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New preprint on BICePs with Replica-averaging

Our new preprint just dropped on ChemRxiv:

Model selection using replica averaging with Bayesian inference of conformational populations.
Raddi RM, Marshall T, Ge Y, Voelz V.

In this work, we describe and demonstrate with examples how equipping our BICePs algorithm with a replica-averaged forward model makes it a Maximum-Entropy reweighting method with some key advantages:

  • It is a post-processing method that does not require restraints implemented inside simulations
  • Heuristic estimates of uncertainty are not needed; posterior sampling enables learning uncertainties from the data
  • BICePs has improved likelihood models to account for outliers
  • The BICePs score can be used for objective model selection
  • Automated force field and forward model parameterization can be performed using the BICePs score as an objective function.

I recently spoke about this work at the ACS Fall 2023 symposium “Emerging Techniques to Quantify Biomolecular Conformational Ensembles” symposium. 

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