  The Absorption, Distribution, Metabolism, Elimination, and Toxicity (ADMET)
properties of drug candidates are estimated to account for up to 50% of all
clinical trial failures. Predicting ADMET properties has therefore been of
great interest to the cheminformatics and medicinal chemistry communities in
recent decades. Traditional cheminformatics approaches, whether the learner is
a random forest or a deep neural network, leverage fixed fingerprint feature
representations of molecules. In contrast, in this paper, we learn the features
most relevant to each chemical task at hand by representing each molecule
explicitly as a graph, where each node is an atom and each edge is a bond. By
applying graph convolutions to this explicit molecular representation, we
achieve, to our knowledge, unprecedented accuracy in prediction of ADMET
properties. By challenging our methodology with rigorous cross-validation
procedures and prospective analyses, we show that deep featurization better
enables molecular predictors to not only interpolate but also extrapolate to
new regions of chemical space.
