Inferring molecular structure from Nuclear Magnetic Resonance (NMR) measurements
requires an accurate forward model that can predict chemical shifts from 3D structure …

This review describes recent advances by the authors and others on the topic of
incorporating experimental data into molecular simulations through maximum entropy …

Coarse-grained (CG) molecular dynamics (MD) can simulate systems inaccessible to fine-
grained (FG) MD simulations. A CG simulation decreases the degrees of freedom by …

Enhanced sampling techniques have revolutionized molecular dynamics (MD) simulations,
enabling the study of rare events and the calculation of free energy differences in complex …

Misfolded amyloid peptides are neurotoxic molecules associated with Alzheimer's disease.
The Aβ21–30 peptide fragment is a decapeptide fragment of the complete Aβ42 peptide …

This work leverages machine learning methods to address various applications in
biomolecular design. Initially, I developed a graph neural network (GNN) capable of …

As interest grows in applying machine learning force-fields and methods to molecular
simulation, there is a need for state-of-the-art inference methods to use trained models …

Inferring molecular structure from NMR measurements requires an accurate forward model
that can predict chemical shifts from 3D structure. Current forward models are limited to …

Explaining and predicting experimental results are the goals of molecular simulations.
Molecular simulations that reproduce experimental results give a molecular explanation of …

There are many events in nature, like self-assembly of peptides, which span a wide range of
time and space. While some multiscale phenomena have detrimental effects and play a …