VAD-MM/GBSA Description

MM/GBSA is widely used in end-point binding free energy prediction in structure-based drug design. Here, combining with machine-learning optimization, we present a refinement version of MM/GBSA named Variable Atomic Dielectric MM/GBSA (VAD-MM/GBSA) that exhibits improved accuracy for predicting binding affinities of diverse protein-ligand systems and is promising to be used in post-processing of structure based virtual screening.
Simulated annealing-based optimization is used to search the optimal atomic dielectric constant distributions, which are correlative with 87 descriptors of the ligand and the pocket of protein by XGBoost regression.
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Submitting Jobs

① Input your jobname with letters, numbers or underline. ② Input your email address to receive the results. ③ Upload an Amber topology (.prmtop) for the protein. ④ Upload an Amber topology (.prmtop) for the ligand. ⑤ Upload an Amber NetCDF trajectory (.nc) for the protein-ligand complex. 25 (or 50) MD snapshots in the trajectory are supported. ⑥ Upload an Amber NetCDF file (.nc) for the protein-ligand native structure (optional). If not provided, the last frame in the trajectory will be used as the native structure. ⑦ Select a model for prediction (VAD-MM/GBSA default). ⑧ Click here to submit your job.
  • Make sure the uploaded files generated by Amber package (Amber v2016 and v2018 have been tested).
  • Make sure waters and ions excluded in the uploaded files.

Job Output

Once your job is completed, an email with the result calculated by our VAD-MM/GBSA method will be sent to you, and then you can extract the information you are interested from the result.


Name: your jobname
Type: function type
Submitted Time: the system time when you submitted your job
  • Queued: the job has been submitted and wait in queue for calculation.
  • Running: the job is on calculation.
  • Completed: the job has finished and users can view or download the results.
  • Error: Some errors happened. Please check your input files or contact us.