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https://github.com/paccmann/paccmann_sarscov2
Code for paper on automation of discovery and synthesis of targeted molecules: https://iopscience.iop.org/article/10.1088/2632-2153/abe808
https://github.com/paccmann/paccmann_sarscov2
accelerated-discovery computer-aided-synthesis-planning de-novo-drug-design deep-learning drug-discovery proteochemometrics sars-cov-2
Last synced: 15 days ago
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Code for paper on automation of discovery and synthesis of targeted molecules: https://iopscience.iop.org/article/10.1088/2632-2153/abe808
- Host: GitHub
- URL: https://github.com/paccmann/paccmann_sarscov2
- Owner: PaccMann
- License: mit
- Created: 2020-09-14T13:15:38.000Z (over 4 years ago)
- Default Branch: master
- Last Pushed: 2021-09-18T00:06:30.000Z (over 3 years ago)
- Last Synced: 2023-03-03T05:46:44.432Z (almost 2 years ago)
- Topics: accelerated-discovery, computer-aided-synthesis-planning, de-novo-drug-design, deep-learning, drug-discovery, proteochemometrics, sars-cov-2
- Homepage:
- Size: 1.04 MB
- Stars: 14
- Watchers: 8
- Forks: 6
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
- License: LICENSE
Awesome Lists containing this project
README
[](https://github.com/PaccMann/paccmann_sarscov2/actions/workflows/build.yml)
# paccmann_sarscov2
Pipeline to reproduce the results of the paper [Data-driven molecular design for discovery and synthesis of novel ligands: a case study on SARS-CoV-2](https://iopscience.iop.org/article/10.1088/2632-2153/abe808) (_Machine Learning: Science and Technology_, 2021). In that paper, we propose a de-novo molecular generative model for protein driven molecular design and bundle it with molecular retrosynthesis models to automatize all steps before the actual synthesis of a drug candidate.
## Description
In the repo we provide a conda environment and instructions to reproduce the pipeline described in the manuscript:
1. Train a multimodal protein-compound interaction classifier, also known as the affinity predictor ([source code](https://github.com/PaccMann/paccmann_predictor))
2. Train a toxicity predictor ([source code](https://github.com/PaccMann/toxsmi))
3. Train a generative model for encoded proteins, also known as the ProteinVAE ([source code](https://github.com/PaccMann/paccmann_omics))
4. Train a generative model for molecules, also known as the SELFIESVAE ([source code](https://github.com/PaccMann/paccmann_chemistry))
5. Train PaccMann^RL on SARS-CoV-2 using the pretained models from above ([source code](https://github.com/PaccMann/paccmann_generator))
**NOTE:** In the linked repositories, there are often multiple examples for training. For the use case of `paccmann_sarscov2`, relevant examples are named `affinity` or `encoded_proteins`.
## Requirements
- `conda>=3.7`
- The following data from this [Box link](https://ibm.ent.box.com/v/paccmann-sarscov2-data).
View the respective `README.md` files on data sources.
- The git repos linked in the [previous section](#description)
## Setup
### Install the environment
Create a conda environment:
```sh
conda env create -f conda.yml
```
Activate the environment:
```sh
conda activate paccmann_sarscov2
```
**NOTE:** On Ubuntu, you may now need to run the following to obtain a functional `RDKit` distribution:
```sh
sudo apt-get install libxrender1
```
### Download data and pretrained models
Download the [data](https://ibm.ent.box.com/v/paccmann-sarscov2-data) as reported in the [requirements section](#requirements).
From now on, we will assume that they are stored in the root of the repository in a folder called `data`, following this structure:
```console
data
├── pretraining
│ ├── ProteinVAE
│ ├── SELFIESVAE
│ ├── affinity_predictor
│ ├── language_models
│ └── toxicity_predictor
└── training
```
This is around **6GB** of data, required for pretaining multiple models.
Also, the workload required to run the full pipeline is intensive and might not be straightforward to run all the steps on a desktop laptop.
For these reasons we also provide [pretrained models](https://ibm.ent.box.com/v/paccmann-sarscov2-models) (ca. 700MB) for download.
Once the download of the pretrained models is completed, the directory structure looks like this:
```console
models
├── ProteinVAE
├── SELFIESVAE
├── Tox21
└── affinity
```
**NOTE:** no worries, the `data` and `models` folders are in the [.gitignore](./.gitignore).
## PaccMann^RL on SARS-CoV-2
Using the pretrained models to train the conditional generator you would only require the data under `data/training/` (8MB).
### Clone the repo
To get the training script simply type this:
```sh
mkdir code && cd code && \
git clone --branch sarscov2 https://github.com/PaccMann/paccmann_generator && \
cd ..
```
The branch is given to ensure a version working with the provided conda environment.
