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# dcTMD
This package aids in the analysis of dissipation-corrected targeted molecular dynamics (dcTMD) simulations. The method enforces rare unbinding events of ligands from proteins via a constraint pulling bias. Subsequently, free energy profiles and friction factors are estimated along the unbinding coordinate. For a methodological overview, see our [article](https://pubs.acs.org/doi/full/10.1021/acs.jctc.8b00835).
> S. Wolf, and G. Stock,
> *Targeted molecular dynamics calculations of free energy profiles using a nonequilibrium friction correction.*,
> **J. Chem. Theory Comput.** 2018 14 (12), 6175-6182,
> doi: [10.1021/acs.jctc.8b00835](https://pubs.acs.org/doi/10.1021/acs.jctc.8b00835)
This package will be published soon:
> V. Tänzel, M. Jäger, D. Nagel, and S. Wolf,
> *Dissipation Corrected Targeted Molecular Dynamics*,
> in preparation 2023
We kindly ask you to cite these articles in case you use this software package for published works.
## Features
- Intuitive usage via module and CI
- Sklearn-style API for fast integration into your Python workflow
- Supports Python 3.8-3.10
- Multitude of [publications](https://www.moldyn.uni-freiburg.de/publications.html) with dcTMD
## Implemented Key Functionalities
- Estimation of free energy profiles and friction factors along the unbinding coordinate of ligands as described by [Wolf and Stock 2018](https://pubs.acs.org/doi/full/10.1021/acs.jctc.8b00835).
- Analysis of separate unbinding pathways as described by [Wolf et al. 2022](https://arxiv.org/abs/2212.07154).
## Installation
The package will be available on PiPY and conda. Until then, install it via:
```bash
python3 -m pip install git+ssh://git@github.com/moldyn/dcTMD.git
```
## Usage
Check out the documentation for an overview over all modules as well as the tutorials.
## Roadmap
- [ ] New Features:
- [ ] Gaussian error estimation
- [ ] 2d distribution WorkSet plots
- [x] Estimator plots: free energy, friction & both
- [x] Normality plot
- [x] Confidence intervals
- [ ] Exponential estimator class
- [ ] Discuss gaussian kernel borders
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