Skip to main content

A standalone module to build a recommender pipeline

Project description


Repo for the recommendation engine that was part of the DRP project

Recommender Pipeline

Steps to implement a recommender pipeline (Specific implementation of this pipeline is available in ./recommender/

  1. Generate reaction features

    • Get the chemicals in a reaction. For DRP these are referred to as triples
    • Generate descriptors for each of the chemicals in the reaction
    • Generate a sampling grid of reaction parameters
    • Expand grid by associating descriptors with each point on the grid
  2. Run trained models with the reaction Sieve

    • Get a trained machine learning model
    • Filter sampling grid by running it through the ML model
    • Make a list of all the potentially successful reactions as predicted by the ML model
  3. Recommend reactions

    • Calculate the mutual information of the potential reactions as compared to the already completed reactions
    • Select the top 'k' reactions with the highest MI


  • [x] Generate Reaction features
  • [x] Reaction Sieve
  • [x] Reaction Recommender
  • [ ] Test and evaluate against Nature paper

Project details

Download files

Download the file for your platform. If you're not sure which to choose, learn more about installing packages.

Files for chemrecommender, version 0.0.1
Filename, size File type Python version Upload date Hashes
Filename, size chemrecommender-0.0.1.macosx-10.7-x86_64.tar.gz (13.7 kB) File type Source Python version None Upload date Hashes View
Filename, size chemrecommender-0.0.1-py3-none-any.whl (8.7 kB) File type Wheel Python version py3 Upload date Hashes View

Supported by

Pingdom Pingdom Monitoring Google Google Object Storage and Download Analytics Sentry Sentry Error logging AWS AWS Cloud computing DataDog DataDog Monitoring Fastly Fastly CDN DigiCert DigiCert EV certificate StatusPage StatusPage Status page