https://github.com/ml-jku/autoregressive_activity_prediction
This repo includes code for the autoregressive activity prediction for low-data drug discovery manuscript
https://github.com/ml-jku/autoregressive_activity_prediction
Last synced: 3 months ago
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This repo includes code for the autoregressive activity prediction for low-data drug discovery manuscript
- Host: GitHub
- URL: https://github.com/ml-jku/autoregressive_activity_prediction
- Owner: ml-jku
- License: gpl-3.0
- Created: 2024-04-05T04:06:01.000Z (about 1 year ago)
- Default Branch: main
- Last Pushed: 2024-04-09T09:44:13.000Z (about 1 year ago)
- Last Synced: 2024-04-09T10:47:30.469Z (about 1 year ago)
- Language: Jupyter Notebook
- Size: 3.8 MB
- Stars: 0
- Watchers: 4
- Forks: 0
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
- License: LICENSE
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README
# Autoregressive activity prediction for low-data drug discovery
## 💻 Run the experiments
### Clone repo and download data
```bash
# Clone repo
git clone https://github.com/ml-jku/autoregressive_activity_prediction.git
# Move into dir
cd ./autoregressive_activity_prediction
# Download and unzip assets folder (~600 MB zipped, ~4 GB unzipped)
pip install gdown
gdown https://drive.google.com/uc?id=1ZW1zzNEjrFmhCb4L0z2J2RWBOB9d3pAe
unzip assets.zip
# Download and unzip preprocessed fsmol data (~400 MB zipped, ~5 GB unzipped)
# Move to location at which data should be stored
cd path_to_preprocessed_fsmol_data_dir
gdown https://drive.google.com/uc?id=1SEi8dkkdXudWzRFAYABBckk12tNWfGtX
unzip preprocessed_data
```
### Update paths in config
```yaml
# config location: .src/autoregr_inf_experiment/cfg.py
# Base settings
seed: int = 1234
# Data
data_path: str = "path_to_preprocessed_fsmol_data_dir" #TODO set path
nbr_support_set_candidates: int = 32
inference_batch_size: int = 64
# Experiment
device='gpu'
# Results
results_path: str = "" #TODO set path
...
```
### Conda environment
```bash
# Create conda environment
conda env create -f requirements.yml -n your_env_name
# Activate conda env
conda activate
```
### Update experiment config
Add suitable output paths for the experiment here: ```.src/autoregr_inf_experiment/cfg.py```.
### Run experiment
```bash
# Navigate into directory
cd .src/autoregr_inf_experiment/
# Run autoregressive inference experiment
python experiment_manager.py
# Create results by running the evaluation script
python evaluation.py
```
For different experiment variants see the experiment_manager.py file.