UnCorrupt SMILES: a novel approach to de novo design.

ABSTRACT: Generative deep learning models have emerged as a powerful approach for de novo drug design as they aid researchers in finding new molecules with desired properties. Despite continuous improvements in the field, a subset of the outputs that sequence-based de novo generators produce cannot be progressed due to errors. Here, we propose to fix these invalid outputs post hoc. In similar tasks, transformer models from the field of natural language processing have been shown to be very effective. Therefore, here this type of model was trained to translate invalid Simplified Molecular-Input Line-Entry System (SMILES) into valid representations. The performance of this SMILES corrector was evaluated on four representative methods of de novo generation: a recurrent neural network (RNN), a target-di

SUBMITTER: Schoenmaker L 

PROVIDER: S-EPMC9926805 | biostudies-literature | 2023 Feb

REPOSITORIES: biostudies-literature