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Scoring functions and their evaluation methods for protein-ligand docking: recent advances and future directions.

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TLDR
Three basic types of scoring functions (force-field, empirical, and knowledge-based) and the consensus scoring technique that are used for protein-ligand docking are reviewed and a discussion of the challenges faced by existing scoring functions and possible future directions for developing improved scoring functions is discussed.
Abstract
The scoring function is one of the most important components in structure-based drug design. Despite considerable success, accurate and rapid prediction of protein–ligand interactions is still a challenge in molecular docking. In this perspective, we have reviewed three basic types of scoring functions (force-field, empirical, and knowledge-based) and the consensus scoring technique that are used for protein–ligand docking. The commonly-used assessment criteria and publicly available protein–ligand databases for performance evaluation of the scoring functions have also been presented and discussed. We end with a discussion of the challenges faced by existing scoring functions and possible future directions for developing improved scoring functions.

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Citations
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Molecular Docking and Structure-Based Drug Design Strategies

TL;DR: The purpose of this review is to examine current molecular docking strategies used in drug discovery and medicinal chemistry, exploring the advances in the field and the role played by the integration of structure- and ligand-based methods.
Journal ArticleDOI

Insights into Protein-Ligand Interactions: Mechanisms, Models, and Methods

TL;DR: The physicochemical mechanisms underlying protein–ligand binding, including the binding kinetics, thermodynamic concepts and relationships, and binding driving forces, are introduced and rationalized.
Journal ArticleDOI

Comprehensive evaluation of ten docking programs on a diverse set of protein-ligand complexes: the prediction accuracy of sampling power and scoring power.

TL;DR: Overall, the ligand binding poses could be identified in most cases by the evaluated docking programs but the ranks of the binding affinities for the entire dataset could not be well predicted by most docking programs.
References
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Journal ArticleDOI

The Protein Data Bank

TL;DR: The goals of the PDB are described, the systems in place for data deposition and access, how to obtain further information and plans for the future development of the resource are described.

疟原虫var基因转换速率变化导致抗原变异[英]/Paul H, Robert P, Christodoulou Z, et al//Proc Natl Acad Sci U S A

宁北芳, +1 more
TL;DR: PfPMP1)与感染红细胞、树突状组胞以及胎盘的单个或多个受体作用,在黏附及免疫逃避中起关键的作�ly.
Journal ArticleDOI

The Cambridge Structural Database: a quarter of a million crystal structures and rising

TL;DR: The Cambridge Structural Database now contains data for more than a quarter of a million small-molecule crystal structures, and projections concerning future accession rates indicate that the CSD will contain at least 500,000 crystal structures by the year 2010.
Journal ArticleDOI

Automated docking using a Lamarckian genetic algorithm and an empirical binding free energy function

TL;DR: It is shown that both the traditional and Lamarckian genetic algorithms can handle ligands with more degrees of freedom than the simulated annealing method used in earlier versions of AUTODOCK, and that the Lamarckia genetic algorithm is the most efficient, reliable, and successful of the three.
Journal ArticleDOI

Electrostatics of nanosystems: Application to microtubules and the ribosome

TL;DR: The application of numerical methods are presented to enable the trivially parallel solution of the Poisson-Boltzmann equation for supramolecular structures that are orders of magnitude larger in size.
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