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https://github.com/molecularsets/moses

Molecular Sets (MOSES): A Benchmarking Platform for Molecular Generation Models
https://github.com/molecularsets/moses

benchmark drug-discovery generative-models machine-learning

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Molecular Sets (MOSES): A Benchmarking Platform for Molecular Generation Models

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# Molecular Sets (MOSES): A benchmarking platform for molecular generation models



[![Build Status](https://travis-ci.com/molecularsets/moses.svg?branch=master)](https://travis-ci.com/molecularsets/moses) [![PyPI version](https://badge.fury.io/py/molsets.svg)](https://badge.fury.io/py/molsets)



Deep generative models are rapidly becoming popular for the discovery of new molecules and materials. Such models learn on a large collection of molecular structures and produce novel compounds. In this work, we introduce Molecular Sets (MOSES), a benchmarking platform to support research on machine learning for drug discovery. MOSES implements several popular molecular generation models and provides a set of metrics to evaluate the quality and diversity of generated molecules. With MOSES, we aim to standardize the research on molecular generation and facilitate the sharing and comparison of new models.



__For more details, please refer to the [paper](https://arxiv.org/abs/1811.12823).__



If you are using MOSES in your research paper, please cite us as

```

@article{10.3389/fphar.2020.565644,

  title={{M}olecular {S}ets ({MOSES}): {A} {B}enchmarking {P}latform for {M}olecular {G}eneration {M}odels},

  author={Polykovskiy, Daniil and Zhebrak, Alexander and Sanchez-Lengeling, Benjamin and Golovanov, Sergey and Tatanov, Oktai and Belyaev, Stanislav and Kurbanov, Rauf and Artamonov, Aleksey and Aladinskiy, Vladimir and Veselov, Mark and Kadurin, Artur and Johansson, Simon and  Chen, Hongming and Nikolenko, Sergey and Aspuru-Guzik, Alan and Zhavoronkov, Alex},

  journal={Frontiers in Pharmacology},

  year={2020}

}

```



![pipeline](images/pipeline.png)



## Dataset



We propose [a benchmarking dataset](https://media.githubusercontent.com/media/molecularsets/moses/master/data/dataset_v1.csv) refined from the ZINC database.



The set is based on the ZINC Clean Leads collection. It contains 4,591,276 molecules in total, filtered by molecular weight in the range from 250 to 350 Daltons, a number of rotatable bonds not greater than 7, and XlogP less than or equal to 3.5. We removed molecules containing charged atoms or atoms besides C, N, S, O, F, Cl, Br, H or cycles longer than 8 atoms. The molecules were filtered via medicinal chemistry filters (MCFs) and PAINS filters.



The dataset contains 1,936,962 molecular structures. For experiments, we split the dataset into a training, test and scaffold test sets containing around 1.6M, 176k, and 176k molecules respectively. The scaffold test set contains unique Bemis-Murcko scaffolds that were not present in the training and test sets. We use this set to assess how well the model can generate previously unobserved scaffolds.



## Models



* [Character-level Recurrent Neural Network (CharRNN)](./moses/char_rnn/README.md)

* [Variational Autoencoder (VAE)](./moses/vae/README.md)

* [Adversarial Autoencoder (AAE)](./moses/aae/README.md)

* [Junction Tree Variational Autoencoder (JTN-VAE)](https://github.com/wengong-jin/icml18-jtnn/tree/master/fast_molvae)

* [Latent Generative Adversarial Network (LatentGAN)](./moses/latentgan/README.md)



## Metrics

Besides standard uniqueness and validity metrics, MOSES provides other metrics to access the overall quality of generated molecules. Fragment similarity (Frag) and Scaffold similarity (Scaff) are cosine distances between vectors of fragment or scaffold frequencies correspondingly of the generated and test sets. Nearest neighbor similarity (SNN) is the average similarity of generated molecules to the nearest molecule from the test set. Internal diversity (IntDiv) is an average pairwise similarity of generated molecules. Fréchet ChemNet Distance (FCD) measures the difference in distributions of last layer activations of ChemNet. Novelty is a fraction of unique valid generated molecules not present in the training set.



