Improving the Accuracy of Distance-Based Protein-Ligand Binding Affinity Prediction Using Linear Regression and Artificial Neural Network

Yang, Y. X.; Zhu, B. T.

doi:10.1101/2025.10.07.680851

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bioRxiv · 10.1101/2025.10.07.680851

Improving the Accuracy of Distance-Based Protein-Ligand Binding Affinity Prediction Using Linear Regression and Artificial Neural Network

Yang, Y. X.·Zhu, B. T.

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Abstract

In the traditional scoring functions for protein-ligand binding affinity prediction, the energies of the electrostatic and van der Waals interactions were evaluated (or restricted) by the mathematical expressions of [Formula] and [Formula], respectively. In comparison, the power exponents of distance-based variables as adopted in the present study are not restricted as those in traditional energy terms for atomic interactions. The distance-based variables were integrated using linear regression and artificial neural network to predict the protein-ligand binding affinity or binding energy. The training of the linear, neural network and mixed models was based on the newest data in PDBbind, i.e., PDBbind (v.2024). Estimated according to Pearsons correlation coefficient (R), the best performances of the linear models are 0.700 < R [≤] 0.800 with the high-quality affinity data, and those of the neural network-based mixed models are 0.800 [≤] R < 0.900 with the same data. The predictive powers of the best models developed in this study are superior to the sophisticated linear and machine learning-based scoring functions developed before. The results suggest that the distance-based variables with appropriate power exponents may have the ability to improve the prediction of protein-ligand binding affinity with high accuracy. GRAPHICAL ABSTRACT O_FIG O_LINKSMALLFIG WIDTH=143 HEIGHT=200 SRC="FIGDIR/small/680851v1_ufig1.gif" ALT="Figure 1"> View larger version (50K): org.highwire.dtl.DTLVardef@10e86e4org.highwire.dtl.DTLVardef@b9ec82org.highwire.dtl.DTLVardef@565a90org.highwire.dtl.DTLVardef@153c4ef_HPS_FORMAT_FIGEXP M_FIG C_FIG HIGHLIGHTSO_LIBy using the newest data in PDBbind (v.2024) to train the linear, neural network and mixed models, the quantitative distance-energy relationships are further explored and improved to predict the binding affinity of protein-ligand complexes. C_LIO_LIThe power exponents of distance in the traditional energy terms are expanded to characterize the distance-energy relationships accurately at atom level for protein-ligand interactions. C_LIO_LIThe best models are superior to the sophisticated machine learning-based scoring functions developed before. C_LI

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Yang, Y. X., Zhu, B. T.. 2025-10-08. Improving the Accuracy of Distance-Based Protein-Ligand Binding Affinity Prediction Using Linear Regression and Artificial Neural Network. https://doi.org/10.1101/2025.10.07.680851

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