  Recurrent neural networks have been widely used to generate millions of de
novo molecules in a known chemical space. These deep generative models are
typically setup with LSTM or GRU units and trained with canonical SMILEs. In
this study, we introduce a new robust architecture, Generative Examination
Networks GEN, based on bidirectional RNNs with concatenated sub-models to learn
and generate molecular SMILES with a trained target space. GENs autonomously
learn the target space in a few epochs while being subjected to an independent
online examination mechanism to measure the quality of the generated set. Here
we have used online statistical quality control (SQC) on the percentage of
valid molecules SMILES as an examination measure to select the earliest
available stable model weights. Very high levels of valid SMILES (95-98%) can
be generated using multiple parallel encoding layers in combination with SMILES
augmentation using unrestricted SMILES randomization. Our architecture combines
an excellent novelty rate (85-90%) while generating SMILES with a strong
conservation of the property space (95-99%). Our flexible examination mechanism
is open to other quality criteria.
