Drug discovery, a cornerstone of modern biomedical research, is critical for developing targeted therapies against complex diseases. However, many existing computational approaches for DTA prediction suffer from limited accuracy and poor generalization across diverse target families and chemical spaces. To address this, we propose CoLA-DTA, a cross-modal alignment framework designed for generalizationenhanced drug-target affinity prediction. CoLA-DTA integrates multiple modalities of protein and drug representations, including sequence embeddings, 3D structural graphs, and molecular graphs. Specifically, pretrained language models and graph neural networks extract semantic, spatial, and topological features from protein sequences, drug SMILES, and their respective graph representations. A Gated Fusion module effectively combines multi-source features, followed by a bidirectional cross-attention mechanism that captures finegrained intermolecular interactions. Experimental results on benchmark datasets demonstrate that CoLA-DTA outperforms state-of-the-art models in both accuracy and generalization, particularly under cold-start settings. A case study on protooncogene tyrosine-protein kinase ROS(PTKR) further demonstrated CoLA-DTA's potential for drug discovery. CoLADTA's source code, pre-trained models, and data preprocessing code are available at https://github.com/NENUBioCompute/CoLA-DTA.git.

MOTIVATION: Most drugs start on their journey inside the body by binding the right target proteins. This is the reason that numerous efforts have been devoted to predicting the drug-target binding during drug development. However, the inherent diversity among molecular properties, coupled with limited training data availability, poses challenges to the accuracy and generalizability of these methods beyond their training domain. RESULTS: In this work, we proposed a neural networks construction for high accurate and generalizable drug-target binding prediction, named Pre-trained Multi-view Molecular Representations (PMMR). The method uses pre-trained models to transfer representations of target proteins and drugs to the domain of drug-target binding prediction, mitigating the issue of poor generalizability stemming from limited data. Then, two typical representations of drug molecules, Graphs and SMILES strings, are learned respectively by a Graph Neural Network and a Transformer to achieve complementarity between local and global features. PMMR was evaluated on drug-target affinity and interaction benchmark datasets, and it derived preponderant performance contrast to peer methods, especially generalizability in cold-start scenarios. Furthermore, our state-of-the-art method was indicated to have the potential for drug discovery by a case study of cyclin-dependent kinase 2. AVAILABILITY AND IMPLEMENTATION: https://github.com/NENUBioCompute/PMMR.

Predicting protein-ligand binding affinity is important in areas such as drug discovery, gene regulation and signal transduction. The DTA(Drug-Target Affinity) method based on protein structure can not only effectively compensates for the lack of binding information, but also more in line with real biological processes. Although the structure-based DTA methods have achieved good performance, the existing methods still have the problem of only considering single-scale structural features and ignoring multi-scale structural features. In order to solve this problem, we propose the MSSDTA (Multi-Scale Structural Representation Drug-Target Affinity Prediction), which extracts multi-scale protein features by integrating the surface node features and structural node features of proteins. At the same time, the drug representation network is used to fuse the 2D molecular structure characteristics and chemical characteristics of the drug to effectively distinguish the drug molecules with similar planar structures. Finally, the affinity prediction network is used to generate protein-ligand binding affinity scores. We verify the performance of this model on the PDBbind v.2019 dataset. The experimental results show that the proposed method achieves excellent performance.