UV-adVISor: Attention-Based Recurrent Neural Networks to Predict UV-Vis Spectra.

ABSTRACT: Ultraviolet-visible (UV-Vis) absorption spectra are routinely collected as part of high-performance liquid chromatography (HPLC) analysis systems and can be used to identify chemical reaction products by comparison to the reference spectra. Here, we present UV-adVISor as a new computational tool for predicting the UV-Vis spectra from a molecule's structure alone. UV-Vis prediction was approached as a sequence-to-sequence problem. We utilized Long-Short Term Memory and attention-based neural networks with Extended Connectivity Fingerprint Diameter 6 or molecule SMILES to generate predictive models for the UV spectra. We have produced two spectrum datasets (dataset I, N = 949, and dataset II, N = 2222) using different compound collections and spectrum acquisition methods to tra

SUBMITTER: Urbina F 

PROVIDER: S-EPMC9137254 | biostudies-literature | 2021 Dec

REPOSITORIES: biostudies-literature