In this paper, a novel astrophysics-based prediction framework is developed for estimating the binding affinity of a glucose binder. The proposed framework utilizes the molecule properties for predicting the binding affinity. It also uses the astrophysics-learning strategy that incorporates the concepts of Kepler’s law during the prediction process. The proposed framework is compared with 10 regression algorithms over ZINC dataset. Experimental results reveal that the proposed framework provides 99.30% accuracy of predicting binding affinity. However, decision tree provides the prediction with 97.14% accuracy. Cross-validation results show that the proposed framework provides better accuracy than the other existing models. The developed framework enables researchers to screen glucose binder rapidly. It also reduces computational time for designing small glucose binding molecule.

中文翻译：

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一种基于天体物理学的新框架，用于预测葡萄糖结合剂的结合亲和力

在本文中，开发了一种新的基于天体物理学的预测框架，用于估计葡萄糖结合剂的结合亲和力。所提出的框架利用分子特性来预测结合亲和力。它还使用天体物理学学习策略，在预测过程中结合了开普勒定律的概念。将所提出的框架与 ZINC 数据集上的 10 种回归算法进行了比较。实验结果表明，所提出的框架提供了 99.30% 的预测结合亲和力的准确度。然而，决策树提供的预测准确率为 97.14%。交叉验证结果表明，所提出的框架比其他现有模型提供了更好的准确性。开发的框架使研究人员能够快速筛选葡萄糖结合剂。