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Updated: Dec 8, 2025

Author Spotlight: A Computational Approach to Decipher Amino Acid Preferences in Multispecific Protein-Protein Interactions
Published on: January 26, 2024
Hybridizing physical and data-driven prediction methods for physicochemical properties
Fabian Jirasek1, Robert Bamler1, Stephan Mandt1
1Department of Computer Science, University of California, Donald Bren Hall, Irvine, CA 92697, USA. fabian.jirasek@mv.uni-kl.de.
Abstract:
We present a generic way to hybridize physical and data-driven methods for predicting physicochemical properties. The approach 'distills' the physical method's predictions into a prior model and combines it with sparse experimental data using Bayesian inference. We apply the new approach to predict activity coefficients at infinite dilution and obtain significant improvements compared to the physical and data-driven baselines and established ensemble methods from the machine learning literature.
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