3D-ReSE-VN: Advanced Deep Learning for Protein Ligand Affinity Prediction in Cancer-Associated Targets
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Abstract
Accurate protein-ligand binding affinity prediction is important for structure-based anticancer drug discovery. This study presents 3D-ReSE-VN, a three-dimensional residual attention network that combines voxelized docking-derived complexes with residual learning, squeeze-and-excitation channel attention, and spatial attention. The model was evaluated on 4,620 protein-ligand complexes using random five-fold cross-validation and a predefined train/validation/test split. In cross-validation, 3D-ReSE-VN achieved an RMSE of 1.9679 ± 0.8242, an MAE of 0.6952 ± 0.1079, a Pearson correlation of 0.8169 ± 0.1095, and an R2 of 0.6724 ± 0.1755. On the fixed hold-out test set of 693 complexes, the model achieved an RMSE of 1.3251, an MAE of 0.6420, a Pearson correlation of 0.9098, and an R2 of 0.8271. The model showed competitive performance against the reported architectures, although performance varied across data partitions and affinity ranges. These findings support further evaluation on larger, chemically diverse, and disjoint protein-ligand datasets.
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© 2026 The authors. This is an open access article distributed under the terms of the Creative Commons Attribution-NonCommercial 4.0 International License.
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