Boosting pre-trained model with silica nanoparticles cellular toxicity prediction.

Area of Science:

• Nanotechnology
• Materials Science
• Computational Toxicology

Background:

• Silica nanoparticles (SiNPs) are crucial in drug delivery and nanocomposites.
• Potential off-target accumulation and cytotoxicity of SiNPs necessitate robust safety evaluations.
• Existing data-driven methods face challenges with data leakage and poor generalization.

Purpose of the Study:

• To develop a pre-trained model-based framework for accurate silica nanoparticle cellular toxicity prediction.
• To address data leakage issues inherent in previous toxicity evaluation methods.
• To enhance the generalizability of predictive models for novel nanoparticle formulations.

Main Methods:

• Removed evaluation-stage features (e.g., Viability_indicator, Positive_control) to prevent data leakage.
• Utilized TabPFN's embedding layer to convert categorical values into dense vectors.
• Employed in-context learning on a pre-trained TabPFN model for improved generalizability.

Main Results:

• Achieved state-of-the-art classification performance on a public dataset.
• Effectively mitigated data leakage by excluding specific evaluation features.
• Demonstrated improved model generalizability for predicting toxicity of new nanoparticle types.

Conclusions:

• The proposed framework offers a robust solution for silica nanoparticle toxicity prediction.
• Mitigating data leakage and enhancing generalizability are key for reliable nanomedicine design.
• This approach supports the rational design of safer and more effective nanomedicines.