred/ 上免费获得。本研究中使用的数据可从 http://ssbio.cau.ac.kr/software/logbb_pred/dataset.zip 下载。

"点击查看英文标题和摘要"

A machine learning-based quantitative model (LogBB_Pred) to predict the blood-brain barrier permeability (logBB value) of drug compounds

Motivation Efficient assessment of the blood-brain barrier (BBB) penetration ability of a drug compound is one of the major hurdles in central nervous system (CNS) drug discovery since experimental methods are costly and time-consuming. To advance and elevate the success rate of neurotherapeutic drug discovery, it is essential to develop an accurate computational quantitative model to determine the absolute logBB value (a logarithmic ratio of the concentration of a drug in the brain to its concentration in the blood) of a drug candidate. Results Here, we developed a quantitative model (LogBB_Pred) capable of predicting a logBB value of a query compound. The model achieved an R2 of 0.61 on an independent test dataset and outperformed other publicly available quantitative models. When compared with the available qualitative (classification) models that only classified whether a compound is BBB-permeable or not, our model achieved the same accuracy (0.85) with the best qualitative model and far-outperformed other qualitative models (accuracies between 0.64 - 0.70). For further evaluation, our model, quantitative models, and the qualitative models were evaluated on a real-world CNS drug screening library. Our model showed an accuracy of 0.97 while the other models showed an accuracy in the range of 0.29 - 0.83. Consequently, our model can accurately classify BBB-permeable compounds as well as predict the absolute logBB values of drug candidates. Availability and implementation Web server is freely available on the web at http://ssbio.cau.ac.kr/software/logbb_pred/. The data used in this study is available to download at http://ssbio.cau.ac.kr/software/logbb_pred/dataset.zip.

更新日期：2023-09-15

点击分享查看原文

点击收藏

公开下载

阅读更多本刊新发论文本刊介绍/投稿指南