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https://github.com/kbhalajiyadav/lipophilicity_model
This project trains a Morgan Fingerprint model to predict lipophilicity.
https://github.com/kbhalajiyadav/lipophilicity_model
maccs-fingerprint mlpregressor morgan-fingerprints nn rdkit rdkit-chem rmse-score sklearn smiles
Last synced: 8 days ago
JSON representation
This project trains a Morgan Fingerprint model to predict lipophilicity.
- Host: GitHub
- URL: https://github.com/kbhalajiyadav/lipophilicity_model
- Owner: kbhalajiyadav
- License: apache-2.0
- Created: 2024-11-08T15:47:13.000Z (10 days ago)
- Default Branch: main
- Last Pushed: 2024-11-08T17:09:41.000Z (10 days ago)
- Last Synced: 2024-11-08T17:23:57.864Z (10 days ago)
- Topics: maccs-fingerprint, mlpregressor, morgan-fingerprints, nn, rdkit, rdkit-chem, rmse-score, sklearn, smiles
- Language: Python
- Homepage:
- Size: 85 KB
- Stars: 0
- Watchers: 1
- Forks: 0
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
- License: LICENSE
Awesome Lists containing this project
README
# Lipophilicity Model with Morgan Fingerprints
This repository contains a Python package and script to train a model predicting lipophilicity based on Morgan fingerprints of molecular SMILES representations. The model uses an MLP regressor and evaluates its performance using Root Mean Squared Error (RMSE) on test data.
## Contents
- **src/train_model.py**: Script for loading data, generating fingerprints, training the model, and saving evaluation results.
- **data/Lipophilicity.csv**: Data file.
- **config.json**: Model hyperparameters specification.
- **environment.yml**: Conda environment file listing dependencies.
- **results.txt**: Output file that stores the model's RMSE, the conda environment used, and key hyperparameters.
## Installation
1. **Clone the repository**:
```bash
git clone https://github.com/kbhalajiyadav/lipophilicity_model.git
cd lipophilicity_model
```
2. **Set up the environment**:
Create a new Conda environment using the `environment.yml` file:
```bash
conda env create -f environment.yml
conda activate molecule_modeling # Replace with your actual environment name
```
### Usage
You can specify model hyperparameters either through a JSON configuration file or by using command-line arguments.
#### Running the Script
To run the model training script with a JSON configuration file:
```bash
python src/train_model.py --config config.json
```
#### Specifying Hyperparameters Directly in the Command Line
If you prefer, you can specify hyperparameters directly on the command line. For example:
```bash
python src/train_model.py --hidden_layer_sizes 100,50 --alpha 0.01
```
#### Using Both JSON and Command-Line Arguments
When both are used, command-line arguments will override values from the JSON file:
```bash
python src/train_model.py --config config.json --alpha 0.01
```
#### Config File Example
Create a JSON configuration file like this in the main directory:
```json
{
"hidden_layer_sizes": [100, 100],
"alpha": 0.001
}
```
### Output
The script will save:
- The RMSE for the test set,
- The name of the active conda environment, and
- Hyperparameter settings
...to `results.txt` in the main directory.
- **Arguments**:
- `--hidden_layer_sizes`: Specifies the architecture of the neural network.
- `--alpha`: Sets the regularization strength of the model.
After execution, the script will output the test set RMSE, current environment, and hyperparameters to `results.txt`.
## License
This project is licensed under the Apache-2.0 License.
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