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        "description": "# recommendation_engine\nRepo for the recommendation engine that was part of the DRP project\n\n\n## Recommender Pipeline\n\nSteps to implement a recommender pipeline\n(Specific implementation of this pipeline is available in ./recommender/recommender_pipeline.py)\n\n1. Generate reaction features\n    - Get the chemicals in a reaction. For DRP these are referred to as triples\n    - Generate descriptors for each of the chemicals in the reaction\n    - Generate a sampling grid of reaction parameters\n    - Expand grid by associating descriptors with each point on the grid\n\n2. Run trained models with the reaction Sieve\n    - Get a trained machine learning model\n    - Filter sampling grid by running it through the ML model\n    - Make a list of all the potentially successful reactions\n      as predicted by the ML model\n\n3. Recommend reactions\n    - Calculate the mutual information of the potential reactions\n      as compared to the already completed reactions\n    - Select the top 'k' reactions with the highest MI\n\n\n### Progress\n\n- [x] Generate Reaction features\n- [x] Reaction Sieve\n- [x] Reaction Recommender\n- [ ] Test and evaluate against Nature paper\n\n",
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