Predicting binding affinities of protein ligands from three-dimensional models: application to peptide binding to class I major histocompatibility proteins.
By D. Rognan, Sanne Lise Lauemøller, Arne Holm, Søren Buus, Vincenzo Tschinke. 1999
Subfields: Computational Biology & Bioinformatics, Computational Chemistry & Drug Discovery
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In Computational Chemistry & Drug Discovery
- António J. M. Ribeiro
- Rui P. P. Neves
- Pedro Alexandrino Fernandes
- Silvia A. Martins
- Maria João Ramos
- João T. S. Coimbra
- Sérgio Filipe Sousa
- D. Lee
Citations in the corpus
- Molecular Docking and Structure-Based Drug Design Strategies (2015, neutral): “…ChemScore [60] PMF_Score [83] ICM [46] X_Score [66] MotifScore [84] LigandFit [47] F_Score [73] RF_Score [85] Molegro Virtual Docker [48] Fresno [75] PESD_SVM [86] SYBYL_G-Score [73] SCORE [76] PoseScore [87] SYBYL_D-Score [73] LUDI [77] MedusaScore [74] SFCscore [78] HYDE [79] LigScore [80]…”
- Protein-ligand docking in the new millennium--a retrospective of 10 years in the field. (2013, methodology): “Popular examples of scoring functions include ChemScore [15], DrugScore [16, 17], D-Score [18], Fresno [19], F-Score [20], G-Score [18], GoldScore [21], SMoG score [22], and X-SCORE [23].”
- Towards the development of universal, fast and highly accurate docking/scoring methods: a long way to go (2008, background): “, 1996) Fresno Standalone Empirical (Rognan et al., 1999) GlideScore Glide Empirical (Friesner et al.”