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首页> 外文期刊>Molecular informatics >Development of Predictive QSAR Models of 4‐Thiazolidinones Antitrypanosomal Activity Using Modern Machine Learning Algorithms
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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.
机译：摘要本文提出了新型QSAR模型，用于预测噻唑烷和相关杂环中的抗酸酐活性。四种机器学习算法的性能：随机森林回归，随机梯度提升，多变量自适应回归花键和高斯过程回归，以达到更好的预测性。随机森林和高斯工艺回归的结果是可比的和优异的其他研究方法。具有Boruta方法的初步描述符选择改善了机器学习方法的结果。使用随机森林和高斯过程中开发的两种新颖的QSAR模型回归算法具有良好的预测能力，这通过对应的Q 2 ext = 0.812和Q 2 ext = 0.830的测试集的外部评估证明。所获得的模型可以进一步用于在相同的化学结构域中的虚拟文库中的硅筛选，以寻找新的抗糖基因组体。对描述符的彻底分析QSAR模型的影响和对化学意义的解释允许突出许多结构活动关系。苯基环的存在与释放的原子或基团在对位置，芳香环数增加，高分支但短链，高同质能量，以及将1取代的2-吲哚基片段引入分子结构中被认为是胰蛋白妇女活动先决条件。

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