使用手性敏感描述符和机器学习预测内分泌干扰物的毒性和血脑屏障通透性

IF 3.1 Q2 TOXICOLOGY
Anish Gomatam , Blessy Joseph , Ulka Gawde , Kavita Raikuvar , Evans Coutinho
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引用次数: 1

摘要

雌激素受体(ER)介导的内分泌干扰和血脑屏障(BBB)渗透性是任何候选药物必须评估的两个关键药理学终点。然而,实验测试既昂贵又耗时,近年来,定量结构-性质关系(QSPRs)已经成为一种可行的硅替代品。然而,大多数报道的关于内质网毒性和血脑屏障通透性的QSPR模型都是使用二维描述符进行的,而已经确定内质网结合和血脑屏障通透性是立体选择过程,其中分子中原子的空间排列起着关键作用。目前的研究使用一种名为“特征值分析(EVANS)”的手性敏感3D-QSPR方法来解决这个问题。EVANS方法将来自3D分子结构的信息与2D物理化学性质相结合,生成特征值,这些特征值用作QSPR建模中的描述符。对于手性化合物,EVANS通过考虑过多对映体状态的距离属性来计算描述符,从而计算多个构象对特定生物端点的贡献。我们将EVANS方法与机器学习算法相结合,建立了雌激素受体(ER)介导的内分泌干扰和血脑屏障通透性的预测QSPR模型。对132个化学实体数据集的ER结合进行回归分析,得到了一个稳健的预测模型,支持向量机模型的rtrain2=0.84, rtest2=0.70。607种化学物质的血脑屏障渗透率分类模型也显示出较高的预测准确率,其中人工神经网络模型的预测准确率为0.85,AUC为0.82,精密度为0.85,F1分数为0.89。为了比较,传统的2D-QSPR模型也为这些端点建立,并且观察到EVANS生成的特征值优于标准2D-QSPR中使用的描述符。总的来说,我们的研究结果表明,EVANS是一种强大的3D-QSPR方法,与现有的QSAR/QSPR方法相比,它具有许多优势,可以作为一种有用的计算工具,用于新药和现有药物的药理学和毒理学评估。
本文章由计算机程序翻译,如有差异,请以英文原文为准。

Predicting toxicity of endocrine disruptors and blood–brain barrier permeability using chirality-sensitive descriptors and machine learning

Predicting toxicity of endocrine disruptors and blood–brain barrier permeability using chirality-sensitive descriptors and machine learning

Estrogen receptor (ER) mediated endocrine disruption and blood–brain barrier (BBB) permeability are two crucial pharmacological endpoints that must be assessed for any drug candidate. However, experimental testing is expensive and time-consuming, and in recent years, Quantitative Structure-Property Relationships (QSPRs) have emerged as a viable in silico alternative. However, most QSPR models reported on ER toxicity and BBB permeability have been carried out using 2D descriptors, whereas it has been established that ER binding and BBB permeability are stereoselective processes in which the spatial arrangement of atoms in the molecule plays a key role. The current study addresses this problem using a chirality-sensitive 3D-QSPR methodology entitled ‘EigenValue ANalysiS (EVANS). The EVANS approach merges information from 3D molecular structure with 2D physicochemical properties to generate eigenvalues which are used as descriptors in QSPR modelling. For chiral compounds, EVANS computes descriptors by considering distance attributes from a plethora of enantiomeric states, thereby accounting for the contributions of multiple conformers towards a particular biological endpoint. We deploy the EVANS methodology with machine learning algorithms to build predictive QSPR models for estrogen receptor (ER) mediated endocrine disruption and BBB permeability. Regression analyses of ER binding on a dataset of 132 chemical entities returned a robust and predictive model, with the support vector machine model having rtrain2=0.84 and rtest2=0.70. Classification models for BBB permeability on a dataset of 607 chemicals also showed high prediction accuracy, with the artificial neural network model showing the best performance (Accuracy = 0.85, AUC = 0.82, precision = 0.85, F1 score = 0.89). For comparison, conventional 2D-QSPR models were also built for these endpoints, and it was observed that EVANS generates eigenvalues that are superior to descriptors used in standard 2D-QSPR. Overall, our results demonstrate that EVANS is a powerful 3D-QSPR methodology that offers several advantages over existing QSAR/QSPR methods, and can be a useful computational tool in the pharmacological and toxicological evaluation of new and existing drugs.

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来源期刊
Computational Toxicology
Computational Toxicology Computer Science-Computer Science Applications
CiteScore
5.50
自引率
0.00%
发文量
53
审稿时长
56 days
期刊介绍: Computational Toxicology is an international journal publishing computational approaches that assist in the toxicological evaluation of new and existing chemical substances assisting in their safety assessment. -All effects relating to human health and environmental toxicity and fate -Prediction of toxicity, metabolism, fate and physico-chemical properties -The development of models from read-across, (Q)SARs, PBPK, QIVIVE, Multi-Scale Models -Big Data in toxicology: integration, management, analysis -Implementation of models through AOPs, IATA, TTC -Regulatory acceptance of models: evaluation, verification and validation -From metals, to small organic molecules to nanoparticles -Pharmaceuticals, pesticides, foods, cosmetics, fine chemicals -Bringing together the views of industry, regulators, academia, NGOs
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