Machine learning prediction of UV-Vis spectra features of organic compounds related to photoreactive potential.

ABSTRACT: Machine learning (ML) algorithms were explored for the classification of the UV-Vis absorption spectrum of organic molecules based on molecular descriptors and fingerprints generated from 2D chemical structures. Training and test data (~ 75 k molecules and associated UV-Vis data) were assembled from a database with lists of experimental absorption maxima. They were labeled with positive class (related to photoreactive potential) if an absorption maximum is reported in the range between 290 and 700 nm (UV/Vis) with molar extinction coefficient (MEC) above 1000 Lmol^-1 cm^-1, and as negative if no such a peak is in the list. Random forests were selected among several algorithms. The models were validated with two external test sets comprising 998 organic molecules, obtain

SUBMITTER: Mamede R 

PROVIDER: S-EPMC8660842 | biostudies-literature | 2021 Dec

REPOSITORIES: biostudies-literature