DISI - User contributions [en]

DOCK 6

2016-03-23T23:59:02Z

Sudipto: /* Manual */

DOCK 6 is a general purpose [[molecular docking]] program developed by the [[Kuntz Laboratory]] at [[UCSF]]. DOCK 6 (released summer 2006) replaced [[DOCK 5]] (2001-2006), which is no longer available. [[DOCK 5]] was a complete re-write of [[DOCK 4]] (1998-2002), which itself was a complete re-design and re-write of [[DOCK 3.5]] (released 1993-1994).

DOCK 6 is coded mainly in C++. Using the object oriented model, it is functionally separated into independent components (classes, methods), allowing a high degree of modularity and programming flexibility. Accessory programs are written in a variety of languages including C and Fortran 77. Source code is available for all programs. The DOCK suite of programs requires on the order of 50 MB of disk space and 512 MB RAM. Some runs may require considerably more disk space and more memory.

* [http://dock.compbio.ucsf.edu DOCK website]
* [http://dock.compbio.ucsf.edu/Online_Licensing/index.htm Licensing]
* Manual as a single PDF. [[Image:Dock61.pdf]]
* [http://dock.compbio.ucsf.edu/DOCK_6/tutorials/index.htm Tutorials]
* [http://dock.compbio.ucsf.edu/Test_Sets/index.htm Test sets]
* [[DOCK:FAQ]]

= General Overview =

DOCK addresses the problem of "docking" molecules to each other. In general, "docking" is the identification of the low-energy binding modes of a small molecule, or ligand, within the active site of a macromolecule, or receptor, whose structure is known. A compound that interacts strongly with, or binds, a receptor associated with a disease may inhibit its function and thus act as a drug. Solving the docking problem computationally requires an accurate representation of the molecular energetics as well as an efficient algorithm to search the potential binding modes.

Historically, the DOCK algorithm addressed rigid body docking using a geometric matching algorithm to superimpose the ligand onto a negative image of the binding pocket. Important features that improved the algorithm's ability to find the lowest-energy binding mode, including force-field based scoring, on-the-fly optimization, an improved matching algorithm for rigid body docking and an algorithm for flexible ligand docking, have been added over the years. For more information on past versions of DOCK, click here.

With the release of DOCK 6, we continue to improve the algorithm's ability to predict binding poses by adding new features like force-field scoring enhanced by solvation and receptor flexibility. For more information about the current release of DOCK, click here.

= What Can DOCK Do for You =

We and others have used DOCK for the following applications:

* predict binding modes of small molecule-protein complexes
* search databases of ligands for compounds that inhibit enzyme activity
* search databases of ligands for compounds that bind a particular protein
* search databases of ligands for compounds that bind nucleic acid targets
* examine possible binding orientations of protein-protein and protein-DNA complexes
* help guide synthetic efforts by examining small molecules that are computationally derivatized
* many more...

= Manual =

* [[Installing DOCK 6]]
* [[What does DOCK do?]]
* [[DOCK Accessories]]
* [[History of DOCK 6]]
* [[Command line arguments in DOCK6]]
* [[Ligand File Input]]
* [[Database Filter]]
* [[Orienting the Ligand]]
* [[Internal Energy Calculation]]
* [[Ligand Flexibility]]
* [[Scoring Functions in DOCK 6]]

[[Category:DOCK]]
[[Category:DOCK 6]]
[[Category:Software]]
[[Category:Freecom]]

Ligand Flexibility

2016-03-23T23:58:36Z

Sudipto: Created page with " The internal degrees of freedom of the ligand can be sampled with the anchor-and-grow incremental construction approach. This conformational search algorithm has been valida..."

The internal degrees of freedom of the ligand can be sampled
with the anchor-and-grow incremental construction approach.
This conformational search algorithm has been validated
for binding mode prediction on sets of ligands that have
no more than seven rotatable bonds
([[Moustakas et al., 2006]]).

=Anchor-and-Grow=

The process of docking a molecule using the
anchor-first strategy is shown in the Workflow for Anchor-and-Grow Algorithm
[[Ewing et al. 2001]].
First, the largest rigid substructure of the ligand (anchor)
is identified (see[[Identification of Rigid Segments]]'') ''and
rigidly oriented in the active site (orientation) by matching its heavy
atoms centers to the receptor sphere centers
(see [[Orienting the Ligand]]).
The anchor orientations are evaluated and optimized using the scoring
function (see [[Scoring]]) and
the energy minimizer (see [[Minimization]]).
In general,
the orientations are then ranked according to their score, spatially
clustered by heavy atom root mean squared deviation (RMSD), and pruned
(see [[Pruning the Conformation Search Tree]]). Next, the remaining flexible
portion of the ligand (see[[Identification of Flexible Layers]])
is built onto the best anchor orientations within the context of the
receptor (grow). It is assumed that the shape of the binding site will
help restrict the sampling of ligand conformations to those that are
most relevant for the receptor geometry.

