Selecting the best set of features to train predictive models is known to be a challenging problem. A bottom-up greedy feature selection method was employed to reduce the redundancy, noise, and low representativity of the 264 graph-based signature features obtained to represent molecules.

This method starts with zero features, by considering each feature independently, adds them one by one in accordance to a machine learning model, and keeps only the set of features with the most prominent performance metric (e.g., Pearson’s correlation coefficient) on that particular model.

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