ABSTRACT

High-dimensionality is one of the major problems which affect the quality of the quantitative structure–activity relationship (QSAR) modelling. Obtaining a reliable QSAR model with few descriptors is an essential procedure in chemometrics. The binary grasshopper optimization algorithm (BGOA) is a new meta-heuristic optimization algorithm, which has been used successfully to perform feature selection. In this paper, four new transfer functions were adapted to improve the exploration and exploitation capability of the BGOA in QSAR modelling of influenza A viruses (H1N1). The QSAR model with these new quadratic transfer functions was internally and externally validated based on MSE_train, Y-randomization test, MSE_test, and the applicability domain (AD). The validation results indicate that the model is robust and not due to chance correlation. In addition, the results indicate that the descriptor selection and prediction performance of the QSAR model for training dataset outperform the other S-shaped and V-shaped transfer functions. QSAR model using quadratic transfer function shows the lowest MSE_train. For the test dataset, proposed QSAR model shows lower value of MSE_test compared with the other methods, indicating its higher predictive ability. In conclusion, the results reveal that the proposed QSAR model is an efficient approach for modelling high-dimensional QSAR models and it is useful for the estimation of IC₅₀ values of neuraminidase inhibitors that have not been experimentally tested.

摘要

高维是影响定量构效关系（QSAR）建模质量的主要问题之一。获得具有少量描述符的可靠 QSAR 模型是化学计量学中必不可少的过程。二元蚱蜢优化算法（BGOA）是一种新的元启发式优化算法，已成功用于执行特征选择。在本文中，采用了四种新的传递函数来提高 BGOA 在甲型流感病毒 (H1N1) 的 QSAR 建模中的探索和利用能力。具有这些新的二次传递函数的 QSAR 模型在 MSE_训练、Y 随机化测试、MSE_{测试的基础上进行了内部和外部验证}, 和适用域 (AD)。验证结果表明该模型是稳健的，而不是由于机会相关性。此外，结果表明，用于训练数据集的 QSAR 模型的描述符选择和预测性能优于其他 S 形和 V 形传递函数。使用二次传递函数的 QSAR 模型显示了最低的 MSE_序列。对于测试数据集，提出的QSAR模型与其他方法相比，MSE_测试值较低，表明其具有较高的预测能力。总之，结果表明，所提出的 QSAR 模型是一种高效的高维 QSAR 模型建模方法，可用于 IC _{50的估计。}未经实验测试的神经氨酸酶抑制剂的值。