MambaTransDTA: A Hybrid Mamba-Transformer Architecture for Accurate Drug-Target Binding Affinity Prediction
Bibliographic record
Abstract
In recent years, deep learning techniques have made significant advances in drug-target affinity (DTA) prediction. However, existing models still have considerable room for improvement in prediction accuracy, robustness, and generalization ability. To address these challenges, we present a novel hybrid model, MambaTransDTA, which integrates the Mamba architecture with the Transformer architecture to optimize drug-target interaction prediction. By combining Mamba's ability to capture long-range dependencies with the Transformer's modeling of local interactions, MambaTransDTA estimates drug-target affinity more comprehensively and achieves notable performance improvements. On the four benchmark data sets, MambaTransDTA demonstrates exceptional prediction accuracy, achieving mean squared error (MSE) values of 0.191 (Davis), 0.173 (KIBA), 0.302 (Metz) and 0.715 (BindingDB). These results represent a significant improvement over the next best existing model, with relative error reductions of 4.3% (Davis), 3.3% (KIBA), 4.7% (Metz) and 10.5% (BindingDB). Ablation experiments and analysis of different hybridization strategies confirm that the proposed MambaTransDTA architecture significantly improves prediction performance by fully leveraging the strengths of both architectures. Therefore, MambaTransDTA is poised to become an essential tool for AI-driven drug discovery. Our data and code are available at https://github.com/pdssunny/MambaTransDTA.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.002 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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 teacher head, 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".