{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,11,1]],"date-time":"2026-11-01T04:11:06Z","timestamp":1793506266304,"version":"4.5.0"},"reference-count":62,"publisher":"MDPI AG","issue":"2","license":[{"start":{"date-parts":[[2026,1,22]],"date-time":"2026-01-22T00:00:00Z","timestamp":1769040000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["IJMS"],"abstract":"<jats:p>Accurately identifying drug off-targets is essential for reducing toxicity and improving the success rate of pharmaceutical discovery pipelines. However, current deep learning approaches often struggle to fuse chemical structure, protein biology, and multi-target context. Here, we introduce HDPC-LGT (Hybrid Dual-Prompt Cross-Attention Ligand\u2013Protein Graph Transformer), a framework designed to predict ligand binding across sixteen human translation-related proteins clinically associated with antibiotic toxicity. HDPC-LGT combines graph-based chemical reasoning with protein language model embeddings and structural priors to capture biologically meaningful ligand\u2013protein interactions. The model was trained on 216,482 experimentally validated ligand\u2013protein pairs from the Chemical Database of Bioactive Molecules (ChEMBL) and the Protein\u2013Ligand Binding Database (BindingDB) and evaluated using scaffold-level, protein-level, and combined holdout strategies. HDPC-LGT achieves a macro receiver operating characteristic\u2013area under the curve (macro ROC\u2013AUC) of 0.996 and a micro F1-score (micro F1) of 0.989, outperforming Deep Drug\u2013Target Affinity Model (DeepDTA), Graph-based Drug\u2013Target Affinity Model (GraphDTA), Molecule\u2013Protein Interaction Transformer (MolTrans), Cross-Attention Transformer for Drug\u2013Target Interaction (CAT\u2013DTI), and Heterogeneous Graph Transformer for Drug\u2013Target Affinity (HGT\u2013DTA) by 3\u20137%. External validation using the Papyrus universal bioactivity resource (Papyrus), the Protein Data Bank binding subset (PDBbind), and the benchmark Yamanishi dataset confirms strong generalisation to unseen chemotypes and proteins. HDPC-LGT also provides biologically interpretable outputs: cross-attention maps, Integrated Gradients (IG), and Gradient-weighted Class Activation Mapping (Grad-CAM) highlight catalytic residues in aminoacyl-tRNA synthetases (aaRSs), ribosomal tunnel regions, and pharmacophoric interaction patterns, aligning with known biochemical mechanisms. By integrating multimodal biochemical information with deep learning, HDPC-LGT offers a practical tool for off-target toxicity prediction, structure-based lead optimisation, and polypharmacology research, with potential applications in antibiotic development, safety profiling, and rational compound redesign.<\/jats:p>","DOI":"10.3390\/ijms27021126","type":"journal-article","created":{"date-parts":[[2026,1,23]],"date-time":"2026-01-23T09:16:59Z","timestamp":1769159819000},"page":"1126","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":4,"title":["Hybrid Dual-Context Prompted Cross-Attention Framework with Language Model Guidance for Multi-Label Prediction of Human Off-Target Ligand\u2013Protein Interactions"],"prefix":"10.3390","volume":"27","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-7983-2189","authenticated-orcid":false,"family":"Abdullah","sequence":"first","affiliation":[{"name":"Center for Computing Research, Instituto Polit\u00e9cnico Nacional, Mexico City 07320, Mexico"},{"name":"Department of Computer Sciences, Bahria University, Lahore 54600, Pakistan"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0009-0001-6154-1893","authenticated-orcid":false,"given":"Zulaikha","family":"Fatima","sequence":"additional","affiliation":[{"name":"Faculty of Allied Health Sciences, Superior University, Lahore 54000, Pakistan"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0009-0004-5397-6768","authenticated-orcid":false,"given":"Muhammad Ateeb","family":"Ather","sequence":"additional","affiliation":[{"name":"Center for Computing Research, Instituto Polit\u00e9cnico Nacional, Mexico City 07320, Mexico"},{"name":"Department of Computer Sciences, Bahria University, Lahore 54600, Pakistan"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-7468-9603","authenticated-orcid":false,"given":"Liliana","family":"Chanona-Hernandez","sequence":"additional","affiliation":[{"name":"Zacatenco Unit, Higher School of Mechanical and Electrical Engineering, Instituto Polit\u00e9cnico Nacional, Mexico City 07700, Mexico"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-8308-8882","authenticated-orcid":false,"given":"Jos\u00e9 Luis Oropeza","family":"Rodr\u00edguez","sequence":"additional","affiliation":[{"name":"Center for Computing Research, Instituto Polit\u00e9cnico Nacional, Mexico City 07320, Mexico"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2026,1,22]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"11141","DOI":"10.1039\/D5CS00415B","article-title":"Modeling protein\u2013ligand interactions for drug discovery in the era of deep learning","volume":"54","author":"Wang","year":"2025","journal-title":"Chem. 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