merged

- conda config --add channels http://conda.binstar.org/omnia

- bash scripts/install_deepchem_conda.sh deepchem

- source activate deepchem

- pip install yapf==0.17.0

- pip install yapf==0.19.0

- pip install coveralls

- python setup.py install

script:

# TODO: Get rid of this when there is a stable release of deepchem.

RUN git clone https://github.com/deepchem/deepchem.git && \

    cd deepchem && \

    git checkout tags/1.3.0 && \

    git checkout tags/1.3.1 && \

    sed -i -- 's/tensorflow$/tensorflow-gpu/g' scripts/install_deepchem_conda.sh && \

    bash scripts/install_deepchem_conda.sh root && \

    python setup.py develop

# mol2vec implementation

In the recent mol2vec [paper](https://chemrxiv.org/articles/Mol2vec_Unsupervised_Machine_Learning_Approach_with_Chemical_Intuition/5513581), authors Jaeger et al consider the features returned by the rdkit Morgan fingerprint as "words" and a compound as a "sentence" to generate fixed-length embeddings. In this case we reproduce 200-element embeddings via a download of all SDF files in the PubChem compound database

## Setup

Ensure that gensim is installed via:

```bash

pip install gensim

```

## Creating training corpus

First, download the pubchem compound SDF corpus via running:

```bash

python ../pubchem_dataset/download_pubchem_ftp.sh

```

Note - the script assumes that a /media/data/pubchem directory exists for this large download (approx 19 GB as of November 2017)

Then generate the embeddings file via:

```bash

./train_mol2vec.sh

```

Then you can use these embeddings as a fixed-length alternative to fingerprints derived directly from RDKit. A full implementation as a featurized for deepchem is WIP

Example code for using the vec.txt file that is created by the above script can be found in eval_mol2vec_results

 No newline at end of file

import gensim

from gensim import models

from rdkit import Chem

from rdkit.Chem import rdMolDescriptors

import numpy as np

def main() :

    model = models.KeyedVectors.load_word2vec_format("vec.txt")

    embeddings = list()

    # Using canonical smiles for glycine, as in original research paper

    mol = Chem.MolFromSmiles("C(C(=O)O)N")

    try:

        info = {}

        rdMolDescriptors.GetMorganFingerprint(mol, 0, bitInfo=info)

        keys = info.keys()

        keys_list = list(keys)

        totalvec = np.zeros(200)

        for k in keys_list:

            wordvec = model.wv[str(k)]

            totalvec = np.add(totalvec, wordvec)

        embeddings.append(totalvec)

    except Exception as e:

        print(e)

        pass

    print(embeddings[0])

import rdkit

from rdkit import Chem

from rdkit.Chem import rdMolDescriptors

import pandas as pd

import gzip

import os

def main() :

    sdf_root_path = "/media/data/pubchem/SDF"

    for path, dirs, filenames in os.walk(sdf_root_path) :

        for filename in filenames:

            filepath = os.path.join(sdf_root_path, filename)

            # This SDF file fails to parse with RDKit on Ubuntu 16.04

            if "Compound_102125001_102150000" in filename:

                continue

            with gzip.open(filepath, 'rb') as myfile:

                suppl = Chem.ForwardSDMolSupplier(myfile)

                for mol in suppl:

                    if not mol:

                        continue

                    try :

                        info = {}

                        rdMolDescriptors.GetMorganFingerprint(mol,1,bitInfo=info)

                        keys = info.keys()

                        keys_list = list(keys)

                        for k in keys_list:

                            print(k,end=' ')

                        print()

                    except Exception:

                        pass

if __name__ == "__main__" :

    main()