Toward clinical translation of AI-Led drug discovery in endometrial cancer
Bibliographic record
Abstract
INTRODUCTION: Deep learning (DL) is transforming cancer research by enabling data-driven drug discovery. However, its clinical translation, particularly in endometrial cancer (EC), faces significant challenges. AREAS COVERED: This review discusses recent DL applications across drug discovery stages in EC, including target identification, virtual screening, and de novo drug design. We highlight key obstacles that hinder clinical translation, such as data scarcity, limited model explainability, biological validation gaps, and regulatory uncertainty, and propose practical solutions. Literature was sourced from PubMed, Web of Science, and relevant AI repositories, with an emphasis on peer-reviewed studies from the past five years. EXPERT OPINION: Despite early success, DL must overcome multiple translational bottlenecks to impact EC therapeutics meaningfully. A multidisciplinary approach that incorporates data quality improvements, functional validation, regulatory engagement, and clinician-focused decision support is essential to fully realize the clinical promise of DL-driven drug discovery in EC.
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 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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.004 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".