The development of a simple empirical scoring function to estimate the binding constant for a protein-ligand complex of known three-dimensional structure

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Molecular Docking and Structure-Based Drug Design Strategies (2015, neutral): “Empirical Knowledge-Based DOCK [32] AutoDock [34] SMoG [82] AutoDock [34] GlideScore [37] DrugScore [62] GoldScore [35] 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 [7”
Towards the development of universal, fast and highly accurate docking/scoring methods: a long way to go (2008, background): “Since the pioneering work of Bo¨hm (1994) in the development of LUDI, a significant amount of work has been devoted to the development and improvement of empirical SFs.”
Improved protein–ligand docking using GOLD (2003, unknown): “For calculations based on the experimental binding modes of the ligands, good scoring functions, suitable for docking, give s values of around 8 kJ/mol, 11,12 which corresponds to about 1.5 orders of magnitude in the afﬁnity.”
A machine learning approach to predicting protein-ligand binding affinity with applications to molecular docking (2010, unknown): “Five scoring functions in the Discovery Studio software version 2.0 (Accelrys, 2001): LigScore (Krammer et al., 2005), PLP (Gehlhaar et al., 1995), PMF (Muegge, 2000, 2001, 2006; Muegge and Martin, 1999), Jain (Jain, 1996) and LUDI (Böhm, 1994, 1998).”