Aug 2026· Interdisciplinary Sciences Computational Life Sciences· 0 citations· 45 references
Medicine
TL;DR
3D-GEGCL is a framework that integrates 3D geometrically equivariant graph neural networks with self-supervised contrastive learning for robust and accurate PLBA prediction and achieves superior performance over existing leading methods across a range of assessment criteria.
An Algebraic Graph Neural Network model designed to encode molecular structures into a low-dimensional graph representation while preserving critical biochemical interactions is introduced, demonstrating superior performance in binding affinity prediction compared to state-of-the-art scoring functions.
Augustine Ouru, Xi Chen, Cameron Yeagle et al.· Computational and Mathematic...· 0 citations
LGBind is introduced, a structure-guided deep learning framework for generalized, ligand-conditioned binding site identification and demonstrates profound robustness and generalization in predicting novel ligand-target interactions, and demonstrates remarkable versatility.
Yun-Feng Li, Xing-Yu Liu, Yi-Jia Liu et al.· European journal of medicina...· 0 citations
Predicting ligand binding sites on protein surfaces requires capturing complex local geometries and satisfying physical constraints. Existing voxel-based methods suffer from high computational costs and rotation sensitivity, while standard point-cloud GNNs often lack geometric completeness—failing to distinguish chiral...
Kang-Xin Chen, Jie-Yu Zhao, Jin-Li Hu et al.· Proceedings of the Thirty-Fi...· 0 citations
Neural operators have revolutionized parametric partial differential equation solving by learning mappings between infinite-dimensional function spaces with unprecedented parameter space generalization. Despite recent advances in physics-informed neural networks and operator learning methods, the challenge of geometry...
It is argued that future drug‐oriented systems should combine joint 2D–3D graph generation, physically credible interaction modeling, synthesis and ADMET constraints, multi‐objective optimization, and closed‐loop experimental feedback.
Si-Liang Chen, Dai-Han Wang, Zhao-Ping Pan et al.· WIREs Computational Molecula...· 0 citations
Equivariant graph neural networks (EGNNs) have revolutionized molecular property prediction by encoding 3D structures while preserving physical symmetry. However, existing EGNNs struggle to capture fine-grained geometric features needed for tasks like understanding protein interactions, and designing molecules that mod...
Animesh Animesh· Proceedings of the Thirty-Fi...· 0 citations
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