Deep Architectures and Deep Learning in Chemoinformatics: The Prediction of Aqueous Solubility for Drug-Like Molecules

Shallow
machine learning methods have been applied to chemoinformatics
problems with some success. As more data becomes available and more
complex problems are tackled, deep machine learning methods may also
become useful. Here, we present a brief overview of deep learning
methods and show in particular how recursive neural network approaches
can be applied to the problem of predicting molecular properties.
However, molecules are typically described by undirected cyclic graphs,
while recursive approaches typically use directed acyclic graphs.
Thus, we develop methods to address this discrepancy, essentially
by considering an ensemble of recursive neural networks associated
with all possible vertex-centered acyclic orientations of the molecular
graph. One advantage of this approach is that it relies only minimally
on the identification of suitable molecular descriptors because suitable
representations are learned automatically from the data. Several variants
of this approach are applied to the problem of predicting aqueous
solubility and tested on four benchmark data sets. Experimental results
show that the performance of the deep learning methods matches or
exceeds the performance of other state-of-the-art methods according
to several evaluation metrics and expose the fundamental limitations
arising from training sets that are too small or too noisy. A Web-based
predictor, AquaSol, is available online through the ChemDB portal
(cdb.ics.uci.edu) together with additional material.