Pages that link to "Q50041152"
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The following pages link to AMOEBA Polarizable Atomic Multipole Force Field for Nucleic Acids. (Q50041152):
Displaying 17 items.
- Tinker 8: Software Tools for Molecular Design (Q57290042) (← links)
- Absolute binding free energies for the SAMPL6 cucurbit[8]uril host-guest challenge via the AMOEBA polarizable force field (Q57463690) (← links)
- Chemically Accurate Relative Folding Stability of RNA Hairpins from Molecular Simulations (Q58096148) (← links)
- Framework for Conducting and Analyzing Crystal Simulations of Nucleic Acids to Aid in Modern Force Field Evaluation (Q90013183) (← links)
- Development of a Robust Indirect Approach for MM → QM Free Energy Calculations That Combines Force-Matched Reference Potential and Bennett's Acceptance Ratio Methods (Q90048941) (← links)
- Molecular Dynamics Study of the Hybridization between RNA and Modified Oligonucleotides (Q90279052) (← links)
- Reconciling NMR Structures of the HIV-1 Nucleocapsid Protein NCp7 Using Extensive Polarizable Force Field Free-Energy Simulations (Q90363773) (← links)
- Impact of electronic polarizability on protein-functional group interactions (Q90486936) (← links)
- Towards large scale hybrid QM/MM dynamics of complex systems with advanced point dipole polarizable embeddings (Q90534443) (← links)
- Development and Testing of the OPLS-AA/M Force Field for RNA (Q91929384) (← links)
- Tubulin response to intense nanosecond-scale electric field in molecular dynamics simulation (Q92035246) (← links)
- Classical Pauli repulsion: An anisotropic, atomic multipole model (Q92054725) (← links)
- Accurate Biomolecular Simulations Account for Electronic Polarization (Q92191165) (← links)
- Molecular Dynamics Simulations of Ionic Liquids and Electrolytes Using Polarizable Force Fields (Q92373168) (← links)
- Recent Advances in Coarse-Grained Models for Biomolecules and Their Applications (Q92373510) (← links)
- Polarizable Force Fields for Biomolecular Simulations: Recent Advances and Applications (Q92651324) (← links)
- Combining Machine Learning and Computational Chemistry for Predictive Insights Into Chemical Systems (Q108768061) (← links)