Molecular_Predicted_Solubility using oneAPI Toolkit

Описание: Data Loading and Preprocessing: The initial steps involve loading the dataset using pandas and computing molecular descriptors with rdkit. While these steps are not directly related to Intel optimizations, efficient data loading and preprocessing can benefit from fast, multi-threaded operations that Intel CPUs support.

Descriptor Calculation: The computation of molecular descriptors with rdkit might not be explicitly optimized for Intel hardware, but general performance can still benefit from the efficient use of multi-core processing.

Machine Learning Model Training: This is where Intel's optimizations can significantly impact. When training the XGBRegressor model, if xgboost is compiled with support for Intel's optimizations (e.g., using Intel's oneDAL for optimized mathematical routines), the training process can leverage multi-core CPUs and SIMD instructions for faster computations. This results in quicker model training times and can also improve model scalability when dealing with large datasets.

Prediction and Visualization: Similar to the training phase, predictions made with the XGBRegressor model can also benefit from Intel's hardware optimizations, speeding up the process of generating solubility predictions for the chemical compounds. The visualization step primarily involves the rdkit library and might not directly benefit from Intel optimizations unless the drawing operations or underlying data handling are multi-threaded or require intensive computations.
https://github.com/AbhiLegend/Molecul...