Molecular descriptors are commonly used to digitally represent the physical structure of a molecule in quantitative structure-activity relationship machine learning models, but the overwhelming abundance of features can negatively impact the model's predictive performance. This research explores causal inference modeling as a method of feature engineering for ensemble-based machine learning techniques, and evaluates model performance against other common methods of feature evaluation.

Causal inference modeling for feature engineering of QSAR machine learning models

Macromolecular Chemistry: The Second Century

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ACS Spring 2020 National Meeting & Expo

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