Predicting drug-target affinity (DTA) is essential for screening candidate compounds in early-stage drug discovery. However, most existing computational methods formulate DTA prediction as a regression task, leading to an objective mismatch with the practical need to rank and prioritize candidate compounds based on t...
Qian Huang, Yi-Fan Wu, Lin Wang et al.· Journal of Chemical Informat...· 0 citations
MolDBG achieves competitive performance across all three tasks while enabling site‐specific affinity prediction and interpretable binding‐site discovery, and generalizes to structurally elusive targets, including cryptic pockets and intrinsically disordered proteins.
Gang Luo, Qian-Qian Zhang, Chen-Hao Wang et al.· Advancement of science· 0 citations
BioHSNet, a biological function-guided hypergraph siamese network for inferring drug-target interactions from perturbation transcriptomics, utilizes hyperedge representations of functionally grouped gene expression to capture higher-order functional relationships, and integrates compound structural information into the...
Xinyi Zhang, Xinliang Sun, Jiu-Xu Yang et al.· Bioinform.· 0 citations
MARD-Mol is proposed, a hybrid AR-diffusion framework based on motif-inspired units that reformulate property optimization into an iterative “diagnose-and-repair” process, enabling targeted optimization of defective motifs while preserving the global scaffold.
Sizhe Zhang, G. Luo, Wei Fan et al.· Bioinform.· 0 citations
A multi-modal deep learning framework to predict drug-target affinity by integrating sequence semantics with graph structural information and design a new symmetric dual cross-attention fusion mechanism for drugs and targets.
Wei Lan, Tian Huang, Guohang He et al.· IEEE journal of biomedical a...· 0 citations
The results demonstrate the effectiveness of integrating multi-scale and multi-modal representations with cross-scale alignment for protein–RNA affinity prediction, and suggest that M2-PRNet can highlight relevant RNA-binding regions and support preliminary discrimination between strong and weak binders when plausible...
Junkai Wang, G. Luo, Yun-Song Yang et al.· Bioinformatics· 0 citations
This work designs a biological prior-guided feature fusion framework that integrates pseudo-structural epitope knowledge and CDR-specific attention mechanisms via a mixture-of-experts architecture to effectively capture complex binding landscapes in antibody screening and drug residence time analysis.
G. Luo, Junkai Wang, Sizhe Zhang et al.· Bioinformatics· 0 citations
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