==Workflow
for Anchor-and-Grow Algorithm==

The conformation of a flexible molecule may be
searched or relaxed using the flexible_ligand option. Only the torsion
angles are modified, not the bond lengths or angles. Therefore, the
input geometry of the molecule needs to be of good quality. A structure
generated by ZINC is sufficient.

The torsion angle positions reside in an editable
file (see flex_drive.tbl on page 111) which is identified with the
flex_drive_file parameter. Internal clashes are detected during the
torsion drive search based on the clash_overlap or internal_energy
parameters, which are independent of scoring function.

=Identification of Rigid Segments=

A flexible molecule is treated as a collection of
rigid segments. Each segment contains the largest set of adjacent atoms
separated by non-rotatable bonds. Segments are separated by rotatable
bonds.

The first step in segmentation is ring identification.
All bonds within molecular rings are treated as rigid.
This classification scheme is a first-order approximation of molecular
flexibility, since some amount of flexibility can exist in non-aromatic
rings. To treat such phenomenon as sugar puckering and chair-boat
hexane conformations, the user will need to supply each ring
conformation as a separate input molecule. Additional bonds may be
specified as rigid by the user
(see [[Manual Specification of Non-rotatable Bonds]]).

=Identification of Rigid Anchor and Flexible Bonds =

The second step is flexible bond
identification. Each flexible bond is associated with a label defined
in an editable file (see [[flex.defn]]).
The parameter file is identified with the flex_definition_file
parameter. Each label in the file contains a definition based on the
atom types (and chemical environment) of the bonded atoms. Each label
is also flagged as minimizable. Typically, bonds with some degree of
double bond character are excluded from minimization so that planarity
is preserved. Each label is also associated with a set of preferred
torsion positions. The location of each flexible bond is used to
partition the molecule into rigid segments. A segment is the largest
local set of atoms that contains only non-flexible bonds.

=Manual Specification of Non-rotatable Bonds=

Currently this functionality is not available!

The user can specify additional bonds to be
non-rotatable, to supplement the ring bonds automatically identified by
DOCK. Such a technique could be used to preserve the conformation of
part of a molecule and isolate it from the conformation search.

Non-rotatable bonds are identified in the Tripos MOL2 format file
containing the molecule. The bonds are designated as members of a
STATIC BOND SET named RIGID (see [[Tripos MOL2 Format]]).

Creation of the RIGID set can be done within
Chimera. With the molecule of interest loaded into Chimera, select the
portion of the ligand you would like to remain rigid. Then select on
File > Save MOL2. Make sure the "Write current selection to @
SETS section of file" is checked and save the file.

Alternatively, the RIGID set can be entered into
the MOL2 file by hand. To do this, go to the end of the MOL2 file. If
no sets currently exist, then add a SET identifier on a new line. It
should contain the text "@<TRIPOS>SET". On a new line add
the text "RIGID STATIC BONDS <user> **** Comment". On the
next line
enter the number of bonds that will be included in the set, followed by
the numerical identifier of each bond in the set.

=Identification of Flexible Layers=
==Anchor Selection==
An anchor segment is normally selected from
the rigid segments in an automatic fashion
(see [[Manual Specification of Non-rotatable Bonds]]
to override this behavior). The molecule is divided into segments that
overlap at each rotatable bond. The segment with the largest number of
heavy atoms is selected as the first anchor, number of attachment points are also considered. All segments with more heavy
atoms then min_anchor_size are tried separately as anchors. The number of anchors can be limited by setting the limit_max_anchors flag to "yes"; max_anchor_num is used to specify the maximum number of anchors to be used (anchors are ordered by heavy atoms and attachment points):

min_anchor_size 5
limit_max_anchors yes
max_anchor_num 5

At most 5 anchors are used and all anchors have at least 5 heavy atoms.

