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Julia Rahman

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#graph neural networks Open access Sep 2026

Enhancing protein–ligand pose prediction via dual-branch deep learning with docking-oriented multi-component loss and iterative refinement

KTransPose is presented, a GNN-based protein-ligand pose-refinement framework that combines a dual-branch architecture, a docking-oriented multi-component loss, and iterative refinement, and finds that a TransformerConv-based model provides the strongest overall performance.

Md. Khorshed Alam, Julia Rahman, M. A. Hakim Newton et al. · 0 citations

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