In this review we describe progress in docking and especially high-throughput docking (HTD) for applications in drug design and in silico screening. Computational methods that are used in HTD to assist drug design involve two steps: docking and scoring. Several current docking programs have the ability to generate protein-ligand configurations that are close to the correct structure, as revealed by X-ray crystallography, in many cases. Recent comparison studies of docking and scoring methods have shown that the choice of the best docking (and scoring) tool is to a large extent target-dependent. Most of the docking programs treat the ligand as flexible, but the protein conformation is kept rigid. We review algorithmic advances that allow for partial treatment of protein flexibility. The estimation of binding affinities (scoring) is, from a theoretical point of view, the most challenging part of ligand design. Despite significant progress, a fast and accurate computational prediction of binding affinities is still beyond the limits of current methods. We discuss multivariate statistical methods that have been proposed recently for improving HTD results and briefly outline simplified free energy calculations based on molecular dynamics simulations. Recent applications, in particular those based on end-point free energy models, are re-establishing the interest in this approach and we present an example from our research work. Finally, we discuss new application scenarios of HTD, including chemogenomics docking on entire protein families and docking of natural products.
Keywords: Virtual screening, docking algorithms, scoring functions, protein flexibility, molecular dynamics, natural products
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