Predicting Drug-Drug Interactions Using Deep Neural Network
Bibliographic record
Abstract
Drug-drug interactions (DDIs) can trigger unexpected pharmacological effects, including adverse drug events (ADEs), with causal mechanisms often unknown. Recently, deep neural network (DNN) models have achieved great success in many applications, including predicting pharmacological properties of drugs and drug repurposing. In this study, we generated features produced by SMILES (simplified molecular-input line-entry system) codes for more than 5,000 drugs downloaded from DrugBank. We built a deep neural network model to predict 80 DDI types using the features. We reached an overall accuracy and AUC (area under the curve) of receiver operating characteristic of 93.2% and 94.2% of the test data set and 94.9% and 95.6% of the validation data set, respectively. The trained model was applied to predict the DDI types of 13,155,885 drug-drug pairs combined by 5,130 drugs. The prediction results were applied to analyze the drugs currently used for treating inflammatory bowel disease (IBD). The potential drug combinations for treating IBD were discussed. These results can provide important insights on drug repurposing and guidelines during drug development.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| 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".