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Updated: Jan 30, 2026

Breath Collection from Children for Disease Biomarker Discovery
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Predictive collective variable discovery with deep Bayesian models.

Markus Schöberl1, Nicholas Zabaras1, Phaedon-Stelios Koutsourelakis2

  • 1Center for Informatics and Computational Science, University of Notre Dame, 311 Cushing Hall, Notre Dame, Indiana 46556, USA.

The Journal of Chemical Physics
|January 17, 2019
PubMed
Summary
This summary is machine-generated.

We introduce a Bayesian inference approach to discover crucial collective variables (CVs) for enhanced sampling in complex molecular simulations. This method accelerates exploration of atomistic systems and improves understanding of underlying mechanisms.

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Area of Science:

  • Computational Chemistry and Physics
  • Biochemistry
  • Materials Science

Background:

  • Complex atomistic systems in biochemistry and materials science face spatio-temporal scale limitations in current models.
  • Enhanced sampling methods are crucial for overcoming these limitations, but their efficiency relies heavily on the selection of collective variables (CVs).

Purpose of the Study:

  • To develop a novel method for discovering effective CVs.
  • To formulate CV discovery as a Bayesian inference problem, treating CVs as hidden generators of atomistic trajectories.
  • To enable accurate estimation of observables and their uncertainties from limited data.

Main Methods:

  • Formulation of CV discovery as a Bayesian inference problem.
  • Utilizing machine learning and variational inference techniques.
  • Treating CVs as hidden generators of full-atomistic trajectories.

Main Results:

  • The methodology generates samples of fine-scale atomistic configurations from limited training data.
  • Discovered CVs correlate with essential physicochemical properties.
  • Quantitative assessment demonstrates the predictive ability of the discovered CVs for alanine dipeptide (ALA-2) and ALA-15 peptide.

Conclusions:

  • The Bayesian inference approach effectively discovers relevant CVs for enhanced sampling.
  • This method enhances the exploration of configurational space in complex molecular systems.
  • The discovered CVs provide insights into mechanisms, particularly in unexplored scientific domains.