To use a single specific anchor (e.g scaffold with known bonding pose), specify an atom name and its corresponding atom number in the chosen fragment (e.g. if atom number 10 is C16):

user_specified_anchor yes

atom_in_anchor C16,10

==Identification of Overlapping Segments==

When an anchor has been selected,
then the molecule is redivided into non-overlapping segments, which are
then arranged concentrically about the anchor segment. Segments are
reattached to the anchor according to the innermost layer first and
within a layer to the largest segment first.

=Layered Non-Overlapping Segments=

The anchor is processed separately
(either oriented, scored, and/or minimized). The remaining segments are
subsequently re-attached during the conformation search. The
interaction energy between the receptor and the ligand can be optimized
with a simplex minimizer (see [[Minimization]]).

=Pruning the Conformation Search Tree=

Starting with version 6.1,
there are two methods for pruning.
The first method is the one that existed in earlier versions;
it is the default and corresponds to input parameter
pruning_use_clustering = yes.
In this method
pruning attempts to retain the best, most diverse configurations using
a top-first pruning algorithm, which proceeds as follows. The
configurations are ranked according to score. The top-ranked
configuration is set aside and used as a reference configuration for
the first round of pruning. All remaining configurations are considered
candidates for removal. A root-mean-squared distance (RMSD) between
each candidate and the reference configuration is computed.
Each candidate is then evaluated for removal
based on its rank and RMSD using the inequality:

If the factor is greater than
number_confs_for_next_growth, as appropriate, the candidate is removed.
Based on this factor, a configuration with rank 2
and 0.2 angstroms RMSD is comparable to a configuration with rank 20
and 2.0 angstroms RMSD. The next best scoring configuration which
survives the first pass of removal is then set aside and used as a
reference configuration for the second round of pruning, and so on.
This pruning method biases its search time towards molecules that sample a
more diverse set of binding modes. As the values of
num_anchors_orients_for_growth and number_confs_for_next_growth are
increased, the anchor-first method approaches an exhaustive search.

In the second method, the goal is to bias
the sampling towards
conformations that are close to the correct binding mode (as optimized
using a test set of experimentally solved structures). Much as the
method above, the algorithm ranks the generated poses and
conformations. Then, all poses that violate a user-defined score cutoff
are removed. To facilitate the speed of the calculation, the remaining
list is additionally pared back to a user-defined length.
In this method, the sampling is driven towards molecules that sample
closer
to the experimentally determined binding site, and the result is
a significantly less diverse set of final poses.

=Time Requirements ''

The time demand grows linearly with the
num_anchors_orients_for_growth, the number_confs_for_next_growth, the
number of flexible bonds and the number of torsion positions per bond,
as well as the number of anchor segments explored for a given molecule.
Using the notation in the [[Workflow for Anchor-and-Grow Algorithm]], the time demand can be expressed
as

where the
additional terms are:

* NA is the number of anchor segments tried per
molecule.
* NB is the number of rotatable bonds per molecule.

=Growth Tree and Statistics =

Dock uses Breadth First Search to sample the conformational space of the ligand. The tree is pruned at every stage of growth to remove unsuitable conformations. In order to be as space efficient as possible, DOCK only saves one level of growth at a time unless "write_growth_tree" is turned on. In order to construct the growth tree it was necessary to do the following: (1) Retain all levels of growth (before and after minimization) in memory. (2) Link every conformer to its parent conformer during growth. (3) While writing out the tree, the traversal starts from a fully grown ligand (leaf), moving up the branch (parent conformer) until the ligand anchor (root) is reached. Finally, the growth tree branch is printed as a multi-mol2 file starting from the anchor to the fully grown ligand, including minimizations. This newly implemented feature allows visualization of all stages of growth and optimize behavior of current DOCK routines. Note that the growth trees can easily be visualized using the Viewdock module in the UCSF chimera program. Extra information regarding conformer number, anchor number, parent conformer etc. can also be accessed directly using this tool.

Format for branch files name is as follows:

${Ligand name}_anchor${anchor number}_branch${conformer number of fully grown mol.}.mol2

e.g. LIG1_anchor1_branch4.mol2

The ligand name is that specified in the mol2 file.
The anchor number indicates what fragment or portion of the molecule was used as the anchor.
The every conformer (both partially and fully grown) is assigned a unique number.

we recommend that users cat files together and compress them.
cat *_branch*.mol2 > growth_tree.mol2; gzip growth_tree.mol2

In addition, growth statistics are printed to the output files if the verbose flag is used.

