More Is Not Always Better: Local Models Provide Accurate Predictions of Spectral Properties of Porphyrins

The development of new functional materials based on porphyrins requires fast and accurate prediction of their spectral properties. The available models in the literature for absorption wavelength and extinction coefficient of the Soret band have low accuracy for this class of compounds. We collecte...

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Bibliographic Details
Published in:International journal of molecular sciences 2022-01, Vol.23 (3), p.1201
Main Authors: Rusanov, Aleksey I, Dmitrieva, Olga A, Mamardashvili, Nugzar Zh, Tetko, Igor V
Format: Article
Language:English
Subjects:
absorbance maximum wavelength
Absorption spectra
Chemical compounds
chromophores
Coefficients
Computer Simulation
Functional materials
Homology
Laboratories
Learning algorithms
local model
Machine learning
Models, Molecular
Molecular Structure
Neural networks
Optical properties
Porphyrins
Porphyrins - chemistry
Predictions
QSPR
Random Forest
Solvents
Spectrophotometry - methods
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Summary:The development of new functional materials based on porphyrins requires fast and accurate prediction of their spectral properties. The available models in the literature for absorption wavelength and extinction coefficient of the Soret band have low accuracy for this class of compounds. We collected spectral data for porphyrins to extend the literature set and compared the performance of global and local models for their modelling using different machine learning methods. Interestingly, extension of the public database contributed models with lower accuracies compared to the models, which we built using porphyrins only. The later model calculated acceptable RMSE = 2.61 for prediction of the absorption band of 335 porphyrins synthesized in our laboratory, but had a low accuracy (RMSE = 0.52) for extinction coefficient. A development of models using only compounds from our laboratory significantly decreased errors for these compounds (RMSE = 0.5 and 0.042 for absorption band and extinction coefficient, respectively), but limited their applicability only to these homologous series. When developing models, one should clearly keep in mind their potential use and select a strategy that could contribute the most accurate predictions for the target application. The models and data are publicly available.
ISSN:1422-0067
1661-6596
1422-0067
DOI:10.3390/ijms23031201