Entropic Fragment‐Based Approach to Aptamer Design
Bibliographic record
Abstract
Aptamers are short RNA/DNA sequences that are identified through the process of systematic evolution of ligands by exponential enrichment and that bind to diverse biomolecular targets. Aptamers have strong and specific binding through molecular recognition and are promising tools in studying molecular biology. They are recognized as having potential therapeutic and diagnostic clinical applications. The success of the systematic evolution of ligands by exponential enrichment process requires that the RNA/DNA pools used in the process have a sufficient level of sequence diversity and structural complexity. While the systematic evolution of ligands by exponential enrichment technology is well developed, it remains a challenge in the efficient identification of correct aptamers. In this article, we propose a novel information-driven approach to a theoretical design of aptamer templates based solely on the knowledge regarding the biomolecular target structures. We have investigated both theoretically and experimentally the applicability of the proposed approach by considering two specific targets: the serum protein thrombin and the cell membrane phospholipid phosphatidylserine. Both of these case studies support our method and indicate a promising advancement in theoretical aptamer design. In unfavorable cases where the designed sequences show weak binding affinity, these template sequences can be still modified to enhance their affinities without going through the systematic evolution of ligands by exponential enrichment process.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| 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".