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
DSSMST is introduced, a Deterministic State Space Model for Spatial Transcriptomics, as a self-supervised learning (SSL) framework that integrates a Deterministic State Space Model (DSSM), graph neural networks (GNNs), and contrastive learning.
CABA-Bind improves the reliability and ligand-specific interpretability of RNA-ligand molecular recognition modeling, and provided computational evidence suggesting that model-highlighted RNA regions around G17 and A53/A54 may contribute to ligand-associated recognition.
Xiaoqin Wang, Xingyu Liu, Zhiwei Zhang et al.· Computational biology and ch...· 0 citations
E evaluation across diverse biological systems shows that SGFN achieves improved or competitive performance relative to representative baseline methods in reference-based benchmarks, and identifies biologically coherent spatial or functional regions in unlabeled datasets supported by marker-gene, spatial-autocorrelatio...
