Artificial Intelligence and Drug Design: Future Prospects and Ethical Considerations
Bibliographic record
Abstract
The rapid advancement of science and technology, artificial intelligence (AI) has penetrated into many fields and shown its great potential. In the field of drug design, the application of AI is gradually changing the traditional research and development model. This study first introduces the applicability of AI technology in drug design and its application examples at each stage, and analyzes its important role in improving R&D efficiency and success rate. Subsequently, the article looks forward to the future prospects of AI and drug design, including technological innovation, development trends, challenges and opportunities, and proposes corresponding development strategies. However, the widespread application of AI has also triggered many ethical considerations, such as data privacy, algorithm transparency, and definition of ethical responsibilities, which need to be treated with caution while promoting technological development. Finally, this study highlights how the relationship between innovation and ethics should be balanced in future research and makes corresponding recommendations.
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.
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 | 0.054 | 0.047 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.004 | 0.019 |
| Scholarly communication | 0.011 | 0.013 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.015 | 0.013 |
| Insufficient payload (model declined to judge) | 0.005 | 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".