A general and fast scoring function for protein-ligand interactions: a simplified potential approach.

By I. Muegge, Y. Martin. 1999

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A machine learning approach to predicting protein-ligand binding affinity with applications to molecular docking (2010, unknown): “This cut-off distance was suggested in PMF (Muegge and Martin, 1999) as sufficiently large to implicitly capture solvation effects, although no claim about the optimality of this choice is made.”
FlexE: efficient molecular docking considering protein structure variations. (2001, methodology): “Here, we present a new approach for handling protein structure variations using one of the various scoring functions which are proposed for docking applications.(9,30-32) None of these scor-”
General and targeted statistical potentials for protein–ligand interactions (2005, methodology): “14,15,18–26 We will focus the comparison here on Drugscore 14 and PMF, 15 which are the most widely used.”
A new test set for validating predictions of protein–ligand interaction (2002, background): “However, diversity in terms of protein–ligand complexes appears to be lower for this set of 200 entries: the FlexX set covers 99 different types of protein, and 9 of its types contain 5 or more entries (d-xylose isomerase (5); carboxypeptidase A (6); cytochrome p450-cam (6); hiv-1 protease (6); elas”