About

Phytebyte is an extensible software framework used to train machine learning models to identify bioactive compounds found in food. The use case captured in 'run.py' is training models using known drug compounds that target a specific gene, and identifying plant compounds in FooDB that may behave similarly.

Install

1. Download openbabel https://sourceforge.net/projects/openbabel/files/openbabel/2.4.1/openbabel-2.4.1.tar.gz/download
2. Install openbabel

tar zxf openbabel-2.4.1.tar.gz
mkdir build
cd build
cmake ../openbabel-2.4.1 -DPYTHON_BINDINGS=ON -DPYTHON_EXECUTABLE=/usr/local/bin/python3 -DRUN_SWIG=ON
make -j4
make install

3. Create virtual environment & install dependencies

  # In phytebyte root directory
  python3 -m venv env
  pip install -r requirements.txt

4. Install pybel (Python wrapper for openbabel)

  pip install openbabel

5. Download the 'FooDB SQL file' from http://foodb.ca/downloads
6. Unzip the file

gunzip foodb_2017_06_29.sql.gz

6. Install mysql

brew install mysql
brew tap homebrew/services
brew services start mysql
# Change local admin password to whatever you'd like.
mysqladmin -u root password 'phytebyte'
# Verify that you can connect
mysql -uroot -pphytebyte
# Create the foodb Database
CREATE DATABASE foodb;

7. Load foodb Database

cat foodb_2017_06_29.sql | mysql -uroot -pphytebyte foodb

8. Install postgresql

brew install postgres
brew services start postgres
# Verify you can connect
psql  # Should open `psql>` prompt
# Create the 'chembl' database
CREATE DATABASE chembl_25;

8. Download & decompress ChEMBL Database (for postgresql)

curl -O http://ftp.ebi.ac.uk/pub/databases/chembl/ChEMBLdb/releases/chembl_25/chembl_25_postgresql.tar.gz
gunzip chembl_25_postgresql.tar.gz
tar xopf chembl_25_postgresql.tar
cd chembl_25/chembl_25_postgresql
pg_restore -d chembl_25 chembl_25_postgresql.dmp

9. Set ENV variables in ~/.bash_profile

# Open up ~/.bash_profile with text-editor, enter following at bottom
export FOODB_URL="mysql://root:phytebyte@localhost/foo_db"
export CHEMBL_DB_URL="postgres:///chembl_25"

10. Re-load ~/.bash_profile

source ~/.bash_profile

11. Run Tests

# From phytebyte root dir
source env/bin/activate  # Active python virtual env
pytest -vv tests

12. Run Phytebyte!

python run.py

Customization

BioactiveCompoundSource The BioactiveCompoundSource abstract method defines an interface for fetching compounds by gene target, by name and via exclusion (by checking SMILES for equality). It represents a data source of compounds that will be queried to train a BinaryClassifierModel.

TargetInputs The TargetInput is a simple data structure that tells the BioactiveCompoundSource what method to use when fetching input data for the BinaryClassifierModel. One can leverage the GeneTargetsInput (as done in run.py) to train a model on all compounds that either agonize or antagonize the given gene, in this use case, PPARG. Alternatively, the CompoundNamesTargetInput can be used to give a list of compound names found in ChEMBL to manually select the compounds used to train the model.

Fingerprinters The FP_TYPE variable can be set to either daylight or spectrophore in run.py. Alternative fingerprints can be created by implementing the Fingerprinter abstract base class.

FoodCmpdSource The FoodCmpdSource abstract base class can be further implemented to support additional food database sources. Our case study uses FooDB, but we encourage extending to other sources!

BinaryClassifierModel The BinaryClassifierModel abstract base class can be further implemented to support alternative binary classifiers (SVM, LogisticRegression, etc.). Our use case leverages sklearn's Random Forest, and we also provide support for the TanimotoBinaryClassifier that uses tanimoto index to classify compounds.

Config

Fingerprinters In run.py, FP_TYPE can be set to either spectrophore or daylight

Database URLs In run.py we use environment variables storing CHEMBL_DB_URL and FOODB_URL. These get passed to each respective Source for querying compounds.

Negative Sample Size Factor The neg_sample_size_factor indicates the multiple applied to the number of positive compound samples (i.e. 20 means "20 times the number of positive samples").