-----------------------------------
VERBOSE MOLECULE STATS

Number of heavy atoms = 30
Number of rotatable bonds = 7
Formal Charge = 1.00
Molecular Weight = 429.56
Heavy Atoms = 30
-----------------------------------
VERBOSE ORIENTING STATS :

Orienting 10 anchor heavy atom centers
Sphere Center Matching Parameters:
tolerance: 0.25; dist_min: 2; min_nodes: 3; max_nodes: 10
Num of cliques generated: 2298
Residual Info:
min residual: 0.0261
median residual: 0.3932
max residual: 0.5000
mean residual: 0.3737
std residual: 0.0935
Node Sizes:
min nodes: 3
max nodes: 4
mean nodes: 3.0070
# of anchor positions: 1000
-----------------------------------
VERBOSE GROWTH STATS : ANCHOR #1

32/1000 anchor orients retained (max 1000) t=9.06s
Lyr 1-1 Segs|Lyr 2-1 Segs|Lyr 3-2 Segs|Lyr 4-2 Segs|Lyr 5-1 Segs|
Lyr:1 Seg:0 Bond:8 : Sampling 6 dihedrals C6(C.ar) C4(C.ar) C3(C.3) C1(C.3)
Lyr:1 Seg:0 24/192 retained, Pruning: 6-score 162-clustered t=10.68s
Lyr:2 Seg:0 Bond:5 : Sampling 3 dihedrals C4(C.ar) C3(C.3) C1(C.3) N1(N.3)
Lyr:2 Seg:0 51/72 retained, Pruning: 21-clustered t=11.38s
Lyr:3 Seg:0 Bond:1 : Sampling 3 dihedrals C3(C.3) C1(C.3) N1(N.3) S1(S.o2)
Lyr:3 Seg:0 105/153 retained, Pruning: 7-score 41-clustered t=13.37s
Lyr:3 Seg:1 Bond:3 : Sampling 6 dihedrals N4(N.am) C2(C.2) C1(C.3) C3(C.3)
Lyr:3 Seg:1 86/630 retained, Pruning: 8-score 536-clustered t=23.93s
Lyr:4 Seg:0 Bond:43 : Sampling 3 dihedrals C16(C.ar) S1(S.o2) N1(N.3) C1(C.3)
Lyr:4 Seg:0 90/258 retained, Pruning: 168-clustered t=28.85s
Lyr:4 Seg:1 Bond:26 : Sampling 2 dihedrals C11(C.3) N4(N.am) C2(C.2) C1(C.3)
Lyr:4 Seg:1 147/180 retained, Pruning: 5-score 28-clustered t=35.28s
Lyr:5 Seg:0 Bond:46 : Sampling 6 dihedrals C17(C.ar) C16(C.ar) S1(S.o2) N1(N.3)
Lyr:5 Seg:0 104/882 retained, Pruning: 15-outside grid 22-score 741-clustered t=77.71s

These are the verbose growth statistics for flexible docking to 1PPH (thrombin). These are printed only when the verbose flag is enabled in the command line. This feature is useful for debugging incomplete growths and other possible issues with the growth routines. This feature is also useful to show progress when docking in larger peptide-like ligands (20+ rotatable bonds) which can take several hours. Cumulative timing in seconds (e.g. t=13.37s) is shown at the end of each line to allow quick profiling of the slowest steps during docking. A separate section is printed for each anchor sampled when using multiple anchors. For anchor #1, the orienting routine produces 1000 orients, and 37 are retained after clustering and minimization. The ligand has 7 rotatable bonds. The second line shows the assignment of layers and segments. For details on the terminology, please consult the DOCK 4 paper. subsequently, two lines of information are printed for each torsion sampled.

'''Lyr:1 Seg:0''' indicates that this is Layer #1 and Segment #0. Layer and segment number starts from zero, and corresponds to the array indices used internally. '''Bond:8 '''refers to bond number in the mol2 file read in. "Sampling 6 dihedrals C6(C.ar)  C4(C.ar)  C3(C.3)  C1(C.3)" specifies the exact torsion being sampled. Six dihedral positions are being sampled in this case, as determined by the drive_id in flex_drive.tbl. '''21/246 retained''' means 21 conformers were retained from the 246 conformers generated during growth (41 conformers x 6 dihedral positions = 246 new conformers). The '''Pruning:''' section demonstrates how these (246-21) or 225 conformers were pruned: 2 conformers were outside the energy grid, 5 conformers exceeded the score cut-off (see ''pruning_conformer_score_cutoff'') and 218 conformers were clustered. Typically clustering removes the greatest number of conformers during each torsion grown as controlled by the ''pruning_clustering_cutoff'' parameter. The reader is encouraged to verify that the number of conformers retained can be calculated as above at each stage of growth. If the growth tree is turned on, the total number of conformers stored in the growth tree are also reported.

