Molecules used to train generative models

This directory contains sets of molecules used to train chemical language models in the paper, "Learning generative models of molecules from limited training examples."

Between 1,000 and 500,000 molecules were sampled from each of four chemical databases (ChEMBL, COCONUT, GDB, and ZINC). These molecules were represented using either the SMILES, DeepSMILES, or SELFIES formats. For molecules in the SMILES format, data augmentation was also performed by enumerating non-canonical SMILES, with augmentation factors of 3x, 10x, or 30x. For each training dataset size, ten independent samples were drawn to assess variability.