1. Model Data

2. Model Deployment for Prediction

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3. Model Description

This is a partial least squares regression (PLSR) model for NanoAID-9 (logP).
The model was developed using a training set of 131 nanomaterials (NMs) and employed a 5-fold cross-validation procedure. Additionally, it was validated with a validation set of 16 NMs. The model's coefficient of determination (R²) for the training set is 0.673, and it has a root mean squared error (RMSE) of 0.905. For the validation set, the R² value is 0.739, with an RMSE of 1.021. The experimental logP values of all the nanoparticles were determined using "shaking flask" method.