SFCscore: Scoring functions for affinity prediction of protein–ligand complexes

By C. Sotriffer, P. Sanschagrin, Hans Matter, Gerhard Klebe. 2008

111 citations recorded by the source corpus.

Subfields: Computational Theory and Mathematics, Molecular Biology, 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

1. Shengyou Huang
2. Xiaoqin Zou
3. Gisbert Schneider
4. Yumeng Yan
5. Roderick E. Hubbard
6. Pei-Zhang Zhou
7. Huanyu Tao
8. John Karanicolas

In Molecular Biology

1. Shengyou Huang
2. Xiaoqin Zou
3. Gisbert Schneider
4. Yumeng Yan
5. Roderick E. Hubbard
6. Huanyu Tao
7. Pei-Zhang Zhou
8. John Karanicolas

In Computational Chemistry & Drug Discovery

1. Shengyou Huang
2. Xiaoqin Zou
3. Gisbert Schneider
4. Roderick E. Hubbard
5. Stefano Moro
6. Veronica Salmaso

Citations in the corpus

• Molecular Docking and Structure-Based Drug Design Strategies (2015, neutral): “…[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] PLP [81]”
• Virtual screening: an endless staircase? (2010, background): “Although much progress has been made in the generation of potential ligand 'poses' by automated docking, the scoring functions used for prioritization of the various suggested poses are still grossly inaccurat”
• Advances and Challenges in Protein-Ligand Docking (2010, background): “GlideScore [51,52], PLP [119,120], SYBYL/F-Score [55], LigScore [118], LUDI [115,117], SCORE [116], X-Score [121], ChemScore [114], MedusaScore [122], AIScore [123], and SFCscore [124] are examples of empirical scoring functions.”