  In our study, we demonstrate the synergy effect between convolutional neural
networks and the multiplicity of SMILES. The model we propose, the so-called
Convolutional Neural Fingerprint (CNF) model, reaches the accuracy of
traditional descriptors such as Dragon (Mauri et al. [22]), RDKit (Landrum
[18]), CDK2 (Willighagen et al. [43]) and PyDescriptor (Masand and Rastija
[20]). Moreover the CNF model generally performs better than highly fine-tuned
traditional descriptors, especially on small data sets, which is of great
interest for the chemical field where data sets are generally small due to
experimental costs, the availability of molecules or accessibility to private
databases. We evaluate the CNF model along with SMILES augmentation during both
training and testing. To the best of our knowledge, this is the first time that
such a methodology is presented. We show that using the multiplicity of SMILES
during training acts as a regulariser and therefore avoids overfitting and can
be seen as ensemble learning when considered for testing.
