  Several novel algorithms for learning molecular representations have been
proposed recently with the consolidation of deep learning in computer-aided
drug design. Learned molecular embeddings allow attaining rich representations
of the molecular structure and physical-chemical properties while overcoming
several limitations of traditional molecular representations. Despite their
theoretical benefits, it is not clear how molecular embeddings compare with
each other and with traditional representations, which in turn hinders the
process of choosing a suitable embedding algorithm for QSAR modeling. A reason
for this lack of consensus is that a fair and thorough comparison of different
approaches is not straightforward. To close this gap, we reproduced three
unsupervised and two supervised molecular embedding techniques recently
proposed in the literature. Through a thorough experimental setup, we compared
the molecular representations of these five methods concerning their
performance in QSAR scenarios using five different datasets with varying class
imbalance levels. We also compared these representations to traditional
molecular representations, namely molecular descriptors and fingerprints. Our
results show that molecular embeddings did not significantly surpass baseline
results obtained using traditional molecular representations. While supervised
techniques yielded competitive results compared to those obtained by
traditional molecular representations, unsupervised techniques did not match
the baseline results. Our results motivate a discussion about the usefulness of
molecular embeddings in QSAR modeling and their potential in other drug design
areas, such as similarity analysis and de novo drug design.
