Learning continuous and data-driven molecular descriptors by translating equivalent chemical representations.

ABSTRACT: There has been a recent surge of interest in using machine learning across chemical space in order to predict properties of molecules or design molecules and materials with the desired properties. Most of this work relies on defining clever feature representations, in which the chemical graph structure is encoded in a uniform way such that predictions across chemical space can be made. In this work, we propose to exploit the powerful ability of deep neural networks to learn a feature representation from low-level encodings of a huge corpus of chemical structures. Our model borrows ideas from neural machine translation: it translates between two semantically equivalent but syntactically different representations of molecular structures, compressing the meaningful information both representa

SUBMITTER: Winter R 

PROVIDER: S-EPMC6368215 | biostudies-literature | 2019 Feb

REPOSITORIES: biostudies-literature