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LabMate.AI was designed to help identifying optimized conditions for chemical reactions.

The software is written in the Python 2.7 programming language and uses the following dependencies:

- NumPy (>= 1.11.3)
- Pandas (>= 0.19.2)
- Scikit-learn (>= 0.18.1)





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random_reaction_gen.py

This script creates two text files that are required to run LabMate.AI:

1) "all_combos.txt" - the file contains all possible combinations of reaction conditions.
2) "train_data.txt" - the file contains 10 random reaction sampled from all possible combinations.


The script requires minor editing and instructions are available once the script is opened with a text editor.
To run the script, open a terminal in your destiny folder and type:

>>> python random_reaction_gen.py


After performing the reactions, add a column in the end of the "train_data.txt" file mentioning the reaction yield/conversion or similar (sample file available)

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LabMate.AI.py


This script implements a routine to search for the next best experiment to be carried out.


To run LabMate.AI, open a terminal and type:

>>> python LabMate-AI.py

in the folder where the Python script, the "train_data.txt" and the "all_combos.txt" files are located. The columns in the txt files must be tab separated.
The grid search routine will run in 6 CPUs (n_jobs=6 as in line 56), so make sure there are enough computational resources available.



Train data txt file:
- Fist column is the reaction identifier
- Last column is the reaction yield/conversion
- Columns in the middle correspond to the descriptor set
- File must be named "train_data.txt", otherwise it will not be recognised by the script.


Search space txt file:
- Fist column is the reaction identifier 
- Following columns correspond to the descriptor set
- File must be named "all_combos.txt", otherwise it will not be recognised by the script.


Output files:
- "best_score.txt" - saves the negative mean absolute error value (lower absolute value is better)
- "feature_importances.txt" - importance (given in the range of 0-1) for each descriptor, according to the random forest algorithm
- "selected_reaction.txt" - this is the next best experiment, as suggested by LabMate.AI
- "predictions.txt" - predictions for all possible reactions
- "random_forest_model_grid.sav" - saves the model