Best Practices and Considerations for Applying Multiple Linear Regression in Organic Chemistry Research

This Synopsis provides guidance for designing and executing Multiple Linear Regression (MLR) campaigns in organic chemistry. It outlines key steps of a robust workflow for accelerating reaction outcome prediction and maintaining interpretability, including data preparation, feature generation, data distribution analysis, model building, validation, and virtual screening. Emphasis is placed on defining a clear chemical objective and establishing mechanistic hypotheses. Representative examples illustrate how data size and distribution guide data splitting strategies and ratios, ultimately shaping model reliability and interpretability.