Aug 2026· IEEE Transactions on Pattern Analysis and Machine Intelligence· Vol PP· 1 citation
Medicine
TL;DR
By combining protein language model embeddings with topology-adaptive geometric reasoning, DiConSite offers a reusable framework for residue-level protein interaction analysis and achieves consistently strong and often best-performing results, while improving robustness to structural uncertainty and cross-modal variation.
Abstract
Accurate identification of protein binding sites is essential for understanding biological mechanisms and advancing drug design. However, many structure-based predictors rely on spatial graphs whose topology remains fixed throughout message passing, making them sensitive to structural noise and difficult to transfer across ligand modalities. To address this issue, we propose DiConSite, a topology-adaptive and reusable architecture for residue-level binding site prediction across ligand-specific tasks. DiConSite is centered on a Latent Topological Evolution (LTE) module that augments the initial Euclidean graph with a latent functional topology. A Hierarchical Topological Distillation (HTD) objective and a Dynamic Curriculum Distillation (DCD) schedule are further introduced as LTE-dependent optimization stabilizers: they align relational structure across network depths only after the underlying topology has been refined. Extensive experiments across nine benchmarks show that DiConSite achieves consistently strong and often best-performing results, while improving robustness to structural uncertainty and cross-modal variation. By combining protein language model embeddings with topology-adaptive geometric reasoning, DiConSite offers a reusable framework for residue-level protein interaction analysis.
Abstract Motivation To enable real-world protein-ligand affinity prediction, not only out-of-distribution generalization but also robustness to variable structural availability and quality should be considered in model design. Results We present AlignNet, a hierarchical representation alignment framework that mitigates intra- and inter-molecular heterogeneity to learn robust protein-ligand embeddings for generalizable affinity prediction, even from sequence-level inputs. Its intra-molecular module projects unimodal and multimodal features into a unified space, aligning augmented multimodal views for feature fusion and unimodal with multimodal embeddings to distill multimodal priors for structure-agnostic inference. Its inter-molecular module aligns protein and ligand embeddings for cross-molecular integration. Extensive experiments show that AlignNet (i) achieves highly competitive performance, with up to a 20.4% gain in SCC on the challenging LBA 30% split under sequence-only settings, suggesting improved out-of-distribution generalization; and (ii) learns well-separated affinity-related clusters, supporting reliable structure-independent prediction. Availability and implementation AlignNet is available at https://github.com/altriavin/AlignNet.
Xiaowen Hu, Hongyi Huang, Hao Sun et al.· Bioinformatics· 0 citations
Accurate prediction of drug-target binding affinity (DTA) is a key task in virtual screening. However, current computational methods face a key challenge: sequence-based approaches often fail to capture critical spatial information, while structure-based models rely on computationally expensive 3D coordinates, which restrict their scalability. To address this issue, we propose StructuraDTA, a novel multimodal framework that adopts an implicit structure modeling strategy. Instead of using static protein folding data, our method encodes drug molecular graphs via Graph Isomorphism Networks (GINs) to capture fine-grained topological features. Meanwhile, we optimize protein representations by integrating probabilistic structural priors into a pretrained language model, which effectively simulates thermodynamic conformational flexibility without relying on explicit 3D structural data. A bidirectional cross-attention mechanism is then used to dynamically align these heterogeneous feature modalities. Comprehensive evaluations on the Davis and KIBA benchmark datasets show that StructuraDTA stably outperforms state of-the-art comparison methods. Importantly, the model exhibits strong robustness in cold-start scenarios, and can accurately predict binding affinities for previously unseen drugs and targets. By retaining the predictive performance of structure based models while maintaining the high inference efficiency of sequence-based methods, we provide an accurate and scalable solution to accelerate genome-scale drug discovery research.
Junlin Xu, Ye Yuan, Menglong Hu et al.· IEEE journal of biomedical a...· 0 citations
MOTIVATION
Predicting protein-RNA binding affinity is crucial for understanding cellular regulation and advancing RNA-targeted drug discovery. However, this task remains challenging due to structural complexity, limited labeled data, and insufficient modeling of fine-grained interactions.
