# Protein-ligand binding kinetics setup with Seekrflow and Asyncflow

In this example, we will prepare, run, and analyze calculations to predict
binding and unbinding kinetics for a host-guest system - a common
benchmark system for computational methods to predict biomolecular
interactions.

![Integration of SEEKR into SINAPSE SDK to obtain estimates and ranking of binding kinetics by compound. The subsequent results will backpropagate into the refinement of protocols and the training of AI models.
](seekr_sinapse.png)

We will use two SINAPSE SDK components:

- **[Seekrflow](https://github.com/seekrcentral/seekrflow)** performs the calculations that will predict the kinetics.
- **[Asyncflow](https://github.com/radical-cybertools/radical.asyncflow)** an asynchronous workflow layer for Seekrflow pipeline.

## Prerequisites

These calculations will run on your local Linux machine, although additional 
configuration can allow running on remote compute resources.

## Install

The easiest, quickest way to install seekrflow is to use Mamba. If you don't already have
Mamba installed, Download the Miniforge install script and run.

```sh
curl -O https://github.com/conda-forge/miniforge/releases/latest/download/Miniforge3-$(uname)-$(uname -m).sh
bash Miniforge3-$(uname)-$(uname -m).sh
```

Once this has been done, set up a new environment:

```sh
mamba create -n SEEKR python=3.12 --yes
```

This step installs seekrflow from github.
```sh
mamba activate SEEKR
git clone https://github.com/seekrcentral/seekrflow.git
cd seekrflow
python -m pip install .
```

Next, install seekr:

```sh
mamba install seekr
```

One will also need to install OpenMM:

```sh
mamba install openmm
```

You may wish to specify the cuda version for your pre-installed version.

```
mamba install openmm cuda-nvrtc=##.# cuda-version=##.#
```

Where, of course, you replace the '##.#' with whatever Cuda version you have
installed, found using `nvidia-smi` or other such program.

## Run Example

Next, find the host-guest example directory and run the example:

```sh
seekrflow/seekrflow/examples/host_guest
python ~/seekrflow/seekrflow/flow.py prepare -i seekrflow_1_butanol_local.json
python ~/seekrflow/seekrflow/flow.py run -i seekrflow_1_butanol_local.json
python ~/seekr/seekr/analyze.py work/root/model.json
```

You will see the analysis printed to the screen. If you're curious, the 
experimentally-measured k-off for this compound is 3.8e8 1/s. This calculation 
is artificially truncated for demonstration purposes - a true seekr 
calculation should simulate much longer. The generated images can be 
seen in ~/test_seekr/images_and_plots.

![This example plot shows how the free energy profile across anchors is automatically computed and plotted following the analyze stage.
](free_energy_profile_milestones.png)