NOTE: The following parameter
definitions will use the format below:

parameter_name [default] (value):
#description

In some cases, parameters are only needed
(questions will only be asked) if the parameter above is enforced.
These parameters are indicated below by additional indentation.

==Flexible Ligand Parameters==

flexible_ligand [yes] (yes, no):
#Flag to perform ligand conformational searching
user_specified_anchor [no] (yes no):
#Flag to let the user choose the anchor segment for growth.
atom_in_anchor [C1,1] ():
#Specify an atom within the chosen anchor segment.
limit_max_anchors [no] (yes no):
#Flag to limit the number of anchors used during multi-anchor docking.
max_anchor_num [1] ()::
#The maximum number of anchor segments to be used.
min_anchor_size [5] (int):
#The minimum number of heavy atoms for an anchor segment. Set this to a high number (40) to use a single anchor. For multi-anchor docking, use 5 or 6 (pyrrole or benzene ring respectively).
pruning_use_clustering [yes] (yes, no):
#Flag to enable clustering during pruning

(if pruning_use_clustering = yes)

pruning_max_orients [1000] (int):
#The maximum number of anchor orientations carried forward in the
#anchor & grow search (previously num_anchor_orients_for_growth)
pruning_clustering_cutoff [100] (int):
#The pruning value cutoff for anchor orientations promoted to the
#conformational search (previously number_confs_for_next_growth and
#referred to as N_c in the equation in Pruning the Conformation Search Tree)

(if pruning_use_clustering = no)

pruning_max_orients [1000] (int):
#Maximum number of anchor orientations promoted to the conformational
#search
pruning_orient_score_cutoff [25.0] (float):
#Maximum score for anchor after minimization
pruning_max_conformers [75] (int):
#Maximum number of anchor orientations promoted to the next layer of growth
pruning_conformer_score_cutoff [100.0] (float):
#Maximum score for conformation after minimization
use_clash_overlap [no] (yes, no):
#Flag to check for overlapping atomic volumes during anchor and grow
clash_overlap [0.5] (float):
#The relative threshold for overlapping atomic volumes;
#a clash exists if the distance between a pair of atoms is less than
#the clash_overlap times the sum of their atom type radii;
#thus, a clash_overlap of 0.75 allows 25% (1 - 0.75) of relative overlap.
write_growth_tree [no] (yes, no):
#Warning: Writing the growth tree increases memory usage and can generate lots of files. Concatenating and compressing growth tree branches is recommended

DOCK 6

2016-03-23T23:47:10Z

Sudipto: /* Manual */

DOCK 6 is a general purpose [[molecular docking]] program developed by the [[Kuntz Laboratory]] at [[UCSF]]. DOCK 6 (released summer 2006) replaced [[DOCK 5]] (2001-2006), which is no longer available. [[DOCK 5]] was a complete re-write of [[DOCK 4]] (1998-2002), which itself was a complete re-design and re-write of [[DOCK 3.5]] (released 1993-1994).

DOCK 6 is coded mainly in C++. Using the object oriented model, it is functionally separated into independent components (classes, methods), allowing a high degree of modularity and programming flexibility. Accessory programs are written in a variety of languages including C and Fortran 77. Source code is available for all programs. The DOCK suite of programs requires on the order of 50 MB of disk space and 512 MB RAM. Some runs may require considerably more disk space and more memory.

* [http://dock.compbio.ucsf.edu DOCK website]
* [http://dock.compbio.ucsf.edu/Online_Licensing/index.htm Licensing]
* Manual as a single PDF. [[Image:Dock61.pdf]]
* [http://dock.compbio.ucsf.edu/DOCK_6/tutorials/index.htm Tutorials]
* [http://dock.compbio.ucsf.edu/Test_Sets/index.htm Test sets]
* [[DOCK:FAQ]]

= General Overview =

DOCK addresses the problem of "docking" molecules to each other. In general, "docking" is the identification of the low-energy binding modes of a small molecule, or ligand, within the active site of a macromolecule, or receptor, whose structure is known. A compound that interacts strongly with, or binds, a receptor associated with a disease may inhibit its function and thus act as a drug. Solving the docking problem computationally requires an accurate representation of the molecular energetics as well as an efficient algorithm to search the potential binding modes.

