Identification of intrinsic hepatotoxic compounds in Polygonum multiflorum Thunb. using machine-learning methods

Hu, Xiaowen; Du, Tingting; Dai, Shuguang; Wei, Feng; Chen, Xiaoguang; Ma, Shuang‐Cheng

doi:10.1016/j.jep.2022.115620

Cited by 7 publications

(2 citation statements)

References 25 publications

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“…With advancements in machine learning, researchers have turned their attention to the use of machine learning applications for evaluating drug-induced injuries. Hu [ 57 ] used SVM and in vitro screening to predict and validate the risk of idiosyncratic drug-induced liver injuries caused by the natural products in Polygonum multiflorum Thunb, and provided a powerful tool to screen large datasets for toxicants. He [ 58 ] established a large-scale dataset focused on TCM-induced hepatoprotection to train machine learning models such as RF and voting models.…”

Section: Applications Of Machine Learning In Tcm Researchmentioning

confidence: 99%

“…Natural product development Deep neurol network [47,50], RF [53,54,58,59,93], SVM [51,53,54,57,59,93], DT [59,93], neural network [53,59] RF was better than SVM, neurol network and DT in screening hepatotoxic compounds [59]. RF model is more accurate than SVM and DT in identifying molecular characteristics of natural product compounds with the meridians of TCM [93] Disease diagnosis SVM [10,61,66,[81][82][83], DT [68,[81][82][83], neural network [45, 61-63, 65, 82, 83], RF [61,64,67,82,83], CNN [64,67,[70][71][72][73][74][75][76][77][78]81], RNN…”

Section: Performance Of the Algorithmmentioning

confidence: 99%

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Machine learning in TCM with natural products and molecules: current status and future perspectives

Liu

et al. 2023

Chin Med

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Traditional Chinese medicine (TCM) has been practiced for thousands of years with clinical efficacy. Natural products and their effective agents such as artemisinin and paclitaxel have saved millions of lives worldwide. Artificial intelligence is being increasingly deployed in TCM. By summarizing the principles and processes of deep learning and traditional machine learning algorithms, analyzing the application of machine learning in TCM, reviewing the results of previous studies, this study proposed a promising future perspective based on the combination of machine learning, TCM theory, chemical compositions of natural products, and computational simulations based on molecules and chemical compositions. In the first place, machine learning will be utilized in the effective chemical components of natural products to target the pathological molecules of the disease which could achieve the purpose of screening the natural products on the basis of the pathological mechanisms they target. In this approach, computational simulations will be used for processing the data for effective chemical components, generating datasets for analyzing features. In the next step, machine learning will be used to analyze the datasets on the basis of TCM theories such as the superposition of syndrome elements. Finally, interdisciplinary natural product-syndrome research will be established by unifying the results of the two steps outlined above, potentially realizing an intelligent artificial intelligence diagnosis and treatment model based on the effective chemical components of natural products under the guidance of TCM theory. This perspective outlines an innovative application of machine learning in the clinical practice of TCM based on the investigation of chemical molecules under the guidance of TCM theory.

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Section: Applications Of Machine Learning In Tcm Researchmentioning

confidence: 99%

Section: Performance Of the Algorithmmentioning

confidence: 99%

Machine learning in TCM with natural products and molecules: current status and future perspectives

Liu

et al. 2023

Chin Med

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Processing‐induced reduction in dianthrones content and toxicity of Polygonum multiflorum: Insights from ultra‐high performance liquid chromatography triple quadrupole mass spectrometry analysis and toxicological assessment

Li,

Wang,

Qiu

et al. 2024

Anim Models and Exp Med

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BackgroundPolygonum multiflorum‐induced liver injury (PM‐DILI) has significantly hindered its clinical application and development.MethodsThis study investigates the variation in content and toxicity of dianthrones, the toxic components of P. multiflorum, during different processing cycles. We employed the ultra‐high‐performance liquid chromatography triple quadrupole mass spectrometry method to quantify six dianthrones in raw P. multiflorum and formulations processed with a method called nine cycles of steaming and sunning. Additionally, toxicity assessments were conducted using human normal liver cell line L02 and zebrafish embryos.ResultsResults indicate a gradual reduction in dianthrones content with increasing processing cycles. Processed formulations exhibited significantly reduced cytotoxicity in L02 cells and hepatotoxicity in zebrafish embryos.ConclusionsOur findings elucidate the relationship between processing cycles and P. multiflorum toxicity, providing theoretical support for its safe use.

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Comprehensive characterization of natural products of Polygonum multiflorum by cheminformatics analysis

Hu¹,

Du²,

Wang³

et al. 2023

Pharmacological Research - Modern Chinese Medicine

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Identification of intrinsic hepatotoxic compounds in Polygonum multiflorum Thunb. using machine-learning methods

Cited by 7 publications

References 25 publications

Machine learning in TCM with natural products and molecules: current status and future perspectives

Machine learning in TCM with natural products and molecules: current status and future perspectives

Processing‐induced reduction in dianthrones content and toxicity of Polygonum multiflorum: Insights from ultra‐high performance liquid chromatography triple quadrupole mass spectrometry analysis and toxicological assessment

Comprehensive characterization of natural products of Polygonum multiflorum by cheminformatics analysis

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