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使用可解释回归模型对甲型流感病毒气味剂进行筛选和验证

Screening and Validation of Odorants against Influenza A Virus Using Interpretable Regression Models.

作者信息

Jasial Swarit, Hu Jieying, Miyao Tomoyuki, Hirama Yui, Onishi Shintaro, Matsui Ryoichi, Osaki Koji, Funatsu Kimito

机构信息

Data Science Center and Graduate School of Science and Technology, Nara Institute of Science and Technology, 8916-5 Takayama-cho, Ikoma, Nara630-0192, Japan.

Material Science Research, Kao Corporation, 1334 Minato, Wakayama-shi, Wakayama640-8580, Japan.

出版信息

ACS Pharmacol Transl Sci. 2022 Dec 15;6(1):139-150. doi: 10.1021/acsptsci.2c00193. eCollection 2023 Jan 13.

Abstract

Influenza is a respiratory infection caused by the influenza virus that is prevalent worldwide. One of the most contagious variants of influenza is influenza A virus (IAV), which usually spreads in closed spaces through aerosols. Preventive measures such as novel compounds are needed that can act on viral membranes and provide a safe environment against IAV infection. In this study, we screened compounds with common fragrances that are generally used to mask unpleasant odors but can also exhibit antiviral activity against a strain of IAV. Initially, a set of 188 structurally diverse odorants were collected, and their antiviral activity was measured in vapor phase against the IAV solution. Regression models were built for the prediction of antiviral activity using this set of odorants by taking into account their structural features along with vapor pressure and partition coefficient (-octanol/water). The models were interpreted using a feature weighting approach and Shapley Additive exPlanations to rationalize the predictions as an additional validation for virtual screening. This model was used to screen odorants from an in-house odorant data set consisting of 2020 odorants, which were later evaluated using experiments. Out of 11 odorants proposed using the final model, 8 odorants were found to exhibit antiviral activity. The feature interpretation of screened odorants suggested that they contained hydrophilic substructures, such as hydroxyl group, which might contribute to denaturation of proteins on the surface of the virus. These odorants should be explored as a preventive measure in closed spaces to decrease the risk of infections of IAV.

摘要

流感是一种由流感病毒引起的呼吸道感染,在全球范围内流行。流感最具传染性的变种之一是甲型流感病毒(IAV),它通常在封闭空间中通过气溶胶传播。需要诸如新型化合物之类的预防措施,这些化合物可以作用于病毒膜,并提供一个安全的环境来抵御IAV感染。在本研究中,我们筛选了具有常见香味的化合物,这些化合物通常用于掩盖难闻气味,但也可能对一种IAV毒株具有抗病毒活性。最初,收集了一组188种结构多样的气味剂,并在气相中测量它们对IAV溶液的抗病毒活性。通过考虑这组气味剂的结构特征以及蒸气压和分配系数(-辛醇/水),建立了回归模型来预测抗病毒活性。使用特征加权方法和Shapley加性解释对模型进行解释,以使预测合理化,作为虚拟筛选的额外验证。该模型用于从一个由2020种气味剂组成的内部气味剂数据集中筛选气味剂,随后通过实验对其进行评估。在使用最终模型提出的11种气味剂中,发现有8种气味剂具有抗病毒活性。对筛选出的气味剂的特征解释表明,它们含有亲水性亚结构,如羟基,这可能有助于病毒表面蛋白质的变性。这些气味剂应作为一种预防措施在封闭空间中进行探索,以降低IAV感染的风险。

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5
Influenza.流感。
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6
Mol2vec: Unsupervised Machine Learning Approach with Chemical Intuition.Mol2vec:具有化学直觉的无监督机器学习方法。
J Chem Inf Model. 2018 Jan 22;58(1):27-35. doi: 10.1021/acs.jcim.7b00616. Epub 2018 Jan 10.
8
Visualization and Interpretation of Support Vector Machine Activity Predictions.支持向量机活动预测的可视化和解释。
J Chem Inf Model. 2015 Jun 22;55(6):1136-47. doi: 10.1021/acs.jcim.5b00175. Epub 2015 Jun 2.
9
Machine-learning approaches in drug discovery: methods and applications.药物发现中的机器学习方法:方法与应用。
Drug Discov Today. 2015 Mar;20(3):318-31. doi: 10.1016/j.drudis.2014.10.012. Epub 2014 Nov 4.
10
In silico virtual screening approaches for anti-viral drug discovery.用于抗病毒药物发现的计算机虚拟筛选方法。
Drug Discov Today Technol. 2012 Autumn;9(3):e219-25. doi: 10.1016/j.ddtec.2012.07.009.

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