An end-to-end deep learning framework for translating mass spectra to de-novo molecules.

ABSTRACT: Elucidating the structure of a chemical compound is a fundamental task in chemistry with applications in multiple domains including drug discovery, precision medicine, and biomarker discovery. The common practice for elucidating the structure of a compound is to obtain a mass spectrum and subsequently retrieve its structure from spectral databases. However, these methods fail for novel molecules that are not present in the reference database. We propose Spec2Mol, a deep learning architecture for molecular structure recommendation given mass spectra alone. Spec2Mol is inspired by the Speech2Text deep learning architectures for translating audio signals into text. Our approach is based on an encoder-decoder architecture. The encoder learns the spectra embeddings, while the decoder, pre-train

SUBMITTER: Litsa EE 

PROVIDER: S-EPMC10290119 | biostudies-literature | 2023 Jun

REPOSITORIES: biostudies-literature