A modified binary particle swarm optimization with a machine learning algorithm and molecular docking for QSAR modelling of cholinesterase inhibitors
Author:
Affiliation:
1. Department of Computer Science, Faculty of Mathematics, University of Sistan and Baluchestan, Zahedan, Iran
2. Department of Chemistry, Faculty of Science, University of Sistan and Baluchestan, Zahedan, Iran
Publisher
Informa UK Limited
Subject
Drug Discovery,Molecular Medicine,General Medicine,Bioengineering
Link
https://www.tandfonline.com/doi/pdf/10.1080/1062936X.2021.1971761
Reference61 articles.
1. In silico drug discovery of Acetylcholinesterase and Butyrylcholinesterase enzymes inhibitors based on Quantitative Structure-Activity Relationship (QSAR) and drug-likeness evaluation
2. Effect of different drying techniques on flowability characteristics and chemical properties of natural carbohydrate-protein Gum from durian fruit seed
3. A novel QSAR model for prediction of apoptosis-inducing activity of 4-aryl-4-H-chromenes based on support vector machine
4. Multi-QSAR approaches for investigating the relationship between chemical structure descriptors of Thiadiazole derivatives and their corrosion inhibition performance
Cited by 2 articles. 订阅此论文施引文献 订阅此论文施引文献,注册后可以免费订阅5篇论文的施引文献,订阅后可以查看论文全部施引文献
1. Application of the LAD-LASSO as a dimensional reduction technique in the ANN-based QSAR study: Discovery of potent inhibitors using molecular docking simulation;Chemometrics and Intelligent Laboratory Systems;2022-03
2. A Deep Learning-Based Quantitative Structure–Activity Relationship System Construct Prediction Model of Agonist and Antagonist with High Performance;International Journal of Molecular Sciences;2022-02-15
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