Aug 2026· Molecular diversity· 0 citations· 21 references
Medicine
TL;DR
DSC-BSite is proposed, a dynamic-static collaborative multimodal graph learning framework for residue-level binding site prediction that achieves competitive performance across multiple evaluation metrics, with particularly strong results in Recall on UniProtSMB and Precision and MCC on SJC.
RGLLA-PPIS, a novel multimodal prediction model that integrates retrieval-augmented learning and residual GNNs for PPIS identification, outperforms several state-of-the-art baselines in both accuracy and robustness and demonstrates its potential to guide real-world protein engineering tasks.
Jia Mi, Ya-Wen Liu, Chong Chu et al.· IEEE Transactions on Neural...· 0 citations
This work presents HGRL-PPIS, a novel hierarchical graph representation learning approach for predicting protein-protein interaction sites that achieves superior performance over competing methods on multiple benchmark datasets, enabling more reliable detection of protein-protein binding residues.
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
Protein–Protein Interactions (PPIs) are fundamental to cellular processes and represent major targets for therapeutic development. Recent advances in machine learning for protein modeling, particularly protein language models, have enabled powerful representations of protein sequences and monomeric structures. However,...
Zhi-Yuan Song, Zi-Yu Shi, Gary Sun et al.· bioRxiv· 0 citations
The computational results indicate that the proposed Persistent Sheaf Laplacian learning (PSLL) model achieves strong predictive performance across benchmark datasets, highlighting its potential as an interpretable and mathematically grounded framework with promising generalizability for molecular machine learning and...
Mushal Zia, Benjamin Jones, Guo-Wei Wei· 0 citations
DQHTFI is proposed, a fine-grained interaction prediction framework for drug–target interaction classification and binding affinity regression that employs BRICS fragments and Pfam functional domains as the basic interaction units and jointly learns semantic and structural representations.
Ke Tao, Haoze Chai, Zhuo Chen et al.· bioRxiv· 0 citations
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