A Minimal-Input Multilayer Perceptron for Predicting Drug-Drug Interactions Without Knowledge of Drug Structure
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The necessity of predictive models in the drug discovery industry cannot be understated. With the sheer volume of potentially useful compounds that are considered for use, it is becoming increasingly computationally difficult to investigate the overlapping interactions between drugs. Understanding this is also important to the layperson who needs to know what they can and cannot mix, especially for those who use recreational drugs - which do not have the same rigorous warnings as prescription drugs. Without access to deterministic, experimental results for every drug combination, other methods are necessary to bridge this knowledge gap. Ideally, such a method would require minimal inputs, have high accuracy, and be computationally feasible. We have not come across a model that meets all these criteria. To this end, 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 - 20 per compound in total. 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 potential for high generalized predictive accuracy with further tuning.
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