Application of a wavelength angle mapper for variable selection in iterative optimization technology predictions of drug content in pharmaceutical powder mixtures

Rish, Adam J.; Henson, Samuel R.; Velez-Silva, Natasha L.; Nahid Hasan, Md.; Drennen, James K.; Anderson, Carl A.

doi:10.1016/j.ijpharm.2023.123261

Cited by 2 publications

(1 citation statement)

References 19 publications

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“…The evaluation of the algorithms' performance included metrics such as the receiver operating characteristic curve (ROC), area under the curve (AUC), accuracy rate (ACC), and F1-score (F1). For wavelength predictions (λ abs , λ em_mono , λ em_agg ), four regression ML algorithms were selected: RF, KNN, GBR, and least absolute shrinkage and selection operator (LASSO) regression algorithms, which were all adopted in the prediction of wavelengths in recent reports [55,56]. Pearson correlation coefficient (r), mean relative error (MRE), and mean absolute error (MAE) were used to evaluate the algorithms' performances.…”

Section: Prediction Of Quantum Yields In the Aggregated And Monomeric...mentioning

confidence: 99%

Machine Learning Prediction of Quantum Yields and Wavelengths of Aggregation-Induced Emission Molecules

Bi,

Jiang,

Chen

et al. 2024

Materials

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The aggregation-induced emission (AIE) effect exhibits a significant influence on the development of luminescent materials and has made remarkable progress over the past decades. The advancement of high-performance AIE materials requires fast and accurate predictions of their photophysical properties, which is impeded by the inherent limitations of quantum chemical calculations. In this work, we present an accurate machine learning approach for the fast predictions of quantum yields and wavelengths to screen out AIE molecules. A database of about 563 organic luminescent molecules with quantum yields and wavelengths in the monomeric/aggregated states was established. Individual/combined molecular fingerprints were selected and compared elaborately to attain appropriate molecular descriptors. Different machine learning algorithms combined with favorable molecular fingerprints were further screened to achieve more accurate prediction models. The simulation results indicate that combined molecular fingerprints yield more accurate predictions in the aggregated states, and random forest and gradient boosting regression algorithms show the best predictions in quantum yields and wavelengths, respectively. Given the successful applications of machine learning in quantum yields and wavelengths, it is reasonable to anticipate that machine learning can serve as a complementary strategy to traditional experimental/theoretical methods in the investigation of aggregation-induced luminescent molecules to facilitate the discovery of luminescent materials.

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Section: Prediction Of Quantum Yields In the Aggregated And Monomeric...mentioning

confidence: 99%

Machine Learning Prediction of Quantum Yields and Wavelengths of Aggregation-Induced Emission Molecules

Bi,

Jiang,

Chen

et al. 2024

Materials

View full text Add to dashboard Cite

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Defining the Range of Calibration Burden: From Full Calibration to Calibration-Free

Rish,

Henson,

Drennen

et al. 2024

J Pharm Innov

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The expansion of spectroscopic process analytical technology (PAT) within the pharmaceutical industry requires chemometric models to enable spectral interpretation. However, calibration of chemometric models often require an intense time, material, and financial burden which may discourage PAT deployment. The advancement of modeling strategies that reduce this calibration burden necessitates consistent terminology for describing model inputs and overall calibration burden throughout chemometric literature. The goal of this article is to define levels of calibration burden by clarifying dataset classifications and model inputs during PAT method development. The scope of this work is specific to calibration of chemometric models incorporated in PAT methods. Harmonizing the language used in the field of chemometrics to describe calibration burden benefits both pharmaceutical manufacturers and regulatory agencies.

show abstract

Application of a wavelength angle mapper for variable selection in iterative optimization technology predictions of drug content in pharmaceutical powder mixtures

Cited by 2 publications

References 19 publications

Machine Learning Prediction of Quantum Yields and Wavelengths of Aggregation-Induced Emission Molecules

Machine Learning Prediction of Quantum Yields and Wavelengths of Aggregation-Induced Emission Molecules

Defining the Range of Calibration Burden: From Full Calibration to Calibration-Free

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