AptaGPT: Advancing aptamer design with a generative pre-trained language model
Bibliographic record
Abstract
Aptamers, synthetic oligonucleotide ligands, have shown significant promise for therapeutic and diagnostic applications owing to their high specificity and affinity for target molecules. However, the conventional Systematic Evolution of Ligands by Exponential Enrichment (SELEX) for aptamer selection is time-consuming and often yields limited candidates. To address these limitations, we introduce AptaGPT, a novel computational strategy that leverages a Generative Pre-trained Transformer (GPT) model to design and optimize aptamers. By training on SELEX data from early rounds, AptaGPT generated a diverse array of aptamer sequences, which were then computationally screened for binding using molecular docking. The results of this study demonstrated that AptaGPT is an effective tool for generating potential high-affinity aptamer sequences, significantly accelerating the discovery process and expanding the potential for aptamer research. This study showcases the application of generative language models in bioengineering and provides a new avenue for rapid aptamer development.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| 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".