Augmented Transformer Achieves 97% and 85% for Top5 Prediction of Direct and Classical Retro-Synthesis

5 Mar 2020Igor V. TetkoPavel KarpovRuud Van DeursenGuillaume Godin

We investigated the effect of different augmentation scenarios on predicting (retro)synthesis of chemical compounds using SMILES representation. We showed that augmentation of not only input sequences but also, importantly, of the target data eliminated the effect of data memorization by neural networks and improved their generalization performance for prediction of new sequences... (read more)

PDF Abstract

Results from the Paper


  Submit results from this paper to get state-of-the-art GitHub badges and help the community compare results to other papers.

Methods used in the Paper


METHOD TYPE
🤖 No Methods Found Help the community by adding them if they're not listed; e.g. Deep Residual Learning for Image Recognition uses ResNet