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Relative Binding Free Energy Calculations in Drug Discovery: Recent Advances and Practical Considerations
Relative Binding Free Energy Calculations in Drug Discovery: Recent Advances and Practical Considerations
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Relative Binding Free Energy Calculations in Drug Discovery: Recent Advances and Practical Considerations

Cournia, Zoe ; Allen, Bryce ; Sherman, Woody

Journal of chemical information and modeling, 2017-12, Vol.57 (12), p.2911-2937 [Periódico revisado por pares]

United States: American Chemical Society

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2
Adaptive activation functions accelerate convergence in deep and physics-informed neural networks
Adaptive activation functions accelerate convergence in deep and physics-informed neural networks
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Adaptive activation functions accelerate convergence in deep and physics-informed neural networks

Jagtap, Ameya D. ; Kawaguchi, Kenji ; Karniadakis, George Em

Journal of computational physics, 2020-03, Vol.404 (C), p.109136, Article 109136 [Periódico revisado por pares]

Cambridge: Elsevier Inc

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3
Machine learning of linear differential equations using Gaussian processes
Machine learning of linear differential equations using Gaussian processes
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Machine learning of linear differential equations using Gaussian processes

Raissi, Maziar ; Perdikaris, Paris ; Karniadakis, George Em

Journal of computational physics, 2017-11, Vol.348, p.683-693 [Periódico revisado por pares]

Cambridge: Elsevier Inc

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4
Linear, first and second-order, unconditionally energy stable numerical schemes for the phase field model of homopolymer blends
Linear, first and second-order, unconditionally energy stable numerical schemes for the phase field model of homopolymer blends
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Linear, first and second-order, unconditionally energy stable numerical schemes for the phase field model of homopolymer blends

Yang, Xiaofeng

Journal of computational physics, 2016-12, Vol.327, p.294-316 [Periódico revisado por pares]

Cambridge: Elsevier Inc

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5
NNScore: A Neural-Network-Based Scoring Function for the Characterization of Protein−Ligand Complexes
NNScore: A Neural-Network-Based Scoring Function for the Characterization of Protein−Ligand Complexes
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NNScore: A Neural-Network-Based Scoring Function for the Characterization of Protein−Ligand Complexes

Durrant, Jacob D ; McCammon, J. Andrew

Journal of chemical information and modeling, 2010-10, Vol.50 (10), p.1865-1871 [Periódico revisado por pares]

Washington, DC: American Chemical Society

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6
Computing Relative Binding Affinity of Ligands to Receptor: An Effective Hybrid Single-Dual-Topology Free-Energy Perturbation Approach in NAMD
Computing Relative Binding Affinity of Ligands to Receptor: An Effective Hybrid Single-Dual-Topology Free-Energy Perturbation Approach in NAMD
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Computing Relative Binding Affinity of Ligands to Receptor: An Effective Hybrid Single-Dual-Topology Free-Energy Perturbation Approach in NAMD

Jiang, Wei ; Chipot, Christophe ; Roux, Benoît

Journal of chemical information and modeling, 2019-09, Vol.59 (9), p.3794-3802 [Periódico revisado por pares]

United States: American Chemical Society

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7
Reliable ABC model choice via random forests
Reliable ABC model choice via random forests
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Reliable ABC model choice via random forests

Pudlo, Pierre ; Marin, Jean-Michel ; Estoup, Arnaud ; Cornuet, Jean-Marie ; Gautier, Mathieu ; Robert, Christian P

Bioinformatics, 2016-03, Vol.32 (6), p.859-866 [Periódico revisado por pares]

England: Oxford University Press (OUP)

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8
Firedrake: Automating the Finite Element Method by Composing Abstractions
Firedrake: Automating the Finite Element Method by Composing Abstractions
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Firedrake: Automating the Finite Element Method by Composing Abstractions

Rathgeber, Florian ; Ham, David A. ; Mitchell, Lawrence ; Lange, Michael ; Luporini, Fabio ; Mcrae, Andrew T. T. ; Bercea, Gheorghe-Teodor ; Markall, Graham R. ; Kelly, Paul H. J.

ACM transactions on mathematical software, 2017-01, Vol.43 (3), p.1-27 [Periódico revisado por pares]

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9
Reaction-Based Enumeration, Active Learning, and Free Energy Calculations To Rapidly Explore Synthetically Tractable Chemical Space and Optimize Potency of Cyclin-Dependent Kinase 2 Inhibitors
Reaction-Based Enumeration, Active Learning, and Free Energy Calculations To Rapidly Explore Synthetically Tractable Chemical Space and Optimize Potency of Cyclin-Dependent Kinase 2 Inhibitors
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Reaction-Based Enumeration, Active Learning, and Free Energy Calculations To Rapidly Explore Synthetically Tractable Chemical Space and Optimize Potency of Cyclin-Dependent Kinase 2 Inhibitors

Konze, Kyle D ; Bos, Pieter H ; Dahlgren, Markus K ; Leswing, Karl ; Tubert-Brohman, Ivan ; Bortolato, Andrea ; Robbason, Braxton ; Abel, Robert ; Bhat, Sathesh

Journal of chemical information and modeling, 2019-09, Vol.59 (9), p.3782-3793 [Periódico revisado por pares]

United States: American Chemical Society

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10
A posteriori subcell limiting of the discontinuous Galerkin finite element method for hyperbolic conservation laws
A posteriori subcell limiting of the discontinuous Galerkin finite element method for hyperbolic conservation laws
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A posteriori subcell limiting of the discontinuous Galerkin finite element method for hyperbolic conservation laws

Dumbser, Michael ; Zanotti, Olindo ; Loubère, Raphaël ; Diot, Steven

Journal of computational physics, 2014-12, Vol.278, p.47-75 [Periódico revisado por pares]

Elsevier Inc

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