Cautionary Guidelines for Machine Learning Studies with Combinatorial Datasets.

Area of Science:

• Organic Chemistry
• Computational Chemistry
• Data Science

Background:

• Regression modeling is increasingly used in organic chemistry for reaction prediction.
• Combinatorial datasets are often employed to maximize data points efficiently.
• A common issue is models fitting dataset artifacts rather than chemical principles.

Purpose of the Study:

• To illustrate pitfalls of regression modeling with combinatorial datasets in organic chemistry.
• To demonstrate control experiments for identifying problematic dataset patterns.
• To suggest validation methods for model generalizability and interpretability.

Main Methods:

• Case study analysis of regression modeling on combinatorial datasets.
• Design and execution of control experiments.
• Development of validation strategies for model assessment.

Main Results:

• Combinatorial datasets can lead to models that capture spurious correlations.
• Specific control experiments can reveal overfitting to dataset characteristics.
• Proper validation is crucial for reliable predictive models.

Conclusions:

• Models trained on combinatorial data require careful validation to ensure chemical relevance.
• Overfitting to dataset patterns compromises model interpretability and predictive power.
• Adopting suggested validation techniques enhances the reliability of regression models in organic chemistry.