MCQ4Structures to compute similarity of molecule structures

By Tomasz Zok, Mariusz Popenda, Marta Szachniuk. 2013

43 citations recorded by the source corpus.

Subfields: Materials Chemistry, Molecular Biology

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Materials Chemistry

1. N. V. Dokholyan
2. Janusz M. Bujnicki
3. Feng Ding
4. Eric Westhof
5. Rhiju Das
6. Fang-Chieh Chou
7. Wipapat Kladwang
8. C. Cheng

In Molecular Biology

1. Rhiju Das
2. Janusz M. Bujnicki
3. N. V. Dokholyan
4. Eric Westhof
5. Wipapat Kladwang
6. Fang-Chieh Chou
7. Marta Szachniuk
8. Maciej Antczak

Citations in the corpus

• Integrating end-to-end learning with deep geometrical potentials for ab initio RNA structure prediction (2022, unknown): “The results show that while DRfold does not outperform the control methods in MCQ, likely due to the coarse-grained representation of RNA structures during the model training procedure, DRfold excels in Handedness score over all control methods.”
• RNA-Puzzles Round II: assessment of RNA structure prediction programs applied to three large RNA structures (2015, methodology): “Additionally, MCQ (Mean of Circular Quantities) score (Zok et al. 2014) was added as a reference to assess prediction in terms of torsion angle space.”
• RNA-Puzzles Round III: 3D RNA structure prediction of five riboswitches and one ribozyme (2017, methodology): “…levels (Parisien et al. 2009); the Clash score evaluated by MolProbity (Chen et al. 2010) assesses the atomic harmony of the structure, and the mean of circular quantities (MCQ) score (Zok et al. 2014) assesses the structural similarity with the native structure in the torsion angle space.”