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The necessity of predictive models in the drug discovery industry cannot beunderstated. With the sheer volume of potentially useful compounds that areconsidered for use, it is becoming increasingly computationally difficult toinvestigate the overlapping interactions between drugs. Understanding this isalso 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 samerigorous warnings as prescription drugs. Without access to deterministic,experimental results for every drug combination, other methods are necessary tobridge this knowledge gap. Ideally, such a method would require minimal inputs,have high accuracy, and be computationally feasible. We have not come across amodel that meets all these criteria. To this end, we propose a minimal-inputmulti-layer perceptron that predicts the interactions between two drugs. Thismodel has a great advantage of requiring no structural knowledge of themolecules in question, and instead only uses experimentally accessible chemicaland physical properties - 20 per compound in total. Using a set of knowndrug-drug interactions, and associated properties of the drugs involved, wetrained our model on a dataset of about 650,000 entries. We report an accuracyof 0.968 on unseen samples of interactions between drugs on which the model wastrained, and an accuracy of 0.942 on unseen samples of interactions betweenunseen drugs. We believe this to be a promising and highly extensible modelthat has potential for high generalized predictive accuracy with furthertuning.

A Minimal-Input Multilayer Perceptron for Predicting Drug-Drug Interactions Without Knowledge of Drug Structure