  

    

      Model

      Valid (↑)

      Unique@1k (↑)

      Unique@10k (↑)

      FCD (↓)

      SNN (↑)

      Frag (↑)

      Scaf (↑)

      IntDiv (↑)

      IntDiv2 (↑)

      Filters (↑)

      Novelty (↑)

    

    

      Test

      TestSF

      Test

      TestSF

      Test

      TestSF

      Test

      TestSF

    

  

  

    

      Train

      1.0

      1.0

      1.0

      0.008

      0.4755

      0.6419

      0.5859

      1.0

      0.9986

      0.9907

      0.0

      0.8567

      0.8508

      1.0

      1.0

    

    

      HMM

      0.076±0.0322

      0.623±0.1224

      0.5671±0.1424

      24.4661±2.5251

      25.4312±2.5599

      0.3876±0.0107

      0.3795±0.0107

      0.5754±0.1224

      0.5681±0.1218

      0.2065±0.0481

      0.049±0.018

      0.8466±0.0403

      0.8104±0.0507

      0.9024±0.0489

      0.9994±0.001

    

    

      NGram

      0.2376±0.0025

      0.974±0.0108

      0.9217±0.0019

      5.5069±0.1027

      6.2306±0.0966

      0.5209±0.001

      0.4997±0.0005

      0.9846±0.0012

      0.9815±0.0012

      0.5302±0.0163

      0.0977±0.0142

      0.8738±0.0002

      0.8644±0.0002

      0.9582±0.001

      0.9694±0.001

    

    

      Combinatorial

      1.0±0.0

      0.9983±0.0015

      0.9909±0.0009

      4.2375±0.037

      4.5113±0.0274

      0.4514±0.0003

      0.4388±0.0002

      0.9912±0.0004

      0.9904±0.0003

      0.4445±0.0056

      0.0865±0.0027

      0.8732±0.0002

      0.8666±0.0002

      0.9557±0.0018

      0.9878±0.0008

    

    

      CharRNN

      0.9748±0.0264

      1.0±0.0

      0.9994±0.0003

      0.0732±0.0247

      0.5204±0.0379

      0.6015±0.0206

      0.5649±0.0142

      0.9998±0.0002

      0.9983±0.0003

      0.9242±0.0058

      0.1101±0.0081

      0.8562±0.0005

      0.8503±0.0005

      0.9943±0.0034

      0.8419±0.0509

    

    

      AAE

      0.9368±0.0341

      1.0±0.0

      0.9973±0.002

      0.5555±0.2033

      1.0572±0.2375

      0.6081±0.0043

      0.5677±0.0045

      0.991±0.0051

      0.9905±0.0039

      0.9022±0.0375

      0.0789±0.009

      0.8557±0.0031

      0.8499±0.003

      0.996±0.0006

      0.7931±0.0285

    

    

      VAE

      0.9767±0.0012

      1.0±0.0

      0.9984±0.0005

      0.099±0.0125

      0.567±0.0338

      0.6257±0.0005

      0.5783±0.0008

      0.9994±0.0001

      0.9984±0.0003

      0.9386±0.0021

      0.0588±0.0095

      0.8558±0.0004

      0.8498±0.0004

      0.997±0.0002

      0.6949±0.0069

    

    

      JTN-VAE

      1.0±0.0

      1.0±0.0

      0.9996±0.0003

      0.3954±0.0234

      0.9382±0.0531

      0.5477±0.0076

      0.5194±0.007

      0.9965±0.0003

      0.9947±0.0002

      0.8964±0.0039

      0.1009±0.0105

      0.8551±0.0034

      0.8493±0.0035

      0.976±0.0016

      0.9143±0.0058

    

    

      LatentGAN

      0.8966±0.0029

      1.0±0.0

      0.9968±0.0002

      0.2968±0.0087

      0.8281±0.0117

      0.5371±0.0004

      0.5132±0.0002

      0.9986±0.0004

      0.9972±0.0007

      0.8867±0.0009

      0.1072±0.0098

      0.8565±0.0007

      0.8505±0.0006

      0.9735±0.0006

      0.9498±0.0006

    

  



For comparison of molecular properties, we computed the Wasserstein-1 distance between distributions of molecules in the generated and test sets. Below, we provide plots for lipophilicity (logP), Synthetic Accessibility (SA), Quantitative Estimation of Drug-likeness (QED) and molecular weight.



|logP|SA|

|----|--|

|![logP](images/logP.png)|![SA](images/SA.png)|

|weight|QED|

|![weight](images/weight.png)|![QED](images/QED.png)|



# Installation



### PyPi

The simplest way to install MOSES (models and metrics) is to install [RDKit](https://www.rdkit.org/docs/Install.html): `conda install -yq -c rdkit rdkit` and then install MOSES (`molsets`) from pip (`pip install molsets`). If you want to use LatentGAN, you should also install additional dependencies using `bash install_latentgan_dependencies.sh`.



