RC_CVAE

Tensorflow implementation of the model described in the paper Conditional Variational AutoEncoder to Predict Suitable Conditions for Hydrogenation Reactions

Components

• descriptors_generation.py - functions for data preparation (x, y generation)
• cvae_models.py - functions for RC CVAE modeling
• Run_VAEs.ipynb - tutorial
• example_data/example_hydrogenation_USPTO.rdf - example dataset
• example_data/acids_bases_poisons_list.txt - list of additives: acids, bases, catalytic poisons
• example_data/catalysts_list.txt - list of catalysts

Data

Note: Because the Reaxys database is commercially available, we do not have permission to release the datasets used in this paper to the public. Instead, we provide an example dataset ("example_data/example_hydrogenation_USPTO.rdf") so that anyone can test the code. It contains some hydrogenation chemical reaction records from USPTO database, preliminary standardized.

Usage

Example of usage can be found in Run_VAEs.ipynb

Dependencies

Only python 3.6

• numpy == 1.18.1
• tensorflow-gpu == 2.1.0 ; python_version == '3.6'
• tensorflow == 2.1.0
• tensorflow_probability == 0.9.0
• keras == 2.2.4
• h5py == 2.10.0
• git+https://github.com/cimm-kzn/s-vae-tf.git
• cgrtools == 3.1.9
• CIMtools==3.1.0
• Fragmentor

Installation

1. Install dependencies from requirements.py
2. Download Fragmentor 2017 from https://github.com/cimm-kzn/CIMtools/tree/master/Fragmentor based on your OS and add it to bin/ folder of your virtual environment
3. Rename Fragmentor file to fragmentor-2017.x

Citation

@Article{Mazitov2024, title={Conditional variational autoEncoder to predict suitable conditions for hydrogenation reactions}, author={Mazitov, Daniyar A. and Poyezzhayeva, Assima and Afonina, Valentina A. and Gimadiev, Timur R. and Madzhidov, Timur I.}, }exa