Drug Evolution Concept in Drug Design: 1. Hybridization Method
Bibliographic record
Abstract
A novel concept, "drug evolution", is proposed to develop chemical libraries that have a high probability of finding drugs or drug candidates. It converts biological evolution into chemical evolution. In this paper, we present "hybridization" drug evolution, which is the equivalent of sexual recombination of parental genomes in biological evolution. The hybridization essentially shuffles the building blocks of the parent drugs and ought to drug(s); no drug evolution can otherwise occur. We hybridized two drugs, benzocaine and metoclopramide and generated 16 molecules that include the parent drugs, four known drugs, and two molecules whose therapeutic activities are reported. The unusually high number of drugs and drug candidates in the library encourages high expectations of finding new drug(s) or drug candidate(s) within the remaining eight compounds. Interestingly, the therapeutic applications of the eight drugs or drug candidates in the library are fairly diverse as 38 therapeutic applications and 25 molecular targets are counted. Therefore, the library fits as a general chemical library for unspecified therapeutic activities. The hybridization of other two drugs, aspirin and cresotamide, is also described to demonstrate the generality of the method.
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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 | 0.001 |
| 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 | 0.002 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".