Docking Ligands into Flexible and Solvated Macromolecules. 5. Force-Field-Based Prediction of Binding Affinities of Ligands to Proteins
By Pablo Englebienne, N. Moitessier. 2009
35 citations recorded by the source corpus.
Subfields: Computational Theory and Mathematics, Molecular Biology, Organic Chemistry, Computational Chemistry & Drug Discovery
How researchers see this paper
The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.
In Computational Theory and Mathematics
- Glaucius Oliva
- L. Ferreira
- Ricardo N Dos Santos
- Sun Choi
- Elizabeth Yuriev
- Vijayakumar Gosu
- Adriano Defini Andricopulo
- Paul A. Ramsland
In Molecular Biology
- Glaucius Oliva
- L. Ferreira
- Ricardo N Dos Santos
- Sun Choi
- Elizabeth Yuriev
- Vijayakumar Gosu
- Adriano Defini Andricopulo
- Paul A. Ramsland
In Organic Chemistry
- Sun Choi
- Elizabeth Yuriev
- Glaucius Oliva
- Vijayakumar Gosu
- Adriano Defini Andricopulo
- Paul A. Ramsland
- L. Ferreira
- Mark Agostino
In Computational Chemistry & Drug Discovery
- Sun Choi
- Elizabeth Yuriev
- Glaucius Oliva
- Vijayakumar Gosu
- Adriano Defini Andricopulo
- Paul A. Ramsland
- L. Ferreira
- Mark Agostino
Citations in the corpus
- Molecular Docking and Structure-Based Drug Design Strategies (2015, methodology): “This type of scoring function applies an ab initio method to calculate the energy associated with each term of the function using the equations of classical mechanics [57].”
- Role of computer-aided drug design in modern drug discovery (2015, methodology): “Existing methods that incorporate partial protein flexibility include Glide, RosettaLigand (Davis and Baker 2009), FLIPDock (Zhao and Sanner 2007), and FITTED (Corbeil et al. 2007, 2008; Corbeil and Moitessier 2009; Englebienne and Moitessier 2009a, b; Pottel et al. 2014; Therrien et al. 2014).”
- Challenges and advances in computational docking: 2009 in review (2011, background): “Englebienne and Moitessier (73) have developed empirical scoring functions RankScore 2.0, 3.0, and 4.0 within FITTED, derived from crystal and docked structures as well as trained from VS data.”