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https://github.com/luost26/chemprojector
:dart: Projecting Molecules into Synthesizable Chemical Spaces (ICML 2024)
https://github.com/luost26/chemprojector
Last synced: 24 days ago
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:dart: Projecting Molecules into Synthesizable Chemical Spaces (ICML 2024)
- Host: GitHub
- URL: https://github.com/luost26/chemprojector
- Owner: luost26
- License: mit
- Created: 2024-05-10T08:58:53.000Z (6 months ago)
- Default Branch: main
- Last Pushed: 2024-10-22T15:30:11.000Z (29 days ago)
- Last Synced: 2024-10-23T05:13:43.362Z (28 days ago)
- Language: Python
- Homepage:
- Size: 4.22 MB
- Stars: 26
- Watchers: 2
- Forks: 0
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
- License: LICENSE
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README
# ChemProjector
:dart: Projecting Molecules into Synthesizable Chemical Spaces (ICML 2024)
[[Paper]](https://arxiv.org/abs/2406.04628)
## Install
### Clone Repo
Please clone the repository with the `--recurse-submodules` flag to *include the third-party submodules*.
```bash
git clone --recurse-submodules https://github.com/luost26/ChemProjector.git
```
### Environment
```bash
# Install conda environment
conda env create -f env.yml -n chemprojector
conda activate chemprojector
# Install ChemProjector package
pip install -e .
```
The default CUDA version is 11.8. If you have to use a different version, please modify the `env.yml` file accordingly.
### Building Block Data
We provide preprocessed building block data. You can download it from [here](https://drive.google.com/file/d/1scui0RZ8oeroDAafnw4jgTi3yKtXxXpe/view?usp=drive_link) and put it in the `data` directory.
However, the data is derived from Enamine's building block catalog, which are **available only upon request**.
Therefore, you should first request the data from Enamine [here](https://enamine.net/building-blocks/building-blocks-catalog) and download the US Stock catalog into the `data` directory.
Then run the following script which will check whether you have a copy of the Enamine's catalog and unarchive the preprocessed data for you:
```bash
python unarchive_wizard.py
```
You may also process the building block data by yourself. Please refer to the `scripts/preprocess_data` directory for more details.
### Trained Weights
You can download the trained weights from [here](https://drive.google.com/drive/folders/1T9f9MsEAsAjPV8GR0pXimHKCvq97SIzs?usp=drive_link) and put them in the `data/trained_weights` directory.
## Usage
### Project Molecules
You can create a list of SMILES strings in CSV format (example: `data/example.csv`) and run the following command to project them into the synthesizable chemical space.
```bash
python sample.py \
--model-path data/trained_weights/original_default.ckpt \
--input data/example.csv \
--output results/example.csv
```
### Model Evaluation
#### Bottom-Up Synthesis Planning
Using the test split:
```bash
./scripts/synthesis_planning_test_split.sh
```
or using the ChEMBL dataset:
```bash
./scripts/synthesis_planning_chembl.sh
```
#### Structure-Based Drug Design
Please refer to the `scripts/sbdd` directory for details.
#### Goal-Directed Generation
Please refer to the `scripts/goal_directed` directory for details.
### Train
```bash
python train.py ./configs/original_default.yml
```
## Reference
```bibtex
@inproceedings{luo2024chemprojector,
title={Projecting Molecules into Synthesizable Chemical Spaces},
author={Shitong Luo and Wenhao Gao and Zuofan Wu and Jian Peng and Connor W. Coley and Jianzhu Ma},
booktitle={Forty-first International Conference on Machine Learning},
year={2024}
}
```