MSIGR-PLA: Integrating Multi-Scale Interaction and Global Representations for Protein-Ligand Affinity Prediction.

A Preparation-Free Mixture-of-Experts Framework for Protein-Ligand Affinity Prediction

The resulting model, HydrAffinity, is an interaction-free, dynamic sparse model that uses pre-trained encoders and MoE for parameter-efficient learning and outperforms all interaction-free methods and matches state-of-the-art interaction-based methods on CASF-2016.

M2-PRNet: Multi-Scale and Multi-Modal Learning for Protein-RNA Binding Affinity Prediction.

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.

A Scalable Structure-Aware Multimodal Architecture for Accurate Drug-Target Affinity Prediction.

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.

CoAff-DTI: Fine-grained drug-target interaction prediction using pre-trained language models and affinity-guided mechanisms

Accurate prediction of drug-target interactions (DTI) is essential for drug discovery. Despite the success of pre-trained language models (PLMs) in learning robust molecular and protein representations, a fundamental challenge remains in characterizing the fine-grained, localized biochemical interactions between drug substructures and protein binding sites. Such critical interaction patterns are often underrepresented in conventional global embedding approaches, thereby limiting both predictive accuracy and biological interpretability. To address this challenge, we propose CoAff-DTI, an end-to-end deep learning framework designed to enhance multi-scale interaction modeling for DTI prediction. The model introduces three key components. First, a token-level decomposition strategy is employed to transform global embeddings into pharmacophore- and residue-level representations, facilitating the capture of localized features. Second, an Affinity-Guided Cross-Attention (AGCA) module is designed to explicitly model fine-grained interactions between ligand substructures and protein residues. Third, an Affinity-Gating Fusion (AGF) module is proposed to enhance cross-modal feature integration by dynamically modeling element-wise interactions. Extensive experiments on multiple benchmark datasets demonstrate that CoAff-DTI consistently outperforms state-of-the-art methods. In addition, attention-based visualization results suggest improved interpretability, as the model's learned attention patterns align effectively with experimentally verified binding regions.

MultiGeo: Predicting Drug-Target Affinity via Adaptive Multi-Conformation Ensemble Learning

MSCA-PLA: multi-scale cross-attention with differentiable pooling for protein-ligand binding affinity prediction.