Automatic feature selection and weighting in molecular systems using Differentiable Information Imbalance

Romina Wild, Felix Wodaczek, Vittorio Del Tatto, Bingqing Cheng and Alessandro Laio (laio@sissa.it)
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Romina Wild: International School for Advanced Studies (SISSA)
Felix Wodaczek: The Institute of Science and Technology Austria (ISTA)
Vittorio Del Tatto: International School for Advanced Studies (SISSA)
Bingqing Cheng: The Institute of Science and Technology Austria (ISTA)
Alessandro Laio: International School for Advanced Studies (SISSA)

Nature Communications, 2025, vol. 16, issue 1, 1-12

Abstract: Abstract Feature selection is essential in the analysis of molecular systems and many other fields, but several uncertainties remain: What is the optimal number of features for a simplified, interpretable model that retains essential information? How should features with different units be aligned, and how should their relative importance be weighted? Here, we introduce the Differentiable Information Imbalance (DII), an automated method to rank information content between sets of features. Using distances in a ground truth feature space, DII identifies a low-dimensional subset of features that best preserves these relationships. Each feature is scaled by a weight, which is optimized by minimizing the DII through gradient descent. This allows simultaneously performing unit alignment and relative importance scaling, while preserving interpretability. DII can also produce sparse solutions and determine the optimal size of the reduced feature space. We demonstrate the usefulness of this approach on two benchmark molecular problems: (1) identifying collective variables that describe conformations of a biomolecule, and (2) selecting features for training a machine-learning force field. These results show the potential of DII in addressing feature selection challenges and optimizing dimensionality in various applications. The method is available in the Python library DADApy.

Date: 2025
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DOI: 10.1038/s41467-024-55449-7

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