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

IF 4.4 3区 生物学 Q1 BIOCHEMICAL RESEARCH METHODS Bioinformatics Pub Date : 2023-10-03 DOI:10.1093/bioinformatics/btad577
Bilal Shaker, Jingyu Lee, Yunhyeok Lee, Myeong-Sang Yu, Hyang-Mi Lee, Eunee Lee, Hoon-Chul Kang, Kwang-Seok Oh, Hyung Wook Kim, Dokyun Na
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引用次数: 1

Abstract

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 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 and 0.70). For further evaluation, our model, quantitative models, and the qualitative models were evaluated on a real-world central nervous system 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 are available to download at http://ssbio.cau.ac.kr/software/logbb_pred/dataset.zip.

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一种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通透性(LogBB值)。
动机:有效评估药物化合物的血脑屏障(BBB)穿透能力是中枢神经系统药物发现的主要障碍之一,因为实验方法成本高昂且耗时。为了推进和提高神经治疗药物发现的成功率,必须开发一个准确的计算定量模型来确定候选药物的绝对logBB值(大脑中药物浓度与血液中药物浓度的对数比)。结果:在这里,我们开发了一个能够预测查询化合物的LogBB值的定量模型(LogBB_Pred)。该模型在独立测试数据集上获得了0.61的R2,并优于其他公开可用的定量模型。与只分类化合物是否具有血脑屏障渗透性的现有定性(分类)模型相比,我们的模型获得了与最佳定性模型相同的准确度(0.85),并且远远优于其他定性模型(准确度在0.64和0.70之间)。为了进一步评估,并在真实世界的中枢神经系统药物筛选库中对定性模型进行评估。我们的模型显示出0.97的准确度,而其他模型显示出0.29-0.83的准确度。因此,我们的模型可以准确地对血脑屏障可渗透的化合物进行分类,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/.本研究中使用的数据可在http://ssbio.cau.ac.kr/software/logbb_pred/dataset.zip.
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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来源期刊
Bioinformatics
Bioinformatics 生物-生化研究方法
CiteScore
11.20
自引率
5.20%
发文量
753
审稿时长
2.1 months
期刊介绍: The leading journal in its field, Bioinformatics publishes the highest quality scientific papers and review articles of interest to academic and industrial researchers. Its main focus is on new developments in genome bioinformatics and computational biology. Two distinct sections within the journal - Discovery Notes and Application Notes- focus on shorter papers; the former reporting biologically interesting discoveries using computational methods, the latter exploring the applications used for experiments.
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