Evans_2020_J.Chem.Theory.Comput__

Reference

Title : Combining Machine Learning and Enhanced Sampling Techniques for Efficient and Accurate Calculation of Absolute Binding Free Energies - Evans_2020_J.Chem.Theory.Comput__
Author(s) : Evans R , Hovan L , Tribello GA , Cossins BP , Estarellas C , Gervasio FL
Ref : J Chem Theory Comput , : , 2020
Abstract :

Calculating absolute binding free energies is challenging and important. In this paper, we test some recently-developed metadynamics-based methods and develop a new combination with a Hamiltonian replica-exchange approach. The methods were tested on 18 chemically-diverse ligands with a wide range of different binding affinities to a complex target; namely, human soluble epoxide hydrolase. The results suggest that metadynamics with a funnel-shaped restraint can be used to calcu-late, in a computationally affordable and relatively accurate way, the absolute binding free energy for small fragments. When used in combination with an optimal path-like variable obtained using machine learning; or with the Hamiltonian replica-exchange algorithm SWISH; this method can achieve reasonably accurate results for increasingly complex ligands, with a good balance of computational cost and speed. An additional benefit of using the combination of metadynamics and SWISH is that it also provides useful information about the role of water in the binding mechanism.

PubMedSearch : Evans_2020_J.Chem.Theory.Comput__
PubMedID: 32427471

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Citations formats

Evans R, Hovan L, Tribello GA, Cossins BP, Estarellas C, Gervasio FL (2020)
Combining Machine Learning and Enhanced Sampling Techniques for Efficient and Accurate Calculation of Absolute Binding Free Energies
J Chem Theory Comput :

Evans R, Hovan L, Tribello GA, Cossins BP, Estarellas C, Gervasio FL (2020)
J Chem Theory Comput :