On the quest of novel therapeutics, molecular docking methods have proven to be valuable tools for screening large libraries of compounds determining the interactions of potential drugs with the target proteins. A widely used docking approach is the simulation of the docking process guided by a binding energy function. On the basis of the molecular docking program autodock, we present pso@autodock as a tool for fast flexible molecular docking. Our novel Particle Swarm Optimization (PSO) algorithms varCPSO and varCPSO-ls are suited for rapid docking of highly flexible ligands. Thus, a ligand with 23 rotatable bonds was successfully docked within as few as 100 000 computing steps (rmsd = 0.87 Å), which corresponds to only 10% of the computing time demanded by autodock. In comparison to other docking techniques as gold 3.0, dock 6.0, flexx 2.2.0, autodock 3.05, and sodock, pso@autodock provides the smallest rmsd values for 12 in 37 protein–ligand complexes. The average rmsd value of 1.4 Å is significantly lower then those obtained with the other docking programs, which are all above 2.0 Å. Thus, pso@autodock is suggested as a highly efficient docking program in terms of speed and quality for flexible peptide–protein docking and virtual screening studies.

Structure-based virtual screening is carried out using molecular docking programs. A number of such docking programs are currently available, and the selection of docking program is difficult without knowing the characteristics or performance of each program. In this study, the screening performances of three molecular docking programs, DOCK, AutoDock, and GOLD, were evaluated with 116 target proteins. The screening performances were validated using two novel standards, along with a traditional enrichment rate measurement. For the evaluations, each docking run was repeated 1000 times with three initial conformations of a ligand. While each docking program has some merit over the other docking programs in some aspects, DOCK showed an unexpectedly better screening performance in the enrichment rates. Finally, we made several recommendations based on the evaluation results to enhance the screening performances of the docking programs.

A major problem in virtual screening concerns the accuracy of the binding free energy between a target protein and a putative ligand. Here we report an example supporting the outperformance of the AutoDock scoring function in virtual screening in comparison to the other popular docking programs. The original AutoDock program is in itself inefficient to be used in virtual screening because the grids of interaction energy have to be calculated for each putative ligand in chemical database. However, the automation of the AutoDock program with the potential grids defined in common for all putative ligands leads to more than twofold increase in the speed of virtual database screening. The utility of the automated AutoDock in virtual screening is further demonstrated by identifying the actual inhibitors of various target enzymes in chemical databases with accuracy higher than the other docking tools including DOCK and FlexX. These results exemplify the usefulness of the automated AutoDock as a new promising tool in structure-based virtual screening.

Computational docking of ligands to protein structures is a key step in structure-based drug design. Currently, the time required for each docking run is high and thus limits the use of docking in a high-throughput manner, warranting parallelization of docking algorithms. AutoDock, a widely used tool, has been chosen for parallelization. Near-linear increases in speed were observed with 96 processors, reducing the time required for docking ligands to HIV-protease from 81 min, as an example, on a single IBM Power-5 processor (1.65 GHz), to about 1 min on an IBM cluster, with 96 such processors. This implementation would make it feasible to perform virtual ligand screening using AutoDock.

* Computational methodologies have become a crucial component of many drug discovery programmes, from hit identification to lead optimization and beyond. * One key such methodology — docking of small molecules to protein binding sites — was pioneered during the early 1980s, and remains a highly active area of research. * The docking process involves the prediction of ligand conformation and orientation (or posing) within a targeted binding site. In general, there are two aims of docking studies: accurate structural modelling and correct prediction of activity. * Docking is generally devised as a multi-step process in which each step introduces one or more additional degrees of complexity. The process begins with the application of docking algorithms that pose small molecules in the active site. These algorithms are complemented by scoring functions that are designed to predict the biological activity through the evaluation of interactions between compounds and potential targets. * This article reviews basic concepts and specific features of small-molecule–protein docking methods and several selected applications, with particular emphasis on hit identification and lead optimization. * We attempt to distinguish between the problems of docking compounds into target sites and of scoring docked conformations, because the available data indicate that numerous robust and accurate docking algorithms are available, whereas imperfections of scoring functions continue to be a major limiting factor.

Understanding the ruling principles whereby protein receptors recognize, interact, and associate with molecular substrates and inhibitors is of paramount importance in drug discovery efforts. Protein-ligand docking aims to predict and rank the structure(s) arising from the association between a given ligand and a target protein of known 3D structure. Despite the breathtaking advances in the field over the last decades and the widespread application of docking methods, several downsides still exist. In particular, protein flexibility - a critical aspect for a thorough understanding of the principles that guide ligand binding in proteins - is a major hurdle in current protein-ligand docking efforts that needs to be more efficiently accounted for. In this review the key concepts of protein-ligand docking methods are outlined, with major emphasis being given to the general strengths and weaknesses that presently characterize this methodology. Despite the size of the field, the principal types of search algorithms and scoring functions are reviewed and the most popular docking tools are briefly depicted. Recent advances that aim to address some of the traditional limitations associated with molecular docking are also described. A selection of hand-picked examples is used to illustrate these features.

A computational procedure is detailed where techniques common in the drug discovery process-2D- and 3D-quantitative structure-activity relationships (QSAR)-are applied to rationalize the catalytic activity of a synthetically flexible, Ti-N=P ethylene polymerization catalyst system. Once models relating molecular properties to catalyst activity are built with the two QSAR approaches, two database mining approaches are used to select a small number of ligands from a larger database that are likely to produce catalysts with high activity when grafted onto the Ti-N=P framework. The software employed throughout this work is freely available, is easy to use, and was applied in a "black box" approach to highlight areas where the drug discovery tools, designed to address organic molecules, have difficulty in addressing issues arising from the presence of a metal atom. In general, 3D-QSAR offers an efficient way to screen new potential ligands and separate those likely to lead to poor catalysts from those that are likely to contribute to highly active catalysts. The results for 2D-QSAR appear to be quantitatively unreliable, likely due to the presence of a metal atom; nonetheless, there is evidence that qualitative predictions from different models may be reliable. Pitfalls in the database mining techniques are identified, none of which are insurmountable. The lessons learned about the potential uses and drawbacks of the techniques described herein are readily applicable to other catalyst frameworks, thereby enabling a rational approach to catalyst improvement and design.

Andreas Kerzmann, Dirk Neumann, and Oliver Kohlbacher