RESULTS
We propose M2-PRNet, a multi-scale and multi-modal framework that integrates atom-level graphs, residue-level graphs, and tri-view molecular representations to capture complementary structural information. A cross-scale contrastive learning objective is introduced to align representations across different structural resolutions of the same complex. Under a clustering-based five-fold cross-validation setting on benchmark datasets, M2-PRNet achieves state-of-the-art performance. To further assess generalization under reduced sequence homology, we construct homology-aware RNA-cold, protein-cold, and dual-cold evaluations under a stricter 40% sequence identity threshold, where M2-PRNet maintains competitive performance. To account for conformational flexibility, we evaluate the model on MD150-1ns and an extended MD75-10ns subset, demonstrating stable performance under MD-derived structural perturbations. In addition, representative case studies suggest that M2-PRNet can highlight relevant RNA-binding regions and support preliminary discrimination between strong and weak binders when plausible complex structures are available. These results demonstrate the effectiveness of integrating multi-scale and multi-modal representations with cross-scale alignment for protein-RNA affinity prediction.
AVAILABILITY AND IMPLEMENTATION
The source code and datasets for M2-PRNet are freely available at https://github.com/CSUBioGroup/M2-PRNet.
Junkai Wang, G. Luo, Yunsong Yang et al.· Bioinformatics· 0 citations
Structure-based generative models (SBGMs) hold great promises for accelerating drug discovery by enabling target-aware molecular design. However, existing approaches face fundamental challenges: three-dimensional graph-based models can explicitly incorporate protein structural information but often generate chemically implausible molecules due to limited training data, while chemical language models (CLMs) produce chemically plausible molecules but struggle to effectively leverage three-dimensional structural information for structure-conditioned generation and hard to incorporate lead optimization functionality due to the nature of SMILES string. Here, we present StructureSAFE, a structure-aware chemical language model that resolves this trade-off by integrating protein structural and evolutionary encoders with the SAFE molecular representation via pretraining and finetuning training scheme, enabling both de novo hit identification and a comprehensive suite of lead optimization subtasks within a unified framework. Comprehensive benchmarking on the MolGenBench dataset demonstrates that StructureSAFE achieves state-of-the-art (SOTA) performance across multiple metrics, with particularly pronounced improvements in chemical plausibility relative to graph-based models lacking pretraining. Evaluation on a rigorously constructed held-out test set further confirms its ability to generate drug-like, synthetically accessible molecules with competitive predicted binding affinities for previously unseen targets on both hit identification and lead optimization setting. In silico case studies across four therapeutically relevant targets validate its capacity to generate chemically plausible molecules that recapitulate key binding interactions of known high-affinity ligands while proposing novel interactions for potential better affinity and exploring previously unknown regions of chemical space. Taking together, StructureSAFE represents a versatile and practical tool to provide high-quality candidate molecules for augmenting medicinal chemistry workflows in both hit identification and lead optimization campaigns.
Bo Yang, Ke Xu, Chijian Xiang et al.· bioRxiv· 0 citations
Motivation Accurate drug–target interaction (DTI) prediction remains difficult for underexplored drugs and targets, especially when available interaction evidence is sparse. Existing approaches often focus either on pairwise molecular representations or on heterogeneous biomedical graph topology, making it difficult to effectively integrate structure-derived representations with multi-relational contextual evidence. Result We propose ColdstartMHDTI, a two-stage framework for heterogeneous-graph-based DTI prediction that integrates sequence-derived structural representations with local and global relational information. Specifically, drug SMILES and target sequences are encoded by pretrained transformer models, while one-hop heterogeneous relations are captured through self-supervised DistMult embeddings. These representations are then fused through a meta-path-guided module that models ordered meta-path instances and aggregates them with cross-attention for interaction scoring. Across two benchmark datasets, ColdstartMHDTI shows consistent improvements under warm-start and entity-disjoint settings, with particularly strong performance for underexplored drugs and targets. It also remains robust under more imbalanced evaluation protocols with 1:5 and 1:10 positive-to-negative ratios. In addition to standard classification performance, ColdstartMHDTI supports candidate prioritization for downstream screening and evidence-guided hypothesis generation. Case studies on ESR1, EGFR, and Parkinson’s disease further demonstrate its practical utility, with the Parkinson’s disease analysis additionally highlighting strong per-drug target ranking performance.
Hongyang Yang, Xiucai Ye, Huipu Han et al.· Frontiers in Chemistry· 0 citations
MIRAGE provides an interpretable and robust framework for structure-aware prediction, with potential applications in protein engineering and drug design, and explicitly modeling multi-level interactions is important for accurately capturing the determinants of binding affinity.