Molecular Dynamics Methods Development
I have developed 4 different methods for enhanced sampling of molecular dynamics simulations (3 published in Journal of Chemical Physics, 1 published in Journal of Chemical Information and Modeling). My future plans include both further work in this vein of theory and methods development, as well as implementation of my methods into more generalized (and publicly available) software. For a few examples of this type of work, please see:
Advances in milestoning. I. Enhanced sampling via wind-assisted reweighted milestoning (WARM):
https://aip.scitation.org/doi/abs/10.1063/1.5029954
Advances in milestoning. II. Calculating time-correlation functions from milestoning using stochastic path integrals:
https://aip.scitation.org/doi/abs/10.1063/1.5037482
Automated placement of interfaces in conformational kinetics calculations using machine learning:
Machine Learning Applications for the Molecular Sciences
Another main focus of my research is the development of machine learning-based methods for both setting up and interpreting the results of molecular simulations. I have developed machine learning-based methodology for studying the kinetics of both configurational changes in complex macromolecules, as well as multiple pathway chemical kinetics. I am also interested in developing machine learning-based methodology for interpreting experimental data. For examples of this type of work, please see the following:
Predicting Reaction Products and Automating Reactive Trajectory Characterization in Molecular Simulations with Support Vector Machines:
https://pubs.acs.org/doi/abs/10.1021/acs.jcim.9b00134
Comparative Exploratory Analysis of Intrinsically Disordered Protein Dynamics Using Machine Learning and Network Analytic Methods
https://doi.org/10.3389/fmolb.2019.00042
Automated placement of interfaces in conformational kinetics calculations using machine learning:
Coarse-grained Modeling of Biophysical Systems
My third research focus is the development of coarse-grained modeling techniques for molecular systems whose complexity make atomistic models intractable on the time scales of interest. I have built models for phenomena such as force-modulated catalytic activity of enzymes and protein aggregation events, such as the self-assembly of amyloid fibrils. For examples of this type of work, please see the following:
Network-Based Classification and Modeling of Amyloid Fibrils:
https://pubs.acs.org/doi/abs/10.1021/acs.jpcb.9b03494
A Fokker-Planck type model of force-modulated catalytic activity in thioredoxyn:
https://aip.scitation.org/doi/abs/10.1063/1.4926664
Local Graph Stability in Exponential Family Random Graph Models
Network Hamiltonian Coarse-grained Models of Self-Assembly
This video offers a "lightning talk" style description of a type of coarse-grained modeling that I do, which uses network Hamiltonians to model molecular self-assembly (e.g. amyloid fibril formation).
Google Scholar Profile
If you are interested in reading more about my research, please click on the link to the right to see my Google Scholar profile.