Development of Predictive QSAR Models of 4‐Thiazolidinones Antitrypanosomal Activity Using Modern Machine Learning Algorithms

Kryshchyshyn Anna; Devinyak Oleg; Kaminskyy Danylo; Grellier Philippe; Lesyk Roman

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Development of Predictive QSAR Models of 4‐Thiazolidinones Antitrypanosomal Activity Using Modern Machine Learning Algorithms

机译：现代机械学习算法开发4-噻唑烷酮抗糖粉活性的预测QSAR模型

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Abstract This paper presents novel QSAR models for the prediction of antitrypanosomal activity among thiazolidines and related heterocycles. The performance of four machine learning algorithms: Random Forest regression, Stochastic gradient boosting, Multivariate adaptive regression splines and Gaussian processes regression have been studied in order to reach better levels of predictivity. The results for Random Forest and Gaussian processes regression are comparable and outperform other studied methods. The preliminary descriptor selection with Boruta method improved the outcome of machine learning methods. The two novel QSAR‐models developed with Random Forest and Gaussian processes regression algorithms have good predictive ability, which was proved by the external evaluation of the test set with corresponding Q 2 ext =0.812 and Q 2 ext =0.830. The obtained models can be used further for in silico screening of virtual libraries in the same chemical domain in order to find new antitrypanosomal agents. Thorough analysis of descriptors influence in the QSAR models and interpretation of their chemical meaning allows to highlight a number of structure‐activity relationships. The presence of phenyl rings with electron‐withdrawing atoms or groups in para‐position, increased number of aromatic rings, high branching but short chains, high HOMO energy, and the introduction of 1‐substituted 2‐indolyl fragment into the molecular structure have been recognized as trypanocidal activity prerequisites.

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来源
《Molecular informatics》 |2018年第5期|共13页
作者
Kryshchyshyn Anna; Devinyak Oleg; Kaminskyy Danylo; Grellier Philippe; Lesyk Roman;
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作者单位

Department of Pharmaceutical Organic and Bioorganic ChemistryDanylo Halytsky Lviv National Medical;

Department of Pharmaceutical DisciplinesUzhgorod National UniversityNarodna sq. 1 88000 Uzhgorod;

Department of Pharmaceutical Organic and Bioorganic ChemistryDanylo Halytsky Lviv National Medical;

National Museum of Natural HistorySorbonne UniversitésCP 52 57 Rue Cuvier Paris 75005 France;

Department of Pharmaceutical Organic and Bioorganic ChemistryDanylo Halytsky Lviv National Medical;

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原文格式 PDF
正文语种 eng
中图分类普通生物学;
关键词
Thiazolidinones; Structure-activity relationships; Antitrypanosomal activity; QSAR;

机译：噻唑烷酮;结构 - 活性关系;抗酸酯活性;QSAR;

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Development of Predictive QSAR Models of 4‐Thiazolidinones Antitrypanosomal Activity Using Modern Machine Learning Algorithms

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