TUNA: A Target-aware Unified Network for Protein-Ligand Binding Affinity Prediction via Multi-Modal Feature Integration
Bibliographic record
Abstract
The accurate prediction of protein-ligand binding affinity is crucial for early-stage drug discovery. Sequence-based deep learning models offer scalability and broader applicability than structure-based methods, but typically ignore the local binding site context, limiting predictive power. Recent advances in protein structure prediction and binding pocket detection have enabled the integration of pocket-level information into sequence-based models. We present TUNA, a novel deep learning model that integrates multi-modal features to predict binding affinity. The TUNA integrates global protein sequences, localized pocket representations, ligand features derived from the SMILES, and molecular graph structures. We used three-dimensional structure inference and pocket detection tools for proteins lacking experimentally determined binding sites. The pocket and protein sequences were encoded using embeddings from pre-trained models, including a model pretrained specifically on pocket-derived sequences. Ligands were represented through a fusion of Chemformer-encoded SMILES and graph diffusion-based features, then unified via an alignment strategy to preserve both symbolic and structural information. TUNA achieved consistent improvements over sequence-based models across the PDBbind and BindingDB datasets while remaining competitive with structure-based methods for the PDBbind dataset. Its interpretable cross-modal attention mechanism enables the inference of potential binding sites, thus enhancing biological interpretation. These results demonstrate that TUNA is an effective affinity prediction method, especially for targets without known structures.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".