Application of Artificial Intelligence in Drug Discovery and Development: Targeted Design and Toxicological Property Prediction
Bibliographic record
Abstract
Traditional technologies for new drug research and development face numerous challenges, such as the lack of high-success-rate tools for virtual design of drug molecules based on protein three-dimensional structures (a limitation to research and development efficiency), the absence of reliable methods for generating new scaffold drug molecules, and a shortage of fast, reliable, low-cost models for drug toxicology prediction. AI can accurately predict protein structures to accelerate target design, efficiently generate new scaffold molecules adapted to targets, and greatly enhance the generalization ability of protein-ligand binding predictions. Some AI models have shown excellent performance in pharmacotoxicology prediction and treatment evaluation. These new technologies significantly shorten research and development cycles, reduce costs, improve prediction accuracy and efficiency, and drive the transformation of new drug research and development from experience-driven to data-driven approaches. This article reviews the application status and progress of AI tools in drug research and development, which focuses on two areas: AI-driven cancer drug target identification and optimization, and toxicology prediction and evaluation tools.
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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 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".