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.