by Max Totrov and Ruben Abagyan

Docking and virtual ligand screening. Overview.

This section concerns with predictions of interactions of drugs or small biological substrates (less than about 600-700 dalton) to pockets of larger, more rigid, receptors (typically, protein molecules, DNA or RNA). There are five major steps in docking and screening.

1. Where to dock. Building Receptor and Pocket Model

The goal here is to have an adequate three-dimensional model of the receptor pocket you are planning to dock ligands to. And the pitfalls are that your model is not accurate overall, or does not reflect the induced fit, or alternative conformations of the receptor binding pocket are missed.
Receptor from PDB
If you have only a single entry with your receptor, convert the protein with convertObject yes yes no no , after deleting water molecules and irrelevant chains (e.g. delete a_!1 ), or use menus as in the ligand docking section.
However, if you have a choice between several templates, take the following into account:

X-ray structure is preferable to an NMR structure
high resolution X-ray structure ( less than 2.1A ) is much better than, say 2.5A .
watch out for high-B-factor regions and avoid them; sometimes crystallographers deposit fantasy coordinates with high-B-factors. Use:
```
color a_//* Bfactor( a_//* )  # from command line
```
or Color/B-factor from the Gui-menu .
place polar hydrogens and choose correct form of histidine ( convertObject yes yes no no takes care of that )
a bound conformation of the receptor is preferable, however if you use an apo-model, an NMR structure or a model by homology, the side-chains in a pocket may be incorrect. Frequently they stick out and prevent a ligand from binding. Those stubborn side-chains can be 'tamed', (i) manually; (ii) by a side chain simulation with elevated surfaceTension; or (iii) by an explicit flexible docking calculation with a known ligand.

Receptor from homology modeling
A model by homology can be built with the build model command (menu Homology/Build_Model) followed macro refineModel .
Identifying pockets
If a binding pocket is not known in advance, use icmPocketFinder or icmCavityFinder (for closed pockets) macros. icmPocketFinder can also be accessed from menu Docking/Receptor Setup , submenu Identify_Binding_Sites

2. What to dock. Ligand, ligands, a database or a library.

Usually a good start is to try to dock the known ligand(s) to the receptor model. You may also want to dock a library of compounds in order to identify lead candidates. In this case the main pitfall is that the library is too restricted, molecules are not chemically feasible or not drug-like.
Ligand from PDB
Then to dock a ligand from pdb, go through the procedure described in the ligand docking section.
Ligand(s) from a mol/mol2- file, or SMILES strings.
The main prerequisite is that the formal charges and the bond types are correct. If they are not correct, you need to process each molecule manually as described in the ligand docking section. From a command line you may use the build smiles or convert2Dto3D macro.

Flexible docking considerations.

After the receptor maps are built, you will start a docking simulation. The goal of the flexible docking calculation is prediction of correct binding geometry for each binder. ICM stochastic global optimization algorithm attempts to find the global minimum of the energy function which includes five grid potentials describing interaction of the flexible ligand with the receptor and internal conformational energy of the ligand. During this process a stack of alternative low energy conformations is saved ( one of the choices in the Docking menu ). Some facts about ICM docking:

an average docking time is 2-30 seconds per ligand per processor
ICM docking performed very well in predicting the binding geometry in several comparative benchmarks.
the time per ligand was chosen to be the smallest possible to allow screening of very large data sets. To increase the time spent per ligand, change the Docking_effort parameter from the Docking.Small Set Docking Batch menu to 3. or 5., or supply this parameter to the rundock script directly.

Pitfalls. Inaccurate receptor model, or incorrectly converted ligands, or insufficient optimization effort may lead to incorrect predictions.

Scoring

The goal of scoring in virtual ligand screening is to ensure maximal separation between binders and non-binders , and not to rank a small number of binders according to their binding energies. The scores can be linearly related to binding energy estimates, but the transformation parameters need to be calculated from several reference points (see the learn command).

The vls module allows you to access a good scoring function.

Introduction and pre-requisites

ICM ligand docking procedure performs docking of the fully flexible small-molecule ligand to a known receptor 3D structure. Before setting up the docking project, ICM object of the receptor has to be created. In most cases, x-ray structure of the receptor is initially in the PDB format. Thus, it has to be converted to the ICM format. This process involves addition of the hydrogen atoms, assignment of atom types and charges from the residue templates (icm.res) and imposition of internal coordinates tree (icm-tree) on the original pdb coordinates. The easiest way to convert pdb structure into icm object is through the GUI as described in the GUI Manual or you can automate the setup in the command line using _dockBatch.