If you are using Ubuntu, you should also install `sudo apt-get install libxrender1 libxext6` for RDKit.



### Docker



1. Install [docker](https://docs.docker.com/install/) and [nvidia-docker](https://github.com/nvidia/nvidia-docker/wiki/Installation-(version-2.0)).



2. Pull an existing image (4.1Gb to download) from DockerHub:



```bash

docker pull molecularsets/moses

```



or clone the repository and build it manually:



```bash

git clone https://github.com/molecularsets/moses.git

nvidia-docker image build --tag molecularsets/moses moses/

```



3. Create a container:

```bash

nvidia-docker run -it --name moses --network="host" --shm-size 10G molecularsets/moses

```



4. The dataset and source code are available inside the docker container at /moses:

```bash

docker exec -it molecularsets/moses bash

```



### Manually

Alternatively, install dependencies and MOSES manually.



1. Clone the repository:

```bash

git lfs install

git clone https://github.com/molecularsets/moses.git

```



2. [Install RDKit](https://www.rdkit.org/docs/Install.html) for metrics calculation.



3. Install MOSES:

```bash

python setup.py install

```



4. (Optional) Install dependencies for LatentGAN:

```bash

bash install_latentgan_dependencies.sh

```



# Benchmarking your models



* Install MOSES as described in the previous section.



* Get `train`, `test` and `test_scaffolds` datasets using the following code:



```python

import moses



train = moses.get_dataset('train')

test = moses.get_dataset('test')

test_scaffolds = moses.get_dataset('test_scaffolds')

```



* You can use a standard torch [DataLoader](https://pytorch.org/docs/stable/data.html#torch.utils.data.DataLoader) in your models. We provide a simple `StringDataset` class for convenience:



```python

from torch.utils.data import DataLoader

from moses import CharVocab, StringDataset



train = moses.get_dataset('train')

vocab = CharVocab.from_data(train)

train_dataset = StringDataset(vocab, train)

train_dataloader = DataLoader(

    train_dataset, batch_size=512,

    shuffle=True, collate_fn=train_dataset.default_collate

)



for with_bos, with_eos, lengths in train_dataloader:

    ...

```



* Calculate metrics from your model's samples. We recomend sampling at least `30,000` molecules:



```python

import moses

metrics = moses.get_all_metrics(list_of_generated_smiles)

```



* Add generated samples and metrics to your repository. Run the experiment multiple times to estimate the variance of the metrics.



# Reproducing the baselines



### End-to-End launch



You can run pretty much everything with:

```bash

python scripts/run.py

```

This will **split** the dataset, **train** the models, **generate** new molecules, and **calculate** the metrics. Evaluation results will be saved in `metrics.csv`.



You can specify the GPU device index as `cuda:n` (or `cpu` for CPU) and/or model by running:

```bash

python scripts/run.py --device cuda:1 --model aae

```



For more details run `python scripts/run.py --help`.



You can reproduce evaluation of all models with several seeds by running:

```bash

sh scripts/run_all_models.sh

```



### Training



```bash

python scripts/train.py  \

       --train_load  \

       --model_save  \

       --config_save  \

       --vocab_save 

```



To get a list of supported models run `python scripts/train.py --help`.



For more details of certain model run `python scripts/train.py  --help`.



### Generation



```bash

python scripts/sample.py  \

       --model_load  \

       --vocab_load  \

       --config_load  \

       --n_samples  \

       --gen_save 

```



To get a list of supported models run `python scripts/sample.py --help`.



For more details of certain model run `python scripts/sample.py  --help`.



### Evaluation



```bash

python scripts/eval.py \

       --ref_path  \

       --gen_path 

```



For more details run `python scripts/eval.py --help`.