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

T-pGNN4DTI: Towards better drug-target interactions prediction using Global Self-attentive Pooled Graph Convolutional Networks and protein pre-training Models

Identification of drug-target interactions (DTI) is an important and challenging task in drug discovery and development. Traditional methods generally require biological experiments, which are costly and time-consuming. Machine learning-based methods can rapidly predict DTI using only computer algorithmic models, allowing researchers to validate only the most promising interactions through biochemical experiments. This holds promise for effectively addressing the current challenges of lengthy development cycles and high costs in new drug development. However, it is difficult for the existing DTI prediction methods to learn complete and effective feature information from the compound and protein. Therefore, this work proposes a DTI prediction method based on the global self-attentive pooled graph neural network and protein pretraining model, called T-pGNN4DTI. On the one hand, T-pGNN4DTI uses a global self-attention pooled graph neural network to learn more meaningful features of the drug molecule by paying more attention to the information features of certain important atomic nodes of the molecular structure and ignoring some weakly relevant node information features. On the other hand, T-pGNN4DTI uses a pre-trained Transformer-based model to capture the semantic relationships of contexts in long sequences of proteins, which can learn more complete feature information. The results of comparing experiments on three benchmark datasets show that the performance of the proposed T-pGNN4DTI model is better than that of the existing DTI prediction methods, effectively improving the DTI prediction. It provides a new way of thinking to help solve the DTI-related problems.

DeepGCL: Multi-View Graph Contrastive Learning for Enhanced Drug-Target Binding Affinity Prediction Through Protein Pocket-Drug Interaction Modeling.

Drug-target binding affinity (DTA) prediction plays a critical role in drug discovery. Although recent approaches have demonstrated the importance of incorporating protein pocket information, they still struggle to jointly model local atomic-level interactions and global sequence contexts, as well as capture their intricate cross-modal dependencies, within a unified framework for accurate prediction. To address this limitation, we present DeepGCL, a novel multi-modal framework that leverages multi-view graph contrastive learning to capture latent representations of pocket-drug interactions and their underlying molecular determinants. The DeepGCL integrates protein sequences, pocket-drug interaction graphs, and drug molecular graphs within a unified framework to learn complementary molecular representations. By combining sequence encoding with multi-view graph contrastive learning through augmented view generation, the DeepGCL effectively captures contextual protein information and structural interactions between pockets and drugs. This enables accurate characterization of spatial and chemical relationships between binding partners. Comprehensive evaluation on multiple benchmarks shows that DeepGCL consistently outperforms state-of-the-art methods across standard metrics. Ablation studies further confirm that the protein feature module and graph contrastive learning module both make significant and complementary contributions to performance. These findings underscore the effectiveness of multi-view learning paradigms in capturing the multifaceted nature of drug-target interactions and highlight the importance of integrating diverse molecular representations for accurate binding affinity prediction. Our code is available at https://github.com/domtryto/DeepGCL.

GoMA-DTA: A Gene Ontology-Guided Multimodal Attention Fusion Model for Drug-Target Affinity Prediction.

Accurate prediction of drug-target affinity (DTA) is essential for accelerating drug discovery. Although pretrained protein language models have achieved significant progress, existing methods predominantly focus on bottom-up sequence patterns and lack explicit constraints from high-level biological functions. We propose GoMA-DTA, a framework integrating gene ontology (GO) functional annotations with protein semantic features. GoMA-DTA introduces a channelwise gating mechanism that uses functional semantics as anchors to dynamically recalibrate ESM-2embeddings, achieving adaptive semantic filtering. For drugs, the model integrates Molformer-based semantic and TransConv-derived structural features. These dual-modality drug representations interact with calibrated protein features through a parallel synergistic architecture of cross-attention and Mamba modules, ensuring precise cross-modal alignment and efficient long-range dependency modeling. Evaluations on PDBBind, BindingDB, and ChEMBL benchmarks demonstrate that GoMA-DTA significantly outperforms state-of-the-art models across various evaluation scenarios. Its superior screening power is further validated on CASF-2016. Moreover, virtual screening of 200 000compounds against the SARS-CoV-2Spike protein, supported by experimental evidence (ZINC2111387), underscores its practical utility as a robust and biologically reliable tool. The datasets and codes are publicly available at https://github.com/xa-123955/GoMA-DTA.

CrossSG-DTA: Synergizing Sequence Semantics and Graph Structures via Cross-Attention for Drug-Target Affinity Prediction.

Accurate prediction of drug-target affinities (DTA) is critical for drug discovery. However, this task remains a significant challenge due to the complexity of modeling interactions between small ligands and large targets. In this study, we propose a multi-modal deep learning framework (CrossSG-DTA) to predict drug-target affinity by integrating sequence semantics with graph structural information. We leverage ChemBERTa and ESM-2 to extract rich semantic features for drugs and targets, respectively. In addition, a modified Graph Convolutional Network (GCN) is utilized to simultaneously capture structural data. To effectively fuse these heterogeneous features, we design a new symmetric dual cross-attention fusion mechanism for drugs and targets. This mechanism enables the model to capture complex dependencies between global sequence representations and local topological structures. Subsequently, the fused drug and target features are concatenated and fed into a three-layer Multi-Layer Perceptron (MLP) to obtain the final binding affinity. Experimental results on the Davis and KIBA datasets demonstrate that CrossSG-DTA significantly outperforms state-of-the-art methods. Finally, a case study on a glaucoma-related target highlights the practical utility of our model as a powerful in silico tool for DTA tasks.

LOGIC: LLM-Driven Cross-Scale Feature Coupling for Drug-Disease Interaction Prediction.

Predicting drug-disease interactions (DSI) is a pivotal task in computational drug discovery, aiming to identify potential therapeutic or adverse effects between drugs and diseases. Current methodologies primarily model on two levels of features: the macroscopic level, utilizing network topology, and the mesoscopic level, leveraging molecular-level features. While valuable, these approaches share a common shortcoming: they frequently fail to capture fine-grained, mechanistic interactions. This refers to the specific interplay between drug functional groups and disease symptoms that underpins pharmacological effects. To address this limitation, we propose LOGIC, a novel model for LLM-driven cross-scale feature coupling for DSI prediction. LOGIC comprehensively models drug and disease representations across micro-, meso-, and macro-scales. The key innovation of LOGIC lies in constructing a dictionary of functional groups and symptoms, and performing a simple and intuitive multi-hot encoding of drugs and diseases at the micro-scale, and in employing large language models (LLMs) to derive the meso-scale features of diseases without requiring additional domain knowledge. LOGIC mainly consists of four modules: (1) Drug-disease micro-scale feature learning; (2) Drug-disease meso-scale feature learning; (3) Drug-disease macro-scale feature learning; and (4) cross-scale feature coupling prediction, which integrates micro-, meso- and macro-scale features for both drugs and diseases, and employs the matrix multiplication operation to model fine-grained feature interactions in the dimension level for DSI prediction. Extensive experiments conducted on multiple datasets validate the effectiveness and scalability of LOGIC.

DQHTFI: Dynamic-Query Hypergraph Transformer for Fine-Grained Drug–Target Interaction and Affinity Prediction