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https://github.com/prokia/BioNavi-NP
Biosynthesis Navigator for Natural Products
https://github.com/prokia/BioNavi-NP
Last synced: about 1 month ago
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Biosynthesis Navigator for Natural Products
- Host: GitHub
- URL: https://github.com/prokia/BioNavi-NP
- Owner: prokia
- Created: 2022-01-14T09:54:01.000Z (almost 3 years ago)
- Default Branch: main
- Last Pushed: 2022-07-04T15:47:55.000Z (over 2 years ago)
- Last Synced: 2024-08-01T13:19:50.701Z (4 months ago)
- Language: Jupyter Notebook
- Size: 5.3 MB
- Stars: 35
- Watchers: 4
- Forks: 14
- Open Issues: 2
-
Metadata Files:
- Readme: README.md
Awesome Lists containing this project
- awesome-biochem-ai - BioNavi-NP (2022) - NP is based on the recent work of [Retro* (ICML 2020)](https://github.com/binghong-ml/retro_star) which is a framework for multi-step pathway predictor based on [A*-algorithm](https://en.wikipedia.org/wiki/A*_search_algorithm). BioNavi-NP concretize Retro* in natural-product (Bio-synthesis) pathways by (1) using standard transformers as 1-step predictor, (2) pretrain from organic chemical pathway then finetune to Bio-synthesis pathways and (3) make a [web-based extension](http://biopathnavi.qmclab.com/) which connects to other systems such as [Selenzyme](http://selenzyme.synbiochem.co.uk/) to predict an enzyme for each reaction in a pathway. (Molecule Retrosynthesis Pathways)
README
#### Introduction
Source code for the paper "Deep Learning Driven Biosynthesis Pathways Navigation for Natural Products with BioNavi-NP" in *[Nature Communications](https://www.nature.com/articles/s41467-022-30970-9)*. The server can be found [here](http://biopathnavi.qmclab.com/).
#### Setup
BioNavi-NP requires anaconda with python 3.7 or later, cudatoolkit=11.1.
You will wish to install BioNavi-NP in a virtual environment to prevent conflicting dependencies.
```python
sudo apt-get install graphviz # for output visualization
conda create -n biosynthesis python==3.7
conda activate biosynthesis
cd multistep
sh install.sh
```
BioNavi-NP has been tested on Ubuntu 18.04, with four GPUs (Nvidia RTX3090). Installation should take no longer than 20 minutes on a modern server.
#### Planning
Demo code for multi-step planning has been placed in```run_biosynthesis.py```.
Run
```python
python run_biosynthesis.py
```
This demo now will take only 20 seconds with single GPU (Nvidia RTX3090). One need to first download the [checkpoints](https://drive.google.com/file/d/17DWd5K9vKUV3ynTbUA1yt_7YO0Xpts7c/view?usp=sharing) and unzip under ```biosynthesis/singlestep/checkpoints/``` to run the demo. The output will be visulized in ```viz_dir```.
The key functions are shown in the following block, You may run your own molecule by changing the 'target_mol' in ```main_biosynthesis()```.
```python
def main_biosynthesis():
input_dict = {
'target_mol': 'N[C@@H](CNC(=O)C(=O)O)C(=O)O', # input molecule
'expansion_topk': 50,
'max_depth': 10,
'expansion_iters': 10,
'route_topk': 5,
#'building_blocks': ['N[C@@H](CO)C(=O)O'] # one can assign a specific building block with this command
'building_blocks':'retro_star/dataset/bio_data/bio_building_blocks_all/building_block.csv'
}
res = run(input_dict)
print(res)
def run(input_dict):
os.environ['CUDA_VISIBLE_DEVICES'] = '0, 1, 2, 3'
# canonicalization
mol = Chem.MolToSmiles(Chem.MolFromSmarts(input_dict['target_mol']))
# the trained checkpoints path, one can simply reduce the checkpotins to accelerate the prediction.
one_step_model_path = [
'../singlestep/checkpoints/np-like/model_step_30000.pt',
'../singlestep/checkpoints/np-like/model_step_50000.pt',
'../singlestep/checkpoints/np-like/model_step_80000.pt',
'../singlestep/checkpoints/np-like/model_step_100000.pt'
]
value_fn_model_path = './retro_star/saved_models/best_epoch_final_4.pt'
viz_dir = os.path.join('viz_' + str(int(time.time()))) # the output path with visualization
ret_file_path = os.path.join('./viz/tmp/', viz_dir + '.zip')
planner = RSPlanner(
gpu=get_avai_gpu(),
use_value_fn=True,
value_fn_model_path=value_fn_model_path,
fp_dim=2048,
iterations=input_dict['expansion_iters'],
expansion_topk=input_dict['expansion_topk'],
route_topk=input_dict['route_topk'],
one_step_model_type='onmt',
buliding_block_path=input_dict['building_blocks'],
mlp_templates_path=None,
one_step_model_path=one_step_model_path,
beam_size=20,
viz=True,
viz_dir=viz_dir
)
result = planner.plan(mol)
return result
```
#### Testing on the test set
Our work on BioNavi-NP features an evaluation on our test-set. We automated this evaluation by
```python
python run_batch_by_building_blocks.py
```
The whole process takes around 17 hours on four GPU (Nvidia RTX3090). One could modify the data_path and building_block_path in the script according to their own installation path.
#### Citation
Please cite the following paper if you use this code in your work.
Zheng, S., Zeng, T., Li, C. et al. Deep learning driven biosynthetic pathways navigation for natural products with BioNavi-NP. Nat Commun 13, 3342 (2022). https://doi.org/10.1038/s41467-022-30970-9