  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.
