SPEEDCOM: A Jupyter Notebook repository from jwa7

Authors: Joe Abbott, Ryan Beck, Hang Hu, Yang Liu, Lixin Lu.

Overview

SPEEDCOM is an open source python package that aims to predict the fluorescence emission and absorption spectra of small conjugated organic molecules. These features are predicted using a convolutional neural network, implemented with keras, and trained on data from the PhotochemCAD database. The software has a graphical-user-interface (GUI) where users can input the SMILES string for a given molecule and be returned its predicted spectra and associated characteristic quantities. For further details on the background science, and the operations of our program, please see our use cases.

Current Features

Input vaild SMILES of small organic molecules as input for prediction
Predictions on the maximum absorption/emission peak
Wrapper functions for a numerial encoding of SMILES and common descriptors generation
GUI on local host

Incoming Featrues

Sanitize SMILES input; add alternative input options
Predictions on multiple peaks of absorption/emission spectra
Customization of model training with user input data
Pipelining predictions to facilitate fluorophore design

GUI

Below shows the spectra and characteristics prediction of an example molecule, inputted via our GUI.

Configuration

Pre-requirements:

Python version 3.6.7 or later
conda version 4.6.8 or later
GitHub

Installation & Usage Instructions

You can execute the following commands from your computer's terminal application:

Either clone the SPEEDCOM repository:

git clone https://github.com/emissible/SPEEDCOM.git

or download the zip file:

curl -O https://github.com/emissible/SPEEDCOM/archive/master.zip
cd SPEEDCOM
conda env create -n speedcom_environment.yml
conda activate speedcom
cd speedcom && python runhtml.py
Find the isomeric SMILES string for the moelcule you want the spectra for and input it into the GUI!

Directory Structure

SPEEDCOM (master)  
|---data  
    |--- 
|---doc  
    |--- 
|---speedcom  
    |---frontend
        |---output
        |--jquery.min.js
        |--molecule_ex.png
        |--spectra_ex.png
        |--welcome.png
    |---notebook_scripts
        |--R2_plot.ipynb
        |--encode_smiles.ipynb
        |--rdkit_descriptor.ipynb
        |--rdkit_exploration.ipynb
        |--smiles_clean.ipynb
        |--smiles_cnn.ipynb
        |--to_help_write_tests.ipynb
    |---saved_models
        |--ems_dropna.best1.hdf5
        |--epsilon.best_-1.hdf5
        |--model_lstm_qy.json
        |--model_smiles_cnn.json
        |--model_smiles_ems_dropna.json
        |--model_smiles_epsilon_lstm.json
        |--smiles_wordmap.json
        |--weights.best.hdf5
        |--weight.qy_best.hdf5
    |---tests
        |---DATA_CLEAN_TEST_DIR
        |--__init__.py
        |--context,py
        |--tests_NNModels.py
        |--test_Prediction.py
        |--test_dataUtils.py
    	  |--test_data_extract.py
     	  |--test_model_utils.py
     	  |--test_readData_temp.tsv
     	  |--test_speedcom.py
     	  |--test_utilities.py
    |--NNModels.py
    |--Prediction.py
    |--__init__.py
    |--core.py
    |--dataUtils.py
    |--data_extract.py
    |--matplotlibrc
    |--model_utils.py
    |--runhtml.py
    |--speedcom.html
    |--utilities.py
    |--version.py  
|--.coveragerc
|--.gitignore  
|--.travis.yml
|--LICENSE  
|--README.md 
|--download.sh
|--numpyversion.py
|--requirements.txt
|--runtests.sh
|--setup.sh
|--speedcom_environment.yml

Contributions

Any contributions to the project are warmly welcomed! If you discover any bugs, please report them in the issues section of this repository and we'll work to sort them out as soon as possible. If you have data that you think will be good to train our model on, please contact one of the authors.

References

Garrett B. Goh et al. 2018. SMILES2vec. In Proceedings of ACM SIGKDD Conference, London, UK, Aug, 2018 (KDD 2018), 8 pages

License

SPEEDCOM is licensed under the MIT license.

jwa7/SPEEDCOM