A novel artificial intelligence protocol for finding potential inhibitors of acute myeloid leukemia
Bibliographic record
Abstract
There is currently no effective treatment for acute myeloid leukemia, and surgery is also ineffective as an important treatment for most tumors. Rapidly developing artificial intelligence technology can be applied to different aspects of drug development, and it plays a key role in drug discovery. Based on network pharmacology and virtual screening, candidates were selected from the molecular database. Nine artificial intelligence algorithm models were used to further verify the candidates' potential. The 350 training results of the deep learning model showed higher credibility, and the R-square of the training set and test set of the optimal model reached 0.89 and 0.84, respectively. The random forest model has an R-square of 0.91 and a mean square error of only 0.003. The R-square of the Adaptive Boosting model and the Bagging model reached 0.92 and 0.88, respectively. Molecular dynamics simulation evaluated the stability of the ligand-protein complex and achieved good results. Artificial intelligence models had unearthed the promising candidates for STAT3 inhibitors, and the good performance of most models showed that they still had practical value on small data sets.
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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 | 0.001 | 0.001 |
| 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".