docs: add README

README.md

@@ -0,0 +1,103 @@
+
+
+# moldrug
+
+**moldrug** is a Python package for drug-oriented optimization in chemical space. It leverages genetic algorithms, multi-criteria optimization, and Derringer-Suich desirability functions to navigate and optimize molecular structures for drug design applications. The toolkit integrates structure-based docking (AutoDock Vina), cheminformatics (RDKit, Meeko), and customizable fitness scoring to guide evolutionary search strategies.
+
+## Installation
+
+`moldrug` requires Python 3.8 to 3.11. Key dependencies include `rdkit`, `meeko`, `crem`, `numpy`, `pandas`, and `autodock-vina`.
+
+### From PyPI
+```bash
+pip install moldrug
+```
+
+### From Source
+```bash
+git clone https://github.com/ale94mleon/moldrug.git
+cd moldrug
+pip install .
+```
+
+### Using Conda
+```bash
+conda create -n moldrug_env python=3.9
+conda activate moldrug_env
+conda install -c conda-forge rdkit meeko vina
+pip install moldrug
+```
+
+## Usage
+
+`moldrug` is primarily driven via command-line interface using YAML configuration files. It supports multi-step evolutionary workflows, custom fitness functions, and constraint-based docking.
+
+### Command Line Interface
+Run an optimization workflow by pointing to a configuration file:
+```bash
+moldrug config.yml
+```
+
+Specify a custom fitness scoring script (e.g., integrating MolSkill or predictive models):
+```bash
+moldrug config.yml --fitness /path/to/custom_fitness.py
+```
+
+For constraint-based conformation generation:
+```bash
+constrainconf_moldrug [options]
+```
+
+### Configuration Example
+Workflows are defined in YAML format. A typical configuration chains sequential optimization steps, genetic algorithm parameters, docking settings, and desirability functions:
+```yaml
+01_grow:
+  type: GA
+  njobs: 32
+  seed_mol: "CCCO"
+  costfunc: Cost
+  costfunc_kwargs:
+    vina_executable: vina
+    receptor_pdbqt_path: /path/to/receptor.pdbqt
+    boxcenter: [23.56, 8.74, 15.40]
+    boxsize: [22.5, 19.2, 27.4]
+    exhaustiveness: 9
+    desirability:
+      vina_score:
+        SmallerTheBest:
+          Target: -10
+          UpperLimit: -2
+          r: 1
+        w: 1
+  maxiter: 20
+  popsize: 100
+  deffnm: 01_grow
+
+02_local:
+  mutate_crem_kwargs:
+    radius: 3
+    min_size: 0
+    max_size: 1
+    ncores: 128
+  maxiter: 15
+  deffnm: 02_local
+```
+
+### Interactive Dashboard
+`moldrug` includes a Streamlit dashboard for visualizing optimization results, analyzing molecular grids, and exploring ligand-protein interactions using ProLIF:
+```bash
+streamlit run streamlit/moldrug-dashboard.py
+```
+Upload your `.pbz2` result files and protein PDB structures to interactively explore generations, filter by properties, and check molecular novelty against PubChem.
+
+## Documentation & Community
+- 📖 [Full Documentation](https://moldrug.readthedocs.io/en/latest/)
+- 💬 [Discussions](https://github.com/ale94mleon/moldrug/discussions)
+- 🐛 [Issue Tracker](https://github.com/ale94mleon/moldrug/issues)
+- 📜 [Changelog](https://github.com/ale94mleon/moldrug/blob/main/docs/source/CHANGELOG.md)
+
+## Acknowledgments
+This project originated during Ph.D. research at the [Computational Biophysics Group](https://biophys.uni-saarland.de/) at Saarland University, in collaboration with Boehringer Ingelheim. It received funding from the European Union's Marie Skłodowska-Curie Actions (PROTON ITN, Project ID: 860592).
+
+## License
+Distributed under the Apache Software License. See `LICENSE` for details.