A Minimal-Input Multilayer Perceptron for Predicting Drug-Drug Interactions Without Knowledge of Drug Structure
Bibliographic record
Abstract
Predictive models in the drug discovery industry have an essential role that cannot be understated. With the sheer volume of potentially useful compounds that are considered for use, it is becoming more difficult to investigate overlapping interactions between two drugs. Given that recreational drugs lack the rigorous warnings of prescription drugs, it is important for the layperson to know which drugs can and cannot mix. Other methods are necessary to bridge this knowledge gap in the absence of deterministic experimental results for every drug combination. Ideally, such methods would require minimal inputs, have high accuracy, and be computationally feasible. We have not encountered a model that meets all the above criteria. In light of this, we propose a minimal-input multi-layer perceptron that predicts the interactions between two drugs. This model has a great advantage of requiring no structural knowledge of the molecules in question, and instead only uses experimentally accessible chemical and physical properties; in particular, 20 per compound. These 20 were selected as they seemed they may be pertinent to the authors, although given this was an exploratory model, there was not a rigorous method behind the choice. Using a set of known drug-drug interactions and associated properties of the drugs involved, we trained our model on a dataset of about 650,000 entries. We report an accuracy of 0.968 on unseen samples of interactions between drugs on which the model was trained and an accuracy of 0.942 on unseen samples of interactions between unseen drugs. We believe this to be a promising and highly extensible model that has the potential for high generalized predictive accuracy with further tuning.
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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 | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.000 | 0.003 |
| 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".