Drug Evolution Concept in Drug Design: 2. Chimera Method
Bibliographic record
Abstract
The drug evolution method represents a novel approach towards efficient rational drug design by implementing the drug evolution concept to the creation and development of general chemical libraries with the purpose of allowing the identification of drug candidates with improved odds and lesser costs than the traditional drug design strategies. As another example of successful translation of the biological evolution into chemical evolution, the chimera method comprises the grafting of selected building blocks, identified through a basic search within a drug library, onto the same substitution sites on a rationally chosen scaffold. The method allows the creation of a library containing both drugs and prospective drug candidates without any priorly required knowledge on the pursued disease or molecular target. Two libraries having scaffolds derived from para-aminobenzoic acid and salicylic acid have exemplified the application of the chimera method. The validation of the method has been achieved through the high number of recognized drugs within the library, which exhibit in the same time a wide variety of therapeutic activities and interact with a broad spectrum of molecular targets. The drug-enriched chimera libraries are expected to provide a highly efficient access to novel drug candidates whose unspecified therapeutic effects should be further revealed through high-throughput screening.
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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 | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| 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) | 0.003 | 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".