AI Based Drug Screening Process: From Data Mining to Candidate Drug Validation
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
With the rapid development of artificial intelligence (AI) technology, its application in drug research and development is becoming increasingly widespread. This study introduces the advantages of AI technology in drug screening, such as fast processing and analysis of large amounts of data, improving screening accuracy, and reducing research and development costs. Discussed the shortcomings in the current AI drug screening process, such as data dependence, insufficient model interpretability, and legal and ethical issues. Intended to explore the AI based drug screening process, from data mining to candidate drug validation. I hope to provide a comprehensive and systematic perspective for researchers and practitioners in the field of drug development by deeply understanding the advantages, disadvantages, and challenges faced by AI technology in drug screening, and proposing corresponding solutions, in order to guide them to better utilize AI technology to accelerate the drug development 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.
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 | 0.008 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.004 | 0.001 |
| 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