Molecular Nexus

Accurate force-field parameterization using graph neural network

CGConvNet that enables accurate parameterization of OPLS (Optimized Potentials for Liquid Simulations) force field for molecules.

Authors

Ray Zhu

Kha Trinh

Oliver Tang

Jaehee Park

License

Molecular Nexus is released under the MIT License

Name		Name	Last commit message	Last commit date
Latest commit History 31 Commits
archive		archive
dataloader		dataloader
final_evaluation		final_evaluation
final_evaluation_dataloader		final_evaluation_dataloader
final_evaluation_predictions		final_evaluation_predictions
job_submission_script_CGConvNet		job_submission_script_CGConvNet
lightning_logs		lightning_logs
logs		logs
plots		plots
pretrained_model_CGConvNet		pretrained_model_CGConvNet
src		src
train_dataset/processed		train_dataset/processed
validation_dataset		validation_dataset
.gitignore		.gitignore
0_preprocessing.py		0_preprocessing.py
1_hyperparameter_tuning_CGConvNet.py		1_hyperparameter_tuning_CGConvNet.py
2_generate_job_submission_script_CGConvNet.py		2_generate_job_submission_script_CGConvNet.py
3_parse_hyperparameter_tuning_results.py		3_parse_hyperparameter_tuning_results.py
3_parse_hyperparameter_tuning_results_CGConvNet.py		3_parse_hyperparameter_tuning_results_CGConvNet.py
4_make_prediction.py		4_make_prediction.py
5_results_analysis.py		5_results_analysis.py
README.md		README.md
data.json		data.json
ffbonded.itp		ffbonded.itp
ffnonbonded.itp		ffnonbonded.itp
final_evaluation_modified.py		final_evaluation_modified.py
helper.py		helper.py
hyperparameter_tuning_result.csv		hyperparameter_tuning_result.csv
hyperparameter_tuning_result_CGConvNet.csv		hyperparameter_tuning_result_CGConvNet.csv
make_permutation.py		make_permutation.py
opls.par		opls.par
permutation_example.json		permutation_example.json
permutation_example_masked.json		permutation_example_masked.json
plot_hp_tuning_results.py		plot_hp_tuning_results.py
plot_hp_tuning_results_CGConvNet.py		plot_hp_tuning_results_CGConvNet.py
plot_val_loss.py		plot_val_loss.py
requirements.txt		requirements.txt
validation_example.json		validation_example.json

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Accurate force-field parameterization using graph neural network

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