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GauL-HDAD

Machine learning method for predicting thermochemical (and many more) properties

This repository contains the GauL HDAD algorithm for representing molecules as a numeric vector and neural networks for molecular property prediction.

The full paper can be found here. If you have any questions, comments, ideas, improvements, don't hesitate to contact me! Please find contact details here.

Requirements

For small datasets (<1000 molecules), it is possible to train on a standard laptop in less than 30 minutes. It is recommended to use high-performance computing for larger datasets. Please keep in mind that training large datasets (>40000 molecules) will take more than 2 days.

GauL-HDAD is written in python and can be run from command prompt. This requires the installation of several python packages, which are listed below. It is recommended to use Anaconda for managing packages (https://docs.anaconda.com/anaconda/install/).

  • Python 3.6 or higher
  • NumPy conda install numpy
  • RDKit conda install -c conda-forge rdkit
  • Scikit-learn conda install -c conda-forge scikit-learn
  • joblib conda install -c anaconda joblib
  • TensorFlow 2 pip install tensorflow
  • matplotlib conda install -c conda-forge matplotlib

Installation

GauL-HDAD can be used by cloning this repository and moving in the Anaconda terminal to the folder in which it was cloned.

  • Open Anaconda Prompt (Anaconda3) from the Windows start menu
  • Use the cd command in Anaconda Prompt to go to the folder where GauL-HDAD was cloned

Data

Models can only be trained when data is provided. GauL-HDAD can handle .txt files containing molecules and target values, separated by a tab. Three molecular formats can be parsed: SMILES, InChI and 3D coordinates in .mol files. When your data contains mesomeric radicals, use of InChIs is discouraged.

CC	100
InChI=1S/C2H6/c1-2/h1-2H3	100
ethane.mol	100

Although all formats can be used interchangeably, it is strongly recommended to stick to only one input type.

Training

A model ensemble can be trained by running following command in Anaconda Prompt (in the repository folder):

python train.py <input_file> <target_property> <save_folder>
  • <input_file> is the file location of the training data
  • <target_property> is the property that will be modeled.
    • Use h for enthalpy
    • Use s for entropy
    • Use cp for heat capacity
    • Something else can be used for another property, the models will be trained, but are possibly not optimal.
  • <save_folder> is the folder where you want to store your results. This folder will be created as a subfolder of the repository

Example: Your enthalpy data is stored in a subfolder Data as input.txt and you want to store your results in a folder named Results:

python train.py Data/input.txt h Results

WARNING: If save_folder already exists, the existing data will be overwritten!

A large number of files will be created in <save_folder>.

  • train_results.log gives all information you need: model architecture, preediction statistics and individual predictions
  • output_plot.png shows the input data as a function of the number of heavy atoms. This gives an idea about the distribution of your data.
  • hist is a folder that contains all individual histograms.
  • gmm is a folder that contains the Gaussian mixture models of the histograms in hist.
  • test_results_fold_x_y.txt are the results for the individual folds
  • gmm_dictionary.pickle contains the values of the Gaussian mixture models for use in new models
  • histogram_dictionary.pickle contains the values of the histograms
  • ll_dictionary.pickle contains the values of all log-likelihoods from Gaussian mixture modeling
  • test_statistics.txt contains the performance measures on the test set in each ensemble fold.
  • Fold x (x = 1 ... #folds) are folders containing data per fold:
    • a trained neural network
    • test_results_fold_x.txt contains the test set performance in this fold
    • test_ensemble_predictions_x.txt contains the predictions of the test set molecules with the "Real value" (from input.txt), the ensemble predicted value, the ensemble standard deviation on the prediction and the absolute error.

INFO: In the current version, nested cross-validation is implemented with 10 outer folds and 9 inner folds.

Training with previous representations

Creating Gaussian mixture models is a very time-consuming step in the algorithm. It is therefore likely that you want to work with previous data. This is possible with using a similar command as for training:

python retrain.py <input_file> <target_property> <save_folder>

The code will search for either a representations.pickle file or (when that is not included) for a gmm_dictionary.pickle file. It is, hence, necessary to create a <save_folder> that includes one of those files from a previous run! It will raise an error if neither of those files is included.

Making predictions

Making predictions with GauL-Thermo is easy! The command is similar to those for training and retrining:

python test.py <input_file> <target_property> <save_folder>

<input_file> is also a .txt file, but it has only one column: the molecule. All molecules must be stored in a .txt file all below each other. <save_folder> is the folder where the model was trained that you want to use for testing.

How to refer to this model?

When using GauL-HDAD for your own publication, please cite the original paper:

Learning Molecular Representations for Thermochemistry Prediction of Cyclic Hydrocarbons and Oxygenates
Dobbelaere, M.R.; Plehiers, P.P.; Van de Vijver, R.; Stevens, C.V.; Van Geem, K.M.
Journal of Physical Chemistry A, 2021, 125, 23, pp. 5166-5179