Pharmacology and Mechanisms of Natural Medicine in Treatment of Type 2 Diabetes Mellitus

Karmakar, Tanushree; Chaki, Rituparna; Ghosh, Nilanjan

doi:10.1007/978-981-15-8127-4_49

Cited by 2 publications

(1 citation statement)

References 172 publications

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“…A growing number of studies have proved that natural products, including herbal formulations and their extracts, can be used to treat diabetes. To further evaluate the proposed model’s performance, we collected 13 natural products from relevant literature that are undergoing clinical trials against diabetes as positive samples. − We also obtained 52 natural products unrelated to diabetes and its complications, obesity, and anti-inflammatory effects as negative samples. By inputting this independent data into our model, the performance of the model was examined.…”

Section: Case Analysismentioning

confidence: 99%

iPADD: A Computational Tool for Predicting Potential Antidiabetic Drugs Using Machine Learning Algorithms

Liu

Shi

Gao

et al. 2023

J. Chem. Inf. Model.

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Diabetes mellitus is a chronic metabolic disease, which causes an imbalance in blood glucose homeostasis and further leads to severe complications. With the increasing population of diabetes, there is an urgent need to develop drugs to treat diabetes. The development of artificial intelligence provides a powerful tool for accelerating the discovery of antidiabetic drugs. This work aims to establish a predictor called iPADD for discovering potential antidiabetic drugs. In the predictor, we used four kinds of molecular fingerprints and their combinations to encode the drugs and then adopted minimum-redundancy−maximum-relevance (mRMR) combined with an incremental feature selection strategy to screen optimal features. Based on the optimal feature subset, eight machine learning algorithms were applied to train models by using 5-fold cross-validation. The best model could produce an accuracy (Acc) of 0.983 with the area under the receiver operating characteristic curve (auROC) value of 0.989 on an independent test set. To further validate the performance of iPADD, we selected 65 natural products for case analysis, including 13 natural products in clinical trials as positive samples and 52 natural products as negative samples. Except for abscisic acid, our model can give correct prediction results. Molecular docking illustrated that quercetin and resveratrol stably bound with the diabetes target NR1I2. These results are consistent with the model prediction results of iPADD, indicating that the machine learning model has a strong generalization ability. The source code of iPADD is available at https:// github.com/llllxw/iPADD.

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Section: Case Analysismentioning

confidence: 99%

iPADD: A Computational Tool for Predicting Potential Antidiabetic Drugs Using Machine Learning Algorithms

Liu

Shi

Gao

et al. 2023

J. Chem. Inf. Model.

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Recent Developments in Drug Design of Oral Synthetic Free Fatty Acid Receptor 1 Agonists

Liu,

Zhang,

et al. 2024

DDDT

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Over the past two decades, synthetic FFAR1 agonists such as TAK-875 and TSL1806 have undergone meticulous design and extensive clinical trials. However, due to issues primarily related to hepatotoxicity, no FFAR1 agonist has yet received regulatory approval. Research into the sources of hepatotoxicity suggests that one potential cause lies in the molecular structure itself. These structures typically feature lipid-like carboxylic acid head groups, which tend to generate toxic metabolites. Strategies to mitigate these risks focus on optimizing chemical groups to reduce lipophilicity and prevent the formation of reactive metabolites. Recent studies have concentrated on developing low-molecular-weight compounds that more closely resemble natural products, with CPL207280 showing promising potential and liver safety, currently in Phase II clinical trials. Moreover, ongoing research continues to explore the potential applications of FFAR1 agonists in diabetes management, as well as in conditions such as non-alcoholic fatty liver disease (NAFLD) and cerebrovascular diseases. Utilizing advanced technologies such as artificial intelligence and computer-aided design, the development of compact molecules that mimic natural structures represents a hopeful direction for future research and development.

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Pharmacology and Mechanisms of Natural Medicine in Treatment of Type 2 Diabetes Mellitus

Cited by 2 publications

References 172 publications

iPADD: A Computational Tool for Predicting Potential Antidiabetic Drugs Using Machine Learning Algorithms

iPADD: A Computational Tool for Predicting Potential Antidiabetic Drugs Using Machine Learning Algorithms

Recent Developments in Drug Design of Oral Synthetic Free Fatty Acid Receptor 1 Agonists

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