Historically, the DOCK algorithm addressed rigid body docking using a geometric matching algorithm to superimpose the ligand onto a negative image of the binding pocket. Important features that improved the algorithm's ability to find the lowest-energy binding mode, including force-field based scoring, on-the-fly optimization, an improved matching algorithm for rigid body docking and an algorithm for flexible ligand docking, have been added over the years. For more information on past versions of DOCK, click here.

With the release of DOCK 6, we continue to improve the algorithm's ability to predict binding poses by adding new features like force-field scoring enhanced by solvation and receptor flexibility. For more information about the current release of DOCK, click here.

= What Can DOCK Do for You =

We and others have used DOCK for the following applications:

* predict binding modes of small molecule-protein complexes
* search databases of ligands for compounds that inhibit enzyme activity
* search databases of ligands for compounds that bind a particular protein
* search databases of ligands for compounds that bind nucleic acid targets
* examine possible binding orientations of protein-protein and protein-DNA complexes
* help guide synthetic efforts by examining small molecules that are computationally derivatized
* many more...

= Manual =

* [[Installing DOCK 6]]
* [[What does DOCK do?]]
* [[DOCK Accessories]]
* [[History of DOCK 6]]
* [[Command line arguments in DOCK6]]
* [[Ligand File Input]]
* [[Database Filter]]
* [[Orienting the Ligand]]
* [[Internal Energy Calculation]]
* [[Ligand Flexibility]]

[[Category:DOCK]]
[[Category:DOCK 6]]
[[Category:Software]]
[[Category:Freecom]]

Internal Energy Calculation

2016-03-23T23:46:09Z

Sudipto:

During growth and minimization, an internal energy scoring function can be used. The goal of the internal energy function is to reduce the occurrence of internal clashes during the torsional optimization. This function computes the repulsive Lennard-Jones term between all ligand atom pairs, excluding all 1-2, 1-3, and 1-4 pairs. (Currently, attractive Lennard-Jones and Coulombic terms are neglected; he aim is to eliminate internal clashes not to optimize the internal geometry. in addition, since there is no dihedral term in the force field, if the attractive terms are included the molecule might appear less physical.) The internal energy can be cut on or off; if cut on, it is all ways used and is reported in the final energy. If it is cut of it is never used. The pruning during growth is done by considering both the internal and interaction energies. We recommend the use of the internal energy function for all calculations.

=Internal Energy Parameters=

use_internal_energy [yes] (yes no): #Flag to use Internal energy (only repulsive VDW) for growth and/or minimization
internal_energy_rep_exp [12] (int): #The VDW exponent

2016-03-23T22:17:49Z

Sudipto: /* Docking Programs */

'''Molecular docking''' is the process of posing, scoring and ranking small molecules in the binding sites of proteins to prioritize compounds for aquisition and experimental testing. Typically, a large database of small molecules such as [[ZINC]] is screened using a docking program such as [[DOCK]]. The top scoring compounds are reviewed in a [[hit picking party]] and then purchased and tested experimentally. There are many molecular docking programs to choose from (see below). [[DOCK 3]] is the implementation of molecular docking and virtual screening that we develop and use at [[UCSF]].

The goal of molecular docking screens is often ligand discovery - new chemical matter than can be optimized using medicinal chemistry techniques. Another approach to ligand discovery is to simply use [[cheminformatics]] methods such as [[ZINC]] to identify purchasable compounds.

{{TOCright}}

== Docking Programs ==
There are many docking programs. All of them have been successfully used for ligand discovery. The first three are from UCSF.
* [[DOCK 3]] - latest version is [[DOCK 3.7]] - this is the version our group uses routinely.
* [[DOCK 6]] - latest version is [[DOCK 6.7]]
* [[DOCK 4]]
* [http://www.schrodinger.com Glide]
* [http://www.jainlab.org/ Surflex Dock]
* [http://www.biosolveit.de FlexX]
* [http://www.ccdc.cam.ac.uk GOLD]
* [http://www.molsolft.com ICM]
* [http://www.eyesopen.com FRED]
* [http://www.chemcomp.com MOE]
* [http://autodock.scripps.edu/ AutoDock]
If we have forgotten your program, please add it here.

The difference between the Docking category and the [[:Category:DOCK]] category is that DOCK is specific to our software, whereas Docking (this page) includes all docking programs and the approach in general.
[[Category:Software]]
[[Category:Topic]]
[[Category:Organization]]