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

ABSTRACT: 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.

SUBMITTER: Rusanov AI

PROVIDER: S-EPMC8835262 | biostudies-literature | 2022 Jan

REPOSITORIES: biostudies-literature

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

Rusanov Aleksey I AI Dmitrieva Olga A OA Mamardashvili Nugzar Zh NZ Tetko Igor V IV

International journal of molecular sciences 20220121 3

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, extensi ...[more]

PMID: 35163123

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

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

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