Prediction of Binding Free Energy of Protein–Ligand Complexes with a Hybrid Molecular Mechanics/Generalized Born Surface Area and Machine Learning Method

GXLE is a a hybrid molecular mechanics/generalized born surface area (MM/GBSA) and machine learning methond to predict the binding free energy of protein−ligand complexes。

More information is published in the paper.(https://pubs.acs.org/doi/10.1021/acsomega.1c04996)

To cite: Dong, L.; Qu, X.; Zhao, Y.; Wang, B., Prediction of Binding Free Energy of Protein–Ligand Complexes with a Hybrid Molecular Mechanics/Generalized Born Surface Area and Machine Learning Method. ACS Omega. 2021, 6, 32938–32947.

This is an instruction for users.

After the requirements are met, to apply our model and parameters, you need to

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1 copy the file named application to your service

2 open the file named application

3 put your data(*_protein.pdb and *_ligand.mol2) in the file named data

4 sh GXLE.sh

5 then the results.csv will show in the file named application

6 open the results.csv to see the score

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See the file named examples for the whole files process document

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See training set, validation set and testset in trainingset-gxl.csv, validation-gxl.csv and test-gxl.csv