A Machine Learning-Based Approach for the Prediction of Anticoagulant Activity of Hypericum perforatum L. and Evaluation of Compound Activity.

IF 3 3区 生物学 Q2 BIOCHEMICAL RESEARCH METHODS
Zhiyong Zhang, Wennan Nie, Yijing Zhang, Mulan He, Cunhao Li, Shule Zhang, Wenlong Li
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引用次数: 0

Abstract

Introduction: Hypericum perforatum L. (HPL) is extensively researched domestically and internationally as a medicinal plant. However, no reports of studies related to the anticoagulant activity of HPL have been retrieved. The specific bioactive components are unknown.

Objective: The aim of this study was to develop a machine learning (ML) method for rapid prediction of anticoagulant activity in HPL and evaluation of compound activity.

Materials and methods: First, an in vitro anticoagulant activity assay was developed for the determination of the bioactivity of various medicinal parts of HPL. Then, the peak areas of compounds in HPL were integrated using UPLC-Q-TOF-MS analysis. Subsequently, nine independent ML methods and two ensemble learning methods have been established to predict the anticoagulant activity of HPL and to evaluate the contribution of compounds. Feature importance scores were used for models visualization.

Results: A total of 24 compounds were shown to exhibited superior anticoagulant activity. Molecular docking experiments likewise confirmed this result. The results show that the branches of HPL have excellent anticoagulant activity, which has been previously overlooked. The established ML model demonstrated good performance in the prediction of the activity of HPL.

Conclusion: The results were accurate and reliable, which significantly improved the efficiency of active compounds screening, and further exploration in this area is warranted.

基于机器学习的金丝桃抗凝血活性预测及化合物活性评估方法。
导言:贯叶连翘(HPL)作为一种药用植物在国内外受到广泛研究。然而,目前尚未检索到有关金丝桃抗凝血活性的研究报告。具体的生物活性成分尚不清楚:本研究旨在开发一种机器学习(ML)方法,用于快速预测 HPL 的抗凝血活性并评估化合物的活性:首先,开发了一种体外抗凝血活性测定方法,用于测定 HPL 不同药用部位的生物活性。然后,利用 UPLC-Q-TOF-MS 分析法对 HPL 中化合物的峰面积进行整合。随后,建立了九种独立的 ML 方法和两种集合学习方法来预测 HPL 的抗凝活性并评估化合物的贡献。特征重要性得分被用于模型的可视化:结果:共有 24 种化合物表现出卓越的抗凝血活性。分子对接实验同样证实了这一结果。结果表明,HPL 的分支具有出色的抗凝血活性,而这一点之前一直被忽视。建立的 ML 模型在预测 HPL 活性方面表现出良好的性能:结果准确可靠,大大提高了活性化合物筛选的效率,值得在该领域进一步探索。
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来源期刊
Phytochemical Analysis
Phytochemical Analysis 生物-分析化学
CiteScore
6.00
自引率
6.10%
发文量
88
审稿时长
1.7 months
期刊介绍: Phytochemical Analysis is devoted to the publication of original articles concerning the development, improvement, validation and/or extension of application of analytical methodology in the plant sciences. The spectrum of coverage is broad, encompassing methods and techniques relevant to the detection (including bio-screening), extraction, separation, purification, identification and quantification of compounds in plant biochemistry, plant cellular and molecular biology, plant biotechnology, the food sciences, agriculture and horticulture. The Journal publishes papers describing significant novelty in the analysis of whole plants (including algae), plant cells, tissues and organs, plant-derived extracts and plant products (including those which have been partially or completely refined for use in the food, agrochemical, pharmaceutical and related industries). All forms of physical, chemical, biochemical, spectroscopic, radiometric, electrometric, chromatographic, metabolomic and chemometric investigations of plant products (monomeric species as well as polymeric molecules such as nucleic acids, proteins, lipids and carbohydrates) are included within the remit of the Journal. Papers dealing with novel methods relating to areas such as data handling/ data mining in plant sciences will also be welcomed.
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