Merck molecular force field. I. Basis, form, scope, parameterization, and performance of MMFF94

By T. Halgren. 1996

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Citations in the corpus

Graph Convolutional Policy Network for Goal-Directed Molecular Graph Generation (2018, positive): “We designed a simple steric strain ﬁlter that performs MMFF94 forceﬁeld [11] minimization on a molecule, and then penalizes it as being too sterically strained if the average angle bend energy exceeds a cutoff of 0.82 kcal/mol.”
Open Babel: An open chemical toolbox (2011, methodology): “The MMFF94 (s) force field can be used for organic or drug-like molecules [27-31].”
Torsional Diffusion for Molecular Conformer Generation (2022, methodology): “68 Finally, we evaluate how well a torsional Boltzmann generator trained with MMFF [Halgren, 1996] energies can sample the corresponding Boltzmann density over torsion angles.”
BALLView: a tool for research and education in molecular modeling (2006, methodology): “Furthermore, we are implementing the Merck Molecular Force Field (MMFF94; Halgren, 1996) and an interface for protein–protein and protein–ligand docking, which will be included in the next major BALLView release.”
3D Equivariant Diffusion for Target-Aware Molecule Generation and Affinity Prediction (2023, methodology): “To measure such consistency, we optimize the generated structure with Merck Molecular Force Field (MMFF) Halgren (1996) and calculate the RMSD between pre-and pos-MMFF-optimized coordinates for different rigid fragments that do not contain any rotatable bonds.”
Can We Still Trust Docking Results? An Extension of the Applicability of DockBench on PDBbind Database (2019, methodology): “Ligand geometries were minimized in the first step of DockBench with Openbabel routing using the MMFF94 force field [15].”
Uni-Mol: A Universal 3D Molecular Representation Learning Framework (2023, methodology): “To generate 3D conformations, we use ETKGD [41] with Merck Molecular Force Field [42] optimization in RDKit [43] to randomly generate 10 conformations for each molecule.”
Learning Neural Generative Dynamics for Molecular Conformation Generation (2021, methodology): “To yield a better comparison, we further test our model by taking the generated structures as initial states and utilize the Merck Molecular Force Field (MMFF) (Halgren, 1996a) to ﬁnd the local stable points.”
Computer-Aided Drug Design Methods (2017, background): “Organic molecule force fields such as CGenFF (37, 38), GAFF (40) or MMFF94 (77) can be used for this purpose.”
On the inner workings of Monte Carlo codes (2013, unknown): “A few examples of popular generic force fields are AMBER,[27–31] OPLS,[32–35] AMBER/OPLS,[36–39] CHARMM,[40–42] GROMOS, [43–45] CVFF,[46] CFF,[47–49] MM2,[50–52] MM3, [53,54] MM4,[55–60] MMFF94,[61–66] DREIDING, [67] COMPASS,[68] PCFF [68] and UFF.”