Lazar nanoparticle

lazar

Ruby libraries for the lazar framework

Authors: Christoph Helma, Micha Rautenberg, Denis Gebele, in silico toxicology gmbh.

lazar depends on a couple of external programs and libraries. All required libraries will be installed with the gem install lazar command. If any of the dependencies fails to install, please check if all required development packages are installed from your operating systems package manager (e.g. apt, rpm, pacman, ...). You will need a working Java runtime to use descriptor calculation algorithms from CDK and JOELib libraries.

gem install lazar

Please be patient, the compilation of external libraries can be very time consuming. If installation fails you can try to install manually:

  git clone https://github.com/opentox/lazar.git
  cd lazar
  ruby ext/lazar/extconf.rb
  bundle install

The output should give you more verbose information that can help in debugging (e.g. to identify missing libraries).

Tutorial

Execute the following commands either from an interactive Ruby shell or a Ruby script:

Create and use lazar models for small molecules

Create a training dataset

Create a CSV file with two columns. The first line should contain either SMILES or InChI (first column) and the endpoint (second column). The first column should contain either the SMILES or InChI of the training compounds, the second column the training compounds toxic activities (qualitative or quantitative). Use -log10 transformed values for regression datasets. Add metadata to a JSON file with the same basename containing the fields "species", "endpoint", "source" and "unit" (regression only). You can find example training data at Github.

Create and validate a lazar model with default algorithms and parameters

validated_model = Model::Validation.create_from_csv_file EPAFHM_log10.csv

This command will create a lazar model and validate it with three independent 10-fold crossvalidations.

Inspect crossvalidation results

validated_model.crossvalidations

Predict a new compound

Create a compound

compound = Compound.from_smiles "NC(=O)OCCC"

Predict Fathead Minnow Acute Toxicity

validated_model.predict compound

Experiment with other algorithms

You can pass algorithms parameters to the Model::Validation.create_from_csv_file command. The API documentation provides detailed instructions.

Create and use lazar nanoparticle models

Create and validate a nano-lazar model from eNanoMapper with default algorithms and parameters

validated_model = Model::Validation.create_from_enanomapper

This command will mirror the eNanoMapper database in the local database, create a nano-lazar model and validate it with five independent 10-fold crossvalidations.

Inspect crossvalidation results

validated_model.crossvalidations

Predict nanoparticle toxicities

Choose a random nanoparticle from the "Potein Corona" dataset

  training_dataset = Dataset.where(:name => "Protein Corona Fingerprinting Predicts the Cellular Interaction of Gold and Silver Nanoparticles").first
  nanoparticle = training_dataset.substances.shuffle.first

Predict the "Net Cell Association" endpoint

validated_model.predict nanoparticle

Experiment with other datasets, endpoints and algorithms

You can pass training_dataset, prediction_feature and algorithms parameters to the Model::Validation.create_from_enanomapper command. The API documentation provides detailed instructions. Detailed documentation and validation results can be found in this publication.

• API documentation

Copyright
Copyright (c) 2009-2017 Christoph Helma, Martin Guetlein, Micha Rautenberg, Andreas Maunz, David Vorgrimmler, Denis Gebele. See LICENSE for details.