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Overview/Features
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Overview
  Liaison: Efficient and accurate ligand-receptor binding free energy prediction
Liaison applies linear interaction approximation to accurately compute binding affinities for series of ligands with similar binding modes, making it a powerful tool for lead optimization.


The Advantages Of Linear Interaction Approximation

Accurate ranking of binding affinities is crucial in the lead optimization phase of pharmaceutical research in order to develop potent, effective drug candidates. Both academic groups and the pharmaceutical industry have invested a great deal of effort to meet this challenge. Several approaches have been developed, ranging from rapid QSAR-based scoring functions to computationally intensive free energy perturbation (FEP) calculations. But none have fully met the needs of researcher and developers. QSAR-type approaches, though rapid, involve many approximations and produce large errors in binding energy predictions. FEP approaches are more accurate, but cannot be used when ligand structures vary significantly. They also incur substantial CPU costs.

Linear interaction approximation (LIA) is a way of combining molecular mechanics calculations with experimental data to build a model scoring function for the evaluation of ligand-protein binding free energies. LIA methods strike a perfect balance between accuracy and computational cost.

Sulfonamide inhibitor rendered in tubes is shown docked inside the thrombin active site.
   Features
 
Universal applicability: Unlike FEP-based packages, Liaison can be applied to ligands with large variations in structures, allowing for a thorough investigation of modified leads.
Accurate binding affinities: Liaison predicts binding affinities to within ~1 kcal/mol of experimental values, at a fraction of the cost of traditional FEP methods.
Reduced computational cost: LIA only requires simulations of the ligand's bound and free states. The binding event is viewed as a replacement of the ligand's aqueous environment with a mixed aqueous/protein environment. Only interactions between the ligand and the protein or between the ligand and the aqueous environment enter into the quantities accumulated during the simulation. The protein-protein and protein-water interactions are considered part of the reference Hamiltonian.
Improved computational efficiency: The implicit solvent model is far more computationally efficient. Liaison is at least an order of magnitude faster than LIA packages that employ explicit solvents.
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