clipzyme 0.0.4

Reaction-Conditioned Virtual Screening of Enzymes

CLIPZyme

Reaction-Conditioned Virtual Screening of Enzymes

Table of contents

• Installation
• Screening with CLIPZyme
  • Using CLIPZyme's screening set
  • Using your own screening set
• Reproducing published results
  • Data processing
  • Training and evaluation
• Citation

Installation:

conda create -n clipzyme python=3.10
conda activate clipzyme
python -m pip install rdkit
python -m pip install numpy==1.26.0 pandas==2.1.1 scikit-image==0.19.1 scikit-learn==1.3.2 scipy==1.11.2 tqdm==4.62.3 GitPython==3.1.27 comet-ml==3.28.1 wandb==0.12.19
python -m pip install torch==2.0.1+cu118 torchvision==0.15.2+cu118 --extra-index-url https://download.pytorch.org/whl/cu118
python -m pip install pytorch-lightning==2.0.9 torchmetrics==0.11.4
python -m pip install torch_geometric==2.3.1
python -m pip install pyg_lib torch_scatter torch_sparse torch_cluster torch_spline_conv -f https://data.pyg.org/whl/torch-2.0.1+cu118.html
python -m pip install wget bioservices==1.9.0 pubchempy==1.0.4 openpyxl==3.0.10 transformers==4.25.1 rxn-chem-utils==1.0.4 rxn-utils==1.1.3
python -m pip install biopython p_tqdm einops ninja easydict pyyaml
python -m pip install imageio==2.24.0 ipdb pdbpp networkx==2.8.7 overrides pygsp pyemd moviepy 
python -m pip install molvs==0.1.1 epam.indigo==1.9.0 fair-esm==2.0.0 

Screening with CLIPZyme

Using CLIPZyme's screening set

Using your own screening set

1. In python shell or jupyter notebook (slow)

2. Batched (faster)

---

Reproducing published results

Data processing

We obtain the data from the following sources:

• EnzymeMap: Heid et al. Enzymemap: Curation, validation and data-driven prediction of enzymatic reactions. 2023.
• Terpene Synthases: Samusevich et al. Discovery and characterization of terpene synthases powered by machine learning. 2024.

Our processed data is available at here. It consists of the following files:

• enzymemap.json: contains the EnzymeMap dataset.
• terpene_synthases.json: contains the Terpene Synthases dataset.
• enzymemap_screening.p: contains the screening set.
• sequenceid2sequence.p: contains the mapping form sequence ID to amino acids.

Training and evaluation

1. To train the models presented in the tables below, run the following command:

   python scripts/dispatcher -c {config_path} -l {log_path}
   

   • {config_path} is the path to the config file in the table below
   • {log_path} is the path in which to save the log file.

   For example, to run the first row in Table 1, run:

   python scripts/dispatcher -c configs/train/clip_egnn.json -l ./logs/
   

2. Once you've trained the model, run the eval config to evaluate the model on the test set. For example, to evaluate the first row in Table 1, run:

   python scripts/dispatcher -c configs/eval/clip_egnn.json -l ./logs/
   

3. We perform all analysis in the jupyter notebook included CLIPZyme_CLEAN.ipynb. We first calculate the hidden representations of the screening using the eval configs above and collect them into one matrix (saved as a pickle file). These are loaded into the jupyter notebook as well as the test set. All tables are then generated in the notebook.

Citation

@article{mikhael2024clipzyme,
  title={CLIPZyme: Reaction-Conditioned Virtual Screening of Enzymes},
  author={Mikhael, Peter G and Chinn, Itamar and Barzilay, Regina},
  journal={arXiv preprint arXiv:2402.06748},
  year={2024}
}