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

Area of Science:

• Organic Chemistry
• Computational Chemistry
• Data Science

Background:

• Multiple Linear Regression (MLR) is a powerful statistical technique.
• Accurate prediction of chemical reaction outcomes is crucial for efficient synthesis.
• Interpretability of predictive models is essential for mechanistic understanding.

Purpose of the Study:

• To provide a comprehensive workflow for designing and executing MLR campaigns in organic chemistry.
• To accelerate reaction outcome prediction while maintaining model interpretability.
• To guide researchers in leveraging data for reliable chemical predictions.

Main Methods:

• Data preparation and feature generation tailored for chemical data.
• Analysis of data distribution to inform model building strategies.
• Model building, validation, and virtual screening using MLR.
• Guidance on data splitting strategies based on data size and distribution.

Main Results:

• A robust workflow for MLR campaigns in organic chemistry is presented.
• The workflow enhances both the speed of reaction outcome prediction and model interpretability.
• Data characteristics significantly influence model reliability and interpretability.

Conclusions:

• Implementing the outlined MLR workflow can accelerate chemical research.
• Careful consideration of data properties is key to building reliable and interpretable predictive models.
• This approach supports informed decision-making in synthetic chemistry.