Drug Evolution Concept in Drug Design: 2. Chimera Method
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
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.
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 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 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 it