Improving Deep Generative Models with Randomized SMILES

摘要

A Recurrent Neural Network (RNN) trained with a set of molecules represented as SMILES strings can generate millions of different valid and meaningful chemical structures. In most of the reported architectures the models have been trained using a canonical (unique for each molecule) representation of SMILES. Instead, this research shows that when using randomized SMILES as a data amplification technique, a model can generate more molecules and those are going to accurately represent the training set properties. To show that, an extensive benchmark study has been conducted using research from a recently published article which shows that models trained with molecules from the GDB-13 database (975 million molecules) achieve better overall chemical space coverage when the posterior probability distribution is as uniform as possible. Specifically, we created models that generate nearly all the GDB-13 chemical space using only 1 million molecules as training set. Lastly, models were also trained with smaller training set sizes and show substantial improvement when using randomized SMILES compared to canonical.