Docking project setup

Start project setup by defining the project name (GUI menu Docking/New Project ). Avoid spaces and leading digits in the name. All files related to the docking project will be stored under names, which start from the project name. Most customized parameters will be saved in the table file under the project name dockProjName.dtb as well:

DOCK1.dtb     # control table
DOCK1_rec.ob  # receptor object 
DOCK1_gb.map  # 3D potential grids, or 'maps' 
DOCK1_gc.map 
DOCK1_ge.map 
DOCK1_gh.map 
DOCK1_gl.map 
DOCK1_gs.map 
DOCK1_probe.ob # 4 atom probe for initial superposition (or)
DOCK1_tmplt.ob # template ligand (optional)

etc..
The next step is to set up the receptor (GUI menu Docking/New Project ). Select the receptor molecules, in most cases a_* will do - all molecules in the current object will be included. Define binding site residues, either manually e.g. a_/123,144,152 for selection by residue numbers, or graphically using lasso tool (don't forget to set selection level to residue). This selection is used solely to define boundaries of the docking search and the size of the grids and doesn't have to be complete, selecting some 4 residues delimiting the binding site is sufficient. Receptor setup dialog also lets you run binding site identification routine to quickly locate putative binding sites on your receptor.

The receptor setup procedure will first display the grid box, allowing you to adjust the box dimensions, and then the 'probe' which defines the initial positioning of the ligand's center of mass and long/short axis. The probe can be moved/rotated. While its positioning has only minor influence on the results as long as it remains inside the binding site, it may help the procedure to find the correct docked orientation more reliably and/or in shorter time.
After the receptor setup is complete, the program normally displays the receptor with the selected binding site residues highlighted in yellow xstick representation. Ligand setup offers a number of ways (submenu docking/ligand setup ) to define the ligand, depending on the source of the ligand structure(s).

To run the docking job from a unix shell, use the _dockScan macro with appropriate parameters, e.g.

$ICMHOME/icm -c /icm/_dockScan abl -a -S confs=10 effort=2. from=10 to=20 outdir=/tmp/ name=ou >& abl10.ou &

Automated docking project setup

The _dockBatch script streamlines the setup and execution of docking simulations, enabling automated preparation of the docking project and optional immediate launch of screening runs. This tool supports flexible definition of the binding site, ligand treatment, and docking mode through a range of command-line arguments:

$ICMHOME/icm $ICMHOME/_dockBatch <receptor.pdb|receptor.ob> [ligands2dock.sdf] [proj=projectName] <options>

e.g.

$ICMHOME/icm $ICMHOME/_dockBatch 1xbb.pdb ligmol=asti

Arguments:

ligmol= : ligand in the input structure, used to define site (or redock with -r)
pocket= : pocket selection in a_chain/resNumList format, e.g. a_b/5,8:10,115
pocket= : pocket selection will be defined by a 3D ligand from an .sdf file
box=x,y,z,X,Y,Z : site definition as a box, e.g. -1.,3,-10.,10.,13.,0. Our box definition is x,y,z,X,Y,Z which is to say two corners of the box (with lowest/HIGHEST xyz coordinates). If you want to use center Xc,Yc,Zc and size (box dimension) d you could calculate xyzXYZ as Xc - d/2 Yc - d/2 Zc - d/2 Xc + d/2 Yc + d/2 Zc + d/2
-r : re-dock mode, takes ligand from the receptor complex input
-R : same, with RMSD calculation
-u : do not automatically set protonation/charge state of the ligand
-w : delete all water in the receptor input
-e : use stack embedded in receptor as ensemble for '4D' docking
recConf= : use specific receptor conformation
-dnaChargeScale : scales down charges for phosphate group
covres= : covalent docking mode, modified residue in a_chain/resNum format
react= : table with custom covalent reaction mechanisms
reactNum= : selects reaction from multiple mechanisms in the default or custom table
template= : icm object file with ligand template to be used to bias docking
tmplMeth= : template matching method
proc=N : run multiple ligands across N threads/CPU cores in parallel

Fully Automated Optional - you can run screening automatically by defining the sdf file at the end

e.g.

$ICMHOME/icm64 $ICMHOME/_dockBatch 1xbb.pdb ligmol=asti chem_database.sdf

Note about Ionization State of Ligand setup using _dockBatch There is a setting within the docking project that controls protonation state handling. When project is set up by _dockBatch , the default is to apply built-in pKa model to determine predominant protonation state. To keep protonation as in input sdf, you can turn this off with _dockBatch -u option (or change it in .dtb file later). If you just want to see results of automatic protonation state assignment, you can load your sdf in icm and use dialog under Chemistry/Set Formal Charges

Converting a chemical from pdb.

