Challenges in Computational Drug-Drug Interaction Prediction: A Survey
Bibliographic record
Abstract
Polypharmacy, exacerbated by aging populations and global health crises, underscores the need for accurate drug-drug interaction (DDI) prediction.This review article offers a thorough analysis of the latest advancements in machine learning (ML) models for predicting DDIs.The review zeroes in on the progress made since 2020, a notable period characterized by a significant increase in both the volume of drug-related datasets and the sophistication of deep learning (DL) techniques.We meticulously examine various dataset sources pivotal in the development of these models and delve into the methodologies employed for featurizing molecular structures and biological data.The article further explores a range of DL models and graph neural networks, assessing their efficacy in the accurate prediction of DDIs.Through a comparative analysis, we elucidate the strengths, limitations, and potential challenges faced by these models.Crucially, the review underscores the necessity of incorporating comprehensive clinical and biochemical factors to augment the real-world applicability and accuracy of DDI predictions.This comprehensive overview not only sheds light on the current state of DDI predictive modeling but also paves the way for future research directions, emphasizing the need for more advanced, adaptable models in the dynamic landscape of polypharmacy and drug interactions.
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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 | 0.013 | 0.034 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.002 |
| Bibliometrics | 0.003 | 0.006 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.005 | 0.007 |
| Open science | 0.006 | 0.004 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.004 | 0.002 |
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".