1 code implementation • 5 Mar 2020 • Igor V. Tetko, Pavel Karpov, Ruud Van Deursen, Guillaume Godin
We investigated the effect of different training scenarios on predicting the (retro)synthesis of chemical compounds using a text-like representation of chemical reactions (SMILES) and Natural Language Processing neural network Transformer architecture.
Ranked #6 on Single-step retrosynthesis on USPTO-50k
1 code implementation • 21 Oct 2019 • Pavel Karpov, Guillaume Godin, Igor V. Tetko
That both the augmentation and transfer learning are based on embeddings allows the method to provide good results for small datasets.