MOTIVATION: Drug-target binding affinity (DTA) prediction plays a vital role in drug repositioning. The emergence of large language models (LLMs) has introduced new perspectives for predicting DTA. Herein, we present TSEDTA, a Transformer-based neural network with SMILES Transformer and ESM2 embeddings for predicting DTA. It leverages pre-trained LLMs (SMILES Transformer and ESM2) to extract deep evolutionary representations from drug SMILES and protein sequences. The representations are directly fused with raw sequence embeddings and processed via dual Transformer encoders to capture complex local and global dependencies. RESULTS: The experiments demonstrate that TSEDTA outperforms ten advanced models on the Davis and KIBA datasets, and seven on the BindingDB dataset. Ablation studies show that incorporating LLM embeddings significantly improves the performance of TSEDTA. Furthermore, a practical case study demonstrates its real-world applicability. Ultimately, TSEDTA provides a highly accurate, robust tool for DTA prediction, offering new insights into the application of LLMs for DTA tasks. AVAILABILITY: The source code and data are available at: https://github.com/SunXu24Math/TSEDTA. The version of record is archived in Zenodo with the DOI: 10.5281/zenodo.19103249.

Normalized Cut (NCut) or Spectral Clustering (SC) discourages the isolated segmentation that may result from the standard minimum Cut by adding a volume constraint. Such a volume constraint introduces a significant computational challenge or an undesirable effect when the isolated segmentation is desired. In this paper, we propose theK-way constrained Normalized Cut (K-way CNCut). It is formulated as the minimum Cut witha priorichosen (either manually or automatically) constraints or representatives for the cluster. The role played by the constraints is to attract the strongly connected nodes to the relevant hosting constraints, thus it can both discourage or encourage the isolated segmentation, depending on the choice of constraints and nodes surrounding it. Most critically, in this paper, theK-way CNCut is discovered to have a link with the construction of the optimal prolongation operator in the algebraic multigrid method (AMG) [1], more precisely, the energy minimizing AMG [2], [3] in its most general setting, for the normalized Graph Laplacian. For the special case when a single constraint is given as a representative of a single cluster, it is shown to lead to the multiscale image segmentation [4], [5]. The importance of this link has been demonstrated as well. Among others, a set of constraints for theK-way CNCut was shown to be constructed via the multilevel coarsening algorithm, which exists in the algebraic multigrid method, thereby theK-way CNCut with manually chosen constraints, is made to be a fully automatic image segmentation algorithm. A number of numerical experiments are presented and compared with state-of-the-art classical and learning-based (both supervised and semi-supervised) image segmentation algorithms, which include SegNet [6] and SAM [7], [8] to demonstrate the effectiveness of the proposed framework.

The prediction of virus-drug associations (VDAs) is crucial for drug repositioning, contributing to the identification of latent antiviral drugs. In this study, we developed a graph-based integrated Gaussian kernel similarity (GiGs) method for predicting potential VDAs in drug repositioning. The GiGs model comprises three components: (i) collection of experimentally validated VDA information and calculation virus sequence, drug chemical structure, and drug side effect similarity; (ii) integration of viruses and drugs similarity based on the above information and Gaussian interaction profile kernel (GIPK); and (iii) utilization of similarity-constrained weight graph normalization matrix factorization to predict antiviral drugs. The GiGs model enhances correlation matrix quality through the integration of multiple biological data, improves performance via similarity constraints, and prevents overfitting and predicts missing data more accurately through graph regularization. Extensive experimental results indicated that the GiGs model outperforms five other advanced association prediction methods. A case study identified broad-spectrum drugs for treating highly pathogenic human coronavirus infections, with molecular docking experiments confirming the model's accuracy.