This paper reviews the use of similarity searching in chemical databases. It begins by introducing the concept of similarity searching, differentiating it from the more common substructure searching, and then discusses the current generation of fragment-based measures that are used for searching chemical structure databases. The next sections focus upon two of the principal characteristics of a similarity measure: the coefficient that is used to quantify the degree of structural resemblance between pairs of molecules and the structural representations that are used to characterize molecules that are being compared in a similarity calculation. New types of similarity measure are then compared with current approaches, and examples are given of several applications that are related to similarity searching. 1.

Most computational models of protein-ligand interactions consider only the energetics of the final bound state of the complex and do not examine the dynamics of the ligand as it enters the binding site. We have developed a novel approach to study the dynamics of protein-ligand interactions based on motion planning algorithms from the field of robotics. Our algorithm uses electrostatic and van der Waals potentials to compute the most energetically favorable path between any given initial and goal ligand configurations. We use probabilistic motion planning to sample the distribution of possible paths to a given goal configuration and compute an energy-based "difficulty weight" for each path. By statistically averaging this weight over several randomly generated starting configurations, we compute the relative difficulty of entering and leaving a given binding configuration. This approach yields details of the energy contours around the binding site and can be used to cha...

Three different database docking programs (Dock, FlexX, Gold) have been used in combination with seven scoring functions (Chemscore, Dock, FlexX, Fresno, Gold, Pmf, Score) to assess the accuracy of virtual screening methods against two protein targets (thymidine kinase, estrogen receptor) of known three-dimensional structure. For both targets, it was generally possible to

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Here, we describe two freely available web servers for molecular docking. The PatchDock method performs structure prediction of protein–protein and protein– small molecule complexes. The SymmDock method predicts the structure of a homomultimer with cyclic symmetry given the structure of the monomeric unit. The inputs to the servers are either protein PDB codes or uploaded protein structures. The services are available at

In this article we introduce a molecular docking algorithm called MolDock. MolDock is based on a new heuristic search algorithm that combines differential evolution with a cavity prediction algorithm. The docking scoring function of MolDock is an extension of the piecewise linear potential (PLP) including new hydrogen bonding and electrostatic terms. To further improve docking accuracy, a re-ranking scoring function is introduced, which identifies the most promising docking solution from the solutions obtained by the docking algorithm. The docking accuracy of MolDock has been evaluated by docking flexible ligands to 77 protein targets. MolDock was able to identify the correct binding mode of 87 % of the complexes. In comparison, the accuracy of Glide and Surflex is 82 % and 75%, respectively. FlexX obtained 58 % and GOLD 78 % on subsets containing 76 and 55 cases, respectively.

We report on a test of FLEXX, a fully automatic docking tool for flexible ligands, on a highly diverse data set of 200 protein–ligand com-plexes from the Protein Data Bank. In total 46.5 % of the complexes of the data set can be reproduced by a FLEXX docking solution at rank 1 with an rms devia-tion (RMSD) from the observed structure of less than 2 Å. This rate rises to 70 % if one looks at the entire generated solution set. FLEXX produces reli-able results for ligands with up to 15 components which can be docked in 80 % of the cases with acceptable accuracy. Ligands with more than 15 components tend to generate wrong solutions more often. The average runtime of FLEXX on this test set is 93 seconds per complex on a SUN Ultra-30 worksta-tion. In addition, we report on ‘‘cross-docking’ ’ ex-periments, in which several receptor structures of complexes with identical proteins have been used for docking all cocrystallized ligands of these com-plexes. In most cases, these experiments show that FLEXX can acceptably dock a ligand into a foreign receptor structure. Finally we report on screening runs of ligands out of a library with 556 entries against ten different proteins. In eight cases FLEXX is able to find the original inhibitor within the top 7 % of the total library

ABSTRACT Protein–small molecule docking algorithms provide a means to model the struc-ture of protein–small molecule complexes in struc-tural detail and play an important role in drug development. In recent years the necessity of sim-ulating protein side-chain flexibility for an accu-rate prediction of the protein–small molecule inter-faces has become apparent, and an increasing number of docking algorithms probe different ap-proaches to include protein flexibility. Here we de-scribe a new method for docking small molecules into protein binding sites employing a Monte Carlo minimization procedure in which the rigid body position and orientation of the small mole-cule and the protein side-chain conformations are optimized simultaneously. The energy function comprises van der Waals (VDW) interactions, an implicit solvation model, an explicit orientation hydrogen bonding potential, and an electrostatics model. In an evaluation of the scoring function the computed energy correlated with experimen-tal small molecule binding energy with a correla-tion coefficient of 0.63 across a diverse set of 229 protein – small molecule complexes. The docking method produced lowest energy models with a root mean square deviation (RMSD) smaller than 2 A ̊ in 71 out of 100 protein–small molecule crystal structure complexes (self-docking). In cross-docking calculations in which both protein side-chain and small molecule internal degrees of freedom were varied the lowest energy predictions had RMSDs less than 2 A ̊ in 14 of 20 test cases. Proteins

Docking is a computational technique that samples conformations of small molecules in protein binding sites; scoring functions are used to assess which of these conformations best complements the protein binding site. An evaluation of 10 docking programs and 37 scoring functions was conducted against eight proteins of seven protein types for three tasks: binding mode prediction, virtual screening for lead identification, and rank-ordering by affinity for lead optimization. All of the docking programs were able to generate ligand conformations similar to crystallographically determined protein/ligand complex structures for at least one of the targets. However, scoring functions were less successful at distinguishing the crystallographic conformation from the set of docked poses. Docking programs identified active compounds from a pharmaceutically relevant pool of decoy compounds; however, no single program performed well for all of the targets. For prediction of compound affinity, none of the docking programs or scoring functions made a useful prediction of ligand binding affinity.

We developed a knowledge-based statistical energy function for protein-ligand, protein-protein, and protein-DNA complexes by using 19 atom types and a distance-scale finite ideal-gas reference (DFIRE) state. The correlation coefficients between experimentally measured proteinligand binding affinities and those predicted by the DFIRE energy function are around 0.63 for one training set and two testing sets. The energy function also makes highly accurate predictions of binding affinities of protein-protein and protein-DNA complexes. Correlation coefficients between theoretical and experimental results are 0.73 for 82 protein-protein (peptide) complexes and 0.83 for 45 protein-DNA complexes, despite the fact that the structures of protein-protein (peptide) and protein-DNA complexes were not used in training the energy function. The results of the DFIRE energy function on protein-ligand complexes are compared to the published results of 12 other scoring functions generated from either physical-based, knowledge-based, or empirical methods. They include AutoDock, X-Score, DrugScore, four scoring functions in Cerius 2 (LigScore, PLP, PMF, and LUDI), four scoring functions in SYBYL (F-Score, G-Score, D-Score, and ChemScore), and BLEEP. While the DFIRE energy function is only moderately successful in ranking native or near native conformations, it yields the strongest correlation between theoretical and experimental binding affinities of the testing sets and between rmsd values and energy scores of docking decoys in a benchmark of 100 protein-ligand complexes. The parameters and the program of the all-atom DFIRE energy function are freely available for academic users at