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Integrating structure and experimental data annotations with computational modeling framework for predicting
Xinyu Yang1, Tong Wang1, Genoa R Warner2
1Tulane Center for Biomedical Informatics and Genomics, Tulane University, New Orleans, LA 70112, USA; Division of Biomedical Informatics and Genomics, Deming Department of Medicine, Tulane University, New Orleans, LA 70112, USA; Department of Chemistry and Biochemistry, Rowan University, Glassboro, NJ 08028, USA.
Machine learning models predict micro-nanoplastics (MNPs) toxicity efficiently. This study integrates geometrical and experimental data to assess MNP toxicity, offering a faster, cost-effective alternative to traditional methods.
Area of Science:
- Environmental Science and Toxicology
- Computational Chemistry
- Materials Science
Background:
- Micro-nanoplastics (MNPs) pollution is increasing, posing risks to human health.
- Existing experimental methods for MNP toxicity assessment are expensive and time-consuming.
- Computational modeling using machine learning (ML) offers a promising alternative, but lacks comprehensive data and consideration of complex MNP structures.
Purpose of the Study:
- To develop novel ML models for predicting MNP toxicity.
- To address data limitations by creating virtual MNPs (vMNPs) and integrating diverse descriptors.
- To provide a framework for efficient MNP toxicity assessment and guide future research.
Main Methods:
- Construction of three MNP datasets for popular toxicity endpoints.
- Generation of virtual MNPs (vMNPs) using nanostructure annotation.
- Digitalization of MNP structures and calculation of geometrical descriptors via Delaunay Tessellation.
- Integration of experimental variables (concentrations, cell lines) as training features.
- Development and validation of Partial Least Squares Regression (PLSR) models using leave-one-out cross-validation.
Main Results:
- Developed three novel ML models demonstrating reasonable performance in predicting MNP toxicity.
- Successfully integrated geometrical and experimental descriptors for enhanced model accuracy.
- Created a library of vMNPs with predicted properties and bioactivities to guide future MNP research.
Conclusions:
- The developed ML models offer a powerful tool for assessing the toxicity of new MNPs.
- The integrated modeling strategy effectively overcomes data limitations and enhances prediction accuracy.
- This approach can be extended to other MNP toxicity endpoints, facilitating broader MNP risk assessment.
