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Scoring functions binding mode prediction

Raha K, Merz KM. Large-scale validation of a quantum mechanics based scoring function predicting the binding affinity and the binding mode of a diverse set of protein-ligand complexes. J Med Chem 2005 48 4558-75. [Pg.349]

SVL script. Not surprisingly, only 60 molecules passed this filter and were then re-ranked by GOLD scoring function (3). One of the 10 top-scored molecules was proposed for synthesis and the compound 3 was determined to have an IC50 value of 3.0 xM. Subsequent X-ray structure of 3 in complex with NS5B was solved at 2.0 A and this molecule shows a binding mode just as predicted in the thumb domain (7) (Fig. 9.8). [Pg.184]

Predicting Binding Modes, Binding Affinities and Hot Spots for Protein-Ligand Complexes Using a Knowledge-Based Scoring Function. [Pg.81]

Table 2 The success rates for binding mode prediction and the correlation coefficients (R) for binding affinity prediction with seven knowledge-based scoring functions on Wang et al. s test set of 100 diverse protein-ligand complexes... Table 2 The success rates for binding mode prediction and the correlation coefficients (R) for binding affinity prediction with seven knowledge-based scoring functions on Wang et al. s test set of 100 diverse protein-ligand complexes...
As mean-force potentials become increasingly accurate, they will serve as a valuable tool for structure-based drug design. Detailed analysis of the mean-force potentials will provide insightful hints on how to improve other physics-based scoring functions. Mean-force potentials that perform well on binding mode predictions may also be implemented for molecular dynamics simulations of protein-ligand interactions. [Pg.293]


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