Using neural networks for enhanced sampling in computational biophysics
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Updated
Aug 17, 2017 - Jupyter Notebook
Using neural networks for enhanced sampling in computational biophysics
Using supervised machine learning to build collective variables for accelerated sampling
Enhanced protein mutational sampling using time-lagged variational autoencoders
C++ implementation of metadynamics simulation on a potential energy surface
Additional plumed plugins
🦜 DISCOTRESS 🦜 is a software package to simulate and analyse the dynamics on arbitrary Markov chains
Prediction of magnesium binding sites in RNA molecules using GCMC/MD (Updated version: https://github.com/mackerell-lab/GCMC_PME)
Variationally enhanced sampling for single-particle langevin dynamics with neural network bias potentials and path collective variables. Based on OpenMM + PyTorch.
Files for PLUMED Masterclass-22-11
Machine Learning Transition State Analysis (MLTSA) suite with Analytical models to create data on demand and test the approach on different types of data and ML models.
Plugin for OpenMM providing simulation data wrappers as DLPack data structures
Development version of PLUMED 2 which includes the VES code for performing variationally enhanced sampling simulations
Software Suite for Advanced General Ensemble Simulations
WESTPA modified scripts with NAMD
These are codes of toy physics models that contain building blocks for understanding the concept behind EVCCPMC
Wrapper for LAMMPS simulation data into DLPack data structures
Data-driven discovery of linear molecular probes with optimal selective affinity for PFAS in water
Permutationally invariant networks for enhanced sampling (PINES)
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