The Protein data bank, due to unprecedented ignorance, for the last 15 years has not been storing any information about covalent bond types and formal charges of the chemical compounds interacting with proteins! This oversight makes it impossible to automatically convert those molecules to anything sensible and requires your manual interactive assignment of bond types and formal charges for each compound in a pdb-entry. Therefore, if you apply the convert command to a pdb-entry with ligands, the ligands will just become some crippled incomplete molecules which can not be further conformationally optimized.
Follow these steps to convert a chemical properly from a pdb form to an a correct icm object.

display the molecule, set wireStyle=2 (or via top-left gui-menu), and selection type to GRAPHICS.selectionMode=1 (the first item of the gui-selection-mode menu)
invoke MolMechanics.Structure.SetBondType menu item
graphically select groups of atoms (e.g. a ring) and set appropriate bond type
invoke the next menu item, MolMechanics.Structure.SetFormalCharge and set formal charges
proceed to the MolMechanics.ICM-Convert.Chemical menu (see below)

Setting up a ligand or a set of ligands
Let's now consider the situation when icm object of the ligand loaded. ICM object of the ligand can also be prepared, for instance, by reading structure from SD file (menu File/Read Molecule/Mol/SDF ) and converting it to ICM (menu MolMechanics/ICM-Convert/Chemical ).
Once the icm object of the ligand is ready, proceed to docking ligand setup (menu Docking/Ligand Setup/From Loaded ICM object ).
Ligand setup procedure can be ran repeatedly to change the ligand source within the same docking project. Also box size and probe position can be changed later (menu Review/Adjust ligand/box ). At this point, the project is ready for the calculation of maps (menu docking/Make receptor maps ). The recalculation of the maps typically requires less than 1 minute. While the map calculation dialog allows changing the grid step, we do not recommend altering the default value of 0.5 which was found optimal for a large number of test cases. Maximum van der Walls repulsion parameter can be increased if more rigorous enforcement of steric exclusion is desired With the map calculations completed, everything is ready to start the actual docking simulation. A larger set of ligands in a mol file can be considered as a database and indexed with the ICM indexing tool (menu Docking/Tools/Index Mol,Mol2 Database ) for fast access. Ligand structures from mol/mol2 file can be converted to ICM on the fly and do not require manual preparations necessary in the case of PDB structures.

Running docking simulations

Use menu docking/Small Set Docking Batch to start docking of one or few ligands in the background. You can also view the process interactively (menu docking/Interactive Docking ) although it is much slower due to the time spent on drawing the molecules. The results of the batch docking job are saved in the

PROJECTNAME_answers*.ob  #icm-object file with best solutions for each ligand 
PROJECTNAME_*.cnf        # icm conformational stack files with multiple docked conf. 
PROJECTNAME_*.ou         # output file were various messages are stored.

Multiple conformations accumulated during the docking of the ligand can be visualized and browsed in ICM (menu Docking/Browse Stack Conformations ). Use menu Docking/Display/Preferences to change default graphic representation of ligand/receptor.

Virtual Ligand Screening Introduction

Virtual Ligand Screening (VLS) in ICM is performed via docking of each ligand in the database to the receptor structure, with subsequent evaluation of the docked conformation with a binding-score function. Best-scoring ligands are then stored in the multiple icm-object file. The set-up of the VLS is largely identical to the set-up of the docking simulation (see How-to: Ligand Docking Simulations). In most cases the ligand input file will be an SDF or MOL2 file. These files need to be indexed by ICM before they can be used in VLS runs. The index is used to allow fast access to an arbitrary molecular record in a large file. Use menu Docking/Tools/Index Mol/Mol2 File/Database to generate the index, then set up the SDF/MOL2 file as a ligand source (menu Docking/Ligand Setup/From Database ). As in docking, _dockScan ICM script can be ran directly from UNIX shell/command line to start simulations.

Score Threshold

An important parameter of the VLS run is the score threshold. Docked conformation for a particular ligand will only be stored by ICM VLS procedure if its binding-score is below the threshold. Edit the project .dtb file to adjust this value:

#>r DOCK1.r_ScoreThreshold 
  -35.

The choice of the threshold can be done in two ways:

based on the scores calculated by docking known ligands. Generally, a value somewhat above typical score observed for known ligands is a good guess.
if no ligands are known, a pre-simulation can be run using ~1000 compounds from the target database. Using the resulting statistics for the scores, the threshold should be set to retain ~1% of the ligands.

Potential of mean force score

Potential of mean force calculation ( pmf ) provides an independent score of the strength of ligand-receptor interaction. The pmf-parameters are stored in the icm.pmf file and read with the read pmf s_pmfFile command. There are two types of the mf-calculation: all-to-all atoms and intermolecular mode. The mode is switched with the mfMethod preference. To enable calculation of the pmf-score, define the PROJECTNAME.r_mfScoreThreshold threshold paramter to the table:

#>r PROJECTNAME.r_mfScoreThreshold 
  999.

