  Due to its high computational speed and accuracy compared to ab-initio
quantum chemistry and forcefield modeling, the prediction of molecular
properties using machine learning has received great attention in the fields of
materials design and drug discovery. A main ingredient required for machine
learning is a training dataset consisting of molecular features\textemdash for
example fingerprint bits, chemical descriptors, etc. that adequately
characterize the corresponding molecules. However, choosing features for any
application is highly non-trivial. No "universal" method for feature selection
exists. In this work, we propose a data fusion framework that uses Independent
Vector Analysis to exploit underlying complementary information contained in
different molecular featurization methods, bringing us a step closer to
automated feature generation. Our approach takes an arbitrary number of
individual feature vectors and automatically generates a single, compact (low
dimensional) set of molecular features that can be used to enhance the
prediction performance of regression models. At the same time our methodology
retains the possibility of interpreting the generated features to discover
relationships between molecular structures and properties. We demonstrate this
on the QM7b dataset for the prediction of several properties such as
atomization energy, polarizability, frontier orbital eigenvalues, ionization
potential, electron affinity, and excitation energies. In addition, we show how
our method helps improve the prediction of experimental binding affinities for
a set of human BACE-1 inhibitors. To encourage more widespread use of IVA we
have developed the PyIVA Python package, an open source code which is available
for download on Github.
