State-of-the-art augmented NLP transformer models for direct and single-step retrosynthesis.

ABSTRACT: We investigated the effect of different training scenarios on predicting the (retro)synthesis of chemical compounds using text-like representation of chemical reactions (SMILES) and Natural Language Processing (NLP) neural network Transformer architecture. We showed that data augmentation, which is a powerful method used in image processing, eliminated the effect of data memorization by neural networks and improved their performance for prediction of new sequences. This effect was observed when augmentation was used simultaneously for input and the target data simultaneously. The top-5 accuracy was 84.8% for the prediction of the largest fragment (thus identifying principal transformation for classical retro-synthesis) for the USPTO-50k test dataset, and was achieved by a combination of SM

SUBMITTER: Tetko IV 

PROVIDER: S-EPMC7643129 | biostudies-literature | 2020 Nov

REPOSITORIES: biostudies-literature