This repository is BayeshERG official repository. It contains the pytorch implementation of BayeshERG and trained model to predict arbitrary compounds. The implementation of BayehERG has referred to the official implementation of related studies [1-3].

The BayeshERG is developed with the python v3.6 and following packages:dgl, pytorch, and rdkit.

BayeshERG follows GPL 3.0v license. Therefore, BayeshERG is open source and free to use for everyone.

However, hERG blockers or structual information found by using BayeshERG follows CC-BY-NC-4.0. Thus, those compounds are freely available for academic purposes or individual research but restricted for commercial use.

• Anaconda

To avoid the package version issue, we open our code with Anaconda virtual environment. Therefore, the Anaconda should be installed in advance. https://www.anaconda.com/products/individual

Any .csv file with smiles column.

(Example)

Conda environment file 'environment.yml' is provided

$ conda env create --name BayeshERG --file=environment.yml

$ conda activate BayeshERG

usage: $ python main.py [-i] input_csv_file_path 
                        [-o] output_file_name 
                        [-c] 'cpu' or 'gpu' (default 'cpu')
                        [-t] sampling time (integer, default 30)

• Example

// With GPU
$ python main.py -i data/External/EX1.csv -o EX1_pred -c gpu -t 30

// With CPU
$ python main.py -i data/External/EX1.csv -o EX1_pred -c cpu -t 30

The prediction results (Prediction score, Uncertainties) are appened to the input .csv file and saved to prediction_results directory as output_file_name.csv.

Also, the attention images(.svg) are also depicted and saved to attention_results/output_file_name directory.

Hyunho Kim, [email protected]

Minsu Park, [email protected]

Hojung Nam (Corresponding Author), [email protected]

[1] Gal, Yarin, Jiri Hron, and Alex Kendall. "Concrete dropout." arXiv preprint arXiv:1705.07832 (2017).

[2] Scalia, Gabriele, et al. "Evaluating scalable uncertainty estimation methods for deep learning-based molecular property prediction." Journal of chemical information and modeling 60.6 (2020): 2697-2717.

[3] Yang, Kevin, et al. "Analyzing learned molecular representations for property prediction." Journal of chemical information and modeling 59.8 (2019): 3370-3388.

@ Last modified : 2022.11.18