GEN: Highly Efficient SMILES Explorer Using Autodidactic Generative Examination Networks

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, Generatice Examination Network GEN, based on bidirectional RNNs with concatenated submodels to learn and generate molecular SMILES within a trained target space. GENs autonomously learn the target space in a few epochs while being subjected to online examination for quality on the generated set. Here we have used online statistical quality control (SQC) on the percentage of valid molecular SMILES as 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 combine an excellent novelty rate (85-90%) while generating SMILES with strong conservation of the property space (95-99%). Our flexible examination mechanism is open to other quality criteria