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<p>The article proposes a conceptual approach for evaluating the impact of engineered nanoparticles (NPs) on the functionality of small biomolecules. The developed machine learning (ML) model is based on in-silico 13&#xa0;C NMR spectroscopy chemical shifts derived by the SMILES notations on small biomolecules. The rationale behind this approach is that 13&#xa0;C NMR provide information about the atom environment of the carbon atoms. Thus, decomposing the small biomolecules into their fundamental 13&#xa0;C NMR spectral data, and performing classification based on the count and position of chemical peaks, establishes a baseline for evaluating the impact of NPs on the functionality of small biomolecules, even if the ML model is not based on nano data. The approach mitigates not only the scarcity of nano-bio data but also hold potential for building of NP`s portfolio by utilising data collected from various in vitro, in situ, in vivo, and organ-on-a-chip environments across multiple timeframes. Such a framework enables predictive modeling based on these multi-environmental datasets, facilitating a deeper understanding of NP behaviour. The methodology was demonstrated using data from bioassay focused on human dopamine D1 receptor antagonists provided by PubChem. The model was train with 26,766 samples and test on 5,466 samples, achieving Accuracy of 70.8%, Precision of 74.3%, recall of 63.6%, F1-score of 68.5% and ROC of 70.8% were achieved by the Support Vector classifier, with an Area Under the Curve (AUC) of 76% and Matthews Correlation Coefficient, MCC = 0.4204. A secondary, non-NP-related ML model was developed to complement the study case. It uses PubChem compound and substance identifiers (CIDs and SIDs) to predict whether pre-designed small biomolecules have the potential to be human dopamine D1 receptor antagonists.</p>

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Machine Learning Model Leveraging SMILES-derived NMR Spectroscopy Data to Predict Dopamine D1 Receptor Antagonists: A Prospective Framework for Forecasting the Impact of Engineered Nanoparticles on the Functionalities of Small Biomolecules

  • 期刊论文

  • 发表日期：

    2026年4月1日

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  • Mariya L. Ivanova,
  • Michael Nicholls,
  • Nicola Russo,
  • Gueorgui Mihaylov,
  • Konstantin Nikolic

摘要

The article proposes a conceptual approach for evaluating the impact of engineered nanoparticles (NPs) on the functionality of small biomolecules. The developed machine learning (ML) model is based on in-silico 13 C NMR spectroscopy chemical shifts derived by the SMILES notations on small biomolecules. The rationale behind this approach is that 13 C NMR provide information about the atom environment of the carbon atoms. Thus, decomposing the small biomolecules into their fundamental 13 C NMR spectral data, and performing classification based on the count and position of chemical peaks, establishes a baseline for evaluating the impact of NPs on the functionality of small biomolecules, even if the ML model is not based on nano data. The approach mitigates not only the scarcity of nano-bio data but also hold potential for building of NP`s portfolio by utilising data collected from various in vitro, in situ, in vivo, and organ-on-a-chip environments across multiple timeframes. Such a framework enables predictive modeling based on these multi-environmental datasets, facilitating a deeper understanding of NP behaviour. The methodology was demonstrated using data from bioassay focused on human dopamine D1 receptor antagonists provided by PubChem. The model was train with 26,766 samples and test on 5,466 samples, achieving Accuracy of 70.8%, Precision of 74.3%, recall of 63.6%, F1-score of 68.5% and ROC of 70.8% were achieved by the Support Vector classifier, with an Area Under the Curve (AUC) of 76% and Matthews Correlation Coefficient, MCC = 0.4204. A secondary, non-NP-related ML model was developed to complement the study case. It uses PubChem compound and substance identifiers (CIDs and SIDs) to predict whether pre-designed small biomolecules have the potential to be human dopamine D1 receptor antagonists.