# Inter-Hammett: Enhancing Interpretability in Hammett’s Constant Prediction via Extracting Rules # Reference Implementation of Inter-POL algorithmThis readme file documents all of the required steps to run Inter-POL. Note that the code was implemented and tested on a Linux operating system only. ## How to set up the environmentWe have provided an Anaconda environment file for easy setup.If you do not have Anaconda installed, you can get Miniconda from [here](https://docs.conda.io/en/latest/miniconda.html).Create the `inter_hammet` environment using the following command:```bashconda env create -n inter_hammet -f environment.ymlconda activate inter_hammet``` # In order to install the required packages```bashpip install -r requirements.txt``` # Step by step files: 1_Generate_features.py: prepare features 2_Inter-hammet_train_and_evaluate.py: train and test the model # Data:The DATA taken from "Leveraging graph neural networks to predict Hammett’s constants for benzoic acid derivatives"https://www.sciencedirect.com/science/article/pii/S294974772400037X https://github.com/v-saini/hammet-gnn # Example Preparation of SMILES:It includes Python scripts that allow users to convert between SMILES and common 3D molecular file formats using either **Open Babel** or **RDKit**. These tools are particularly useful for chemists preparing input data or interpreting output in a machine learning pipeline such as Inter-Hammett. ## Obabel:💡 In order to install obabel:```bashsudo apt install openbabelpip install openbabel-wrapper```### 🔁 Convert SMILES → 3D structure (.mol, .mol2, .pdb) ```bashpython3 convert_smiles_to_mol2_with_openbabel.py "CC(=O)OC1=CC=CC=C1C(=O)O" output.mol2```### 🔁 Convert 3D structure (.mol, .mol2, .pdb) → SMILES```bashpython3 convert_3D_structure_to_smiles_openbabel.py input.mol2```It is available online as well: https://www.cheminfo.org/Chemistry/Cheminformatics/FormatConverter/index.html ## RDkit:⚠️ RDKit does not natively support .mol2 export unless patched or extended. 💡 Install RDKit (if not already installed):```bashconda install -c conda-forge rdkit```### 🔁 Convert SMILES → 3D structure (.mol, .mol2, .pdb) ```bashpython3 Convert_SMILES_to_3D_structure.py "CC(=O)OC1=CC=CC=C1C(=O)O" output.mol```### 🔁 Convert 3D structure (.mol, .mol2, .pdb) → SMILES```bashpython3 Convert_3D_structures_into_SMILES.py input.pdb``` ## License This project is licensed for **academic and research purposes only**. For commercial usage, please connect with s.yavuz.ugurlu@gmail.com # References:[1] Saini, Vaneet, and Ranjeet Kumar. "Leveraging graph neural networks to predict Hammett’s constants for benzoic acid derivatives." Artificial Intelligence Chemistry 2.2 (2024): 100079.