The glucose is an important source of fuel for the body. The binding affinity is an essential indicator of the interaction of a glucose molecule with its binder. This paper proposes a novel machine learning model for predicting the binding affinity of a small glucose molecule with the binder. Seven regression algorithms were compared on a dataset is generated based on Molecular Mechanics-Generalized Born and Surface Area (MM-GBSA). Through the comparison, Random Forest and Decision Tree were selected for our model, in light of their robustness and accuracy. The established model predicts binding affinity from the interaction properties of compounds and glucose, which are obtained through GLIDE program from Schrödinger software suite 2018-4. Finally, the prediction accuracy of our model was confirmed through k-fold cross-validation. Our research provides an efficient and low-cost method for screening of molecules during the development of glucose binders.
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.
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