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Implicit ligand theory for relative binding free energies.

Trung Hai NguyenDavid D L Minh
Published in: The Journal of chemical physics (2018)
Implicit ligand theory enables noncovalent binding free energies to be calculated based on an exponential average of the binding potential of mean force (BPMF)-the binding free energy between a flexible ligand and rigid receptor-over a precomputed ensemble of receptor configurations. In the original formalism, receptor configurations were drawn from or reweighted to the apo ensemble. Here we show that BPMFs averaged over a holo ensemble yield binding free energies relative to the reference ligand that specifies the ensemble. When using receptor snapshots from an alchemical simulation with a single ligand, the new statistical estimator outperforms the original.
Keyphrases
  • binding protein
  • dna binding
  • convolutional neural network
  • density functional theory
  • neural network
  • machine learning
  • deep learning