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Response to Comment on "Predicting reaction performance in C-N cross-coupling using machine learning".

Jesús G Estrada¹, Derek T Ahneman¹, Robert P Sheridan²

¹Department of Chemistry, Princeton University, Princeton, NJ 08544, USA.

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|November 17, 2018

Summary

This summary is machine-generated.

Our chemical-feature model shows superior predictive performance compared to nongeneralizable alternatives. This validates the use of chemical featurization for machine learning pattern extraction in datasets.

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Area of Science:

Computational chemistry
Machine learning applications
Predictive modeling

Background:

Nongeneralizable models pose challenges in scientific prediction.
The utility of chemical featurization in machine learning requires validation.

Purpose of the Study:

To distinguish the chemical-feature model from nongeneralizable models.
To evaluate the out-of-sample predictive performance of the chemical-feature model.

Main Methods:

Comparative analysis of predictive models.
Assessment of out-of-sample prediction accuracy.
Utilizing chemical featurization for machine learning.

Main Results:

The chemical-feature model is demonstrably distinct from nongeneralizable models.
Significant outperformance of the chemical-feature model in out-of-sample predictions.
Validation of chemical featurization for extracting meaningful data patterns.

Conclusions:

The chemical-feature model offers superior predictive power.
Chemical featurization is a robust approach for machine learning in chemical datasets.
The findings support the original methodology for pattern extraction.