A standalone module to build a recommender pipeline
Project description
recommendation_engine
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/recommender_pipeline.py)
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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
-
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
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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
Progress
- Generate Reaction features
- Reaction Sieve
- Reaction Recommender
- Test and evaluate against Nature paper
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