Prediction of Human Drug Targets and Their Interactions Using Machine Learning Methods: Current and Future Perspectives

2018 ◽  
pp. 21-30 ◽  
Author(s):  
Abhigyan Nath ◽  
Priyanka Kumari ◽  
Radha Chaube
Keyword(s):  
Machine Learning ◽  
Drug Targets ◽  
Future Perspectives ◽  
Learning Methods ◽  
Machine Learning Methods

Recent Advances on Antioxidant Identification Based on Machine Learning Methods

2020 ◽  
Vol 21 (10) ◽  
pp. 804-809
Author(s):  
Pengmian Feng ◽  
Lijing Feng
Keyword(s):  
Machine Learning ◽  
Antioxidant Activity ◽  
Free Radicals ◽  
Computational Methods ◽  
Future Perspectives ◽  
Learning Methods ◽  
The Past ◽  
Machine Learning Methods ◽  
Recent Advances

Antioxidants are molecules that can prevent damages to cells caused by free radicals. Recent studies also demonstrated that antioxidants play roles in preventing diseases. However, the number of known molecules with antioxidant activity is very small. Therefore, it is necessary to identify antioxidants from various resources. In the past several years, a series of computational methods have been proposed to identify antioxidants. In this review, we briefly summarized recent advances in computationally identifying antioxidants. The challenges and future perspectives for identifying antioxidants were also discussed. We hope this review will provide insights into researches on antioxidant identification.

scholarly journals Network Analyses Based on Machine Learning Methods to Quantify Effects of Peptide–Protein Complexes as Drug Targets Using Cinnamon in Cardiovascular Diseases and Metabolic Syndrome as a Case Study

2021 ◽  
Vol 12 ◽  
Author(s):  
Yingying Wang ◽  
Lili Wang ◽  
Yinhe Liu ◽  
Keshen Li ◽  
Honglei Zhao
Keyword(s):  
Machine Learning ◽  
Metabolic Syndrome ◽  
Cardiovascular Diseases ◽  
Drug Targets ◽  
Protein Complexes ◽  
Deep Understanding ◽  
Learning Methods ◽  
Network Analyses ◽  
Machine Learning Methods

Peptide–protein complexes play important roles in multiple diseases such as cardiovascular diseases (CVDs) and metabolic syndrome (MetS). The peptides may be the key molecules in the designing of inhibitors or drug targets. Many Chinese traditional drugs are shown to play various roles in different diseases, and comprehensive analyses should be performed using networks which could offer more information than results generated from a single level. In this study, a network analysis pipeline was designed based on machine learning methods to quantify the effects of peptide–protein complexes as drug targets. Three steps, namely, pathway filter, combined network construction, and biomarker prediction and validation based on peptides, were performed using cinnamon (CA) in CVDs and MetS as a case. Results showed that 17 peptide–protein complexes including six peptides and four proteins were identified as CA targets. The expressions of AKT1, AKT2, and ENOS were tested using qRT-PCR in a mouse model that was constructed. AKT2 was shown to be a CA-indicating biomarker, while E2F1 and ENOS were CA treatment targets. AKT1 was considered a diabetic responsive biomarker because it was down-regulated in diabetic but not related to CA. Taken together, the pipeline could identify new drug targets based on biological function analyses. This may provide a deep understanding of the drugs’ roles in different diseases which may foster the development of peptide–protein complex–based therapeutic approaches.

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