**NOTE:** no worries, the `code` folder is in the [.gitignore](./.gitignore).
### Running training
Running the training is as easy as running:
``` console
(paccmann_sarscov2) $ python ./code/paccmann_generator/examples/affinity/train_conditional_generator.py \
./models/SELFIESVAE \
./models/ProteinVAE \
./models/affinity \
./data/training/merged_sequence_encoding/uniprot_covid-19.csv \
./code/paccmann_generator/examples/affinity/conditional_generator.json \
paccmann_sarscov2 \
35 \
./data/training/unbiased_predictions \
--tox21_path ./models/Tox21
```
This will create a `biased_models` folder containing the conditional generators, biased for all provided proteins from [covid-19.uniprot.org](https://covid-19.uniprot.org/) except one, in the example for ACE2_HUMAN. The biased generator generates compounds with a shifted distribution compared to unbiased predictions. Ideally, the model generalizes to ACE2_HUMAN and the biased compounds have overall higher affinity (to ACE2_HUMAN) **according to the affinity predictor**. See the pdf files in `biased_models/paccmann_sarscov2_35/results` to observe the effect at different stages of training.
**NOTE:** no worries, the `biased_models` folder is in the [.gitignore](./.gitignore).
## Pretraining pipeline
We also provide instructions and scripts to reproduce the full pretraining pipeline, keep in mind **we discourage you from running this on a desktop laptop**.
Calling any of the scripts with the `-h` or `--help` flag will provide you with some information on the arguments.
**NOTE:** in the following, we assume a folder `models` has been created in the root of the repository.
### Clone the repos
To get the scripts to run each of the component create a `code` folder and clone the repos. Simply type this:
```sh
mkdir code && cd code && \
git clone --branch sarscov2 https://github.com/PaccMann/paccmann_predictor && \
git clone --branch 0.0.2 https://github.com/PaccMann/toxsmi && \
git clone --branch sarscov2 https://github.com/PaccMann/paccmann_omics && \
git clone --branch sarscov2 https://github.com/PaccMann/paccmann_chemistry && \
git clone --branch sarscov2 https://github.com/PaccMann/paccmann_generator && \
cd ..
```
The branch is given to ensure a version working with the provided conda environment.
### affinity predictor
```console
(paccmann_sarscov2) $ python ./code/paccmann_predictor/examples/affinity/train_affinity.py \
./data/pretraining/affinity_predictor/filtered_train_binding_data.csv \
./data/pretraining/affinity_predictor/filtered_val_binding_data.csv \
./data/pretraining/affinity_predictor/sequences.smi \
./data/pretraining/affinity_predictor/filtered_ligands.smi \
./data/pretraining/language_models/smiles_language_chembl_gdsc_ccle_tox21_zinc_organdb_bindingdb.pkl \
./data/pretraining/language_models/protein_language_bindingdb.pkl \
./models/ \
./code/paccmann_predictor/examples/affinity/affinity.json \
affinity
```
### toxicity predictor
```console
(paccmann_sarscov2) $ python ./code/toxsmi/scripts/train_tox.py \
./data/pretraining/toxicity_predictor/tox21_train.csv \
./data/pretraining/toxicity_predictor/tox21_test.csv \
./data/pretraining/toxicity_predictor/tox21.smi \
./data/pretraining/language_models/smiles_language_tox21.pkl \
./models/ \
./code/toxsmi/params/mca.json \
Tox21 \
--embedding_path ./data/pretraining/toxicity_predictor/smiles_vae_embeddings.pkl
```
### protein VAE
``` console
(paccmann_sarscov2) $ python ./code/paccmann_omics/examples/encoded_proteins/train_protein_encoding_vae.py \
./data/pretraining/proteinVAE/tape_encoded/train_representation.csv \
./data/pretraining/proteinVAE/tape_encoded/val_representation.csv \
./models/ \
./code/paccmann_omics/examples/encoded_proteins/protein_encoding_vae_params.json \
ProteinVAE
```
### SELFIES VAE
``` console
(paccmann_sarscov2) $ python ./code/paccmann_chemistry/examples/train_vae.py \
./data/pretraining/SELFIESVAE/train_chembl_22_clean_1576904_sorted_std_final.smi \
./data/pretraining/SELFIESVAE/test_chembl_22_clean_1576904_sorted_std_final.smi \
./data/pretraining/language_models/selfies_language.pkl \
./models/ \
./code/paccmann_chemistry/examples/example_params.json \
SELFIESVAE
```
## References
If you use `paccmann_sarscov2` in your projects, please cite the following:
```bib
@article{born2021datadriven,
author = {Born, Jannis and Manica, Matteo and Cadow, Joris and Markert, Greta and Mill, Nil Adell and Filipavicius, Modestas and Janakarajan, Nikita and Cardinale, Antonio and Laino, Teodoro and {Rodr{\'{i}}guez Mart{\'{i}}nez}, Mar{\'{i}}a},
doi = {10.1088/2632-2153/abe808},
issn = {2632-2153},
journal = {Machine Learning: Science and Technology},
number = {2},
pages = {025024},
title = {{Data-driven molecular design for discovery and synthesis of novel ligands: a case study on SARS-CoV-2}},
url = {https://iopscience.iop.org/article/10.1088/2632-2153/abe808},
volume = {2},
year = {2021}
}
```