Other selection criteria

ICM VLS uses a number of criteria to pre-select compounds before docking. Edit the project .dtb file to change their defaults. A full description of each field in the .tab file can be found here ftp://ftp.molsoft.com/man/dockTable.pdf

#>i DOCK1.i_maxHdonors 
 5 
#>i DOCK1.i_maxLigSize 
 500 
#>i DOCK1.i_maxNO 
 10 
#>i DOCK1.i_maxTorsion 
 10 
#>i DOCK1.i_minLigSize 
 100

Parallelization

If the database size exceeds several thousand compounds, it is desirable to run a number of VLS jobs in parallel to speed up calculations. Use from= and to= options of _dockScan to start multiple jobs on different slices of the database, e.g.

icm _dockScan from=1     to=10000 MYPROJECT
icm _dockScan from=10001 to=20000 MYPROJECT
icm _dockScan from=20001 to=30000 MYPROJECT
..

Running VLS jobs in PBS UNIX cluster environment

Jobs on the Linux cluster are run through PBS queuing system. Several scripts are provided to facilitate submission of vls jobs. To submit a single job, use pbs script 'pbsrun', which is a pbs wrapper for rundock
qsub $ICMHOME/pbsrun -v"JOBARGS=-f 1 -t 1000 -o MYPROJECT"
Note that the rundock arguments go in the quotes after JOBARGS= . The qsub command is a part of PBS.
To submit multiple jobs, there is a simple shell script 'pbsscan' which executes multiple qsub's for database stripes:
$ICMHOME/pbsscan MYPROJECT 1 6000 1000
-submits 6 jobs, 1 to 1000; 1001 to 2000 ... 5001 to 6000. Currently this script only supports default rundock arguments, copy/edit to change.
The command qstat is a part of PBS and can be used to check the status of the jobs. In addition, $ICMHOME/scanstat script can be used to monitor the progress of the VLS jobs. It analyses the *.ou rundock output files.
$ICMHOME/scanstat *.ou
To delete the jobs, use PBS command qdel:
qdel 1234 # delets job number 1234

Where to find the scores of the compounds

Once the compounds are docked, if VLS option is installed, the procedure evaluates the score and stores it in the 'comment' of the ligand object. When browsing scan answers, the SCORE>... line appears for each object viewed, containing the value of the score and it's component terms. It can also be extracted from the icm object in shell using Namex( a_1. ) function, and Field() can be used to get particular component or the total: Field( Namex( a_1. ) "Score=" 1). The SCORE lines also appear in the output file and can be extracted by simple unix grep command grep SCORE *.ou
The MFScore is calculated if r_mfScoreThreshold variable is defined in the project .dtb file. It can be added manually:

#>r PROJECTNAME.r_mfScoreThreshold 
  999.

The hitlist can also be prepared by a macro. In this case the scores will be extracted.

Analysis of the results

The hits found by the screening procedure and stored in *answers*.ob files can be processed into a hitlist spreadsheet which is browsable and exportable as SDF: (menu Docking/Make Hit List... ). An older way to export hits as SD file is using (menu Docking/Tools/Export scan answers as mol ). The score and its components are stored in the resulting SD file as well. Simple analysis of the score distribution can be performed by making a histogram (menu Docking/Tools/Scan results histogram ).

To make a hitlist in GUI use Docking/Make hitlist and on the command line use _scanMakeHitlist

scanMakeHitList "DOCK1" ""//vls/DOCK1_answers*.ob" Name(Name( "//vls/DOCK1_answers*.ob"")) no no yes 0

The logical arguments at the end are:

l_import2DfromDB (no) - you can make the hitlist smaller by not saving 2D
l_makeUnique (no) - if you had made mulitple runs you can choose the highest scoring ligand
l_import3D (yes) - will import 3D coordinates
i_topScored (0) - if you had a large hitlist you can choose to import just the top hits e.g. top 1000 based on score.

Please see the GUI Manual for a description of the physics-based score (Score) and the Neural Network score (RTCCN).

Running ICM batch docking script with _dockScan

After the project, the project directory and the maps have been created, you can start docking different sets of ligands into this receptor. To run it directly by ICM instead of through an intermediate Unix shell script, use the _dockScan script.

To run the _dockScan script just run ICM and provide the script as the first argument. All _dockScan arguments need to be provided after it.

Prerequisites:Complete these steps of the Docking menu:

Receptor Setup
Make receptor maps
Ligand setup ( Note: you do not need this step if you dock directly from an .sdf file)

Modify the resulting projFile (e.g. DOCK1.dtb) to your liking if necessary.

The full syntax of the _dockScan script is the following.

 icm _dockScan [ optional arguments ] projName

The arguments could be the following

argument	comment	example
	dock according to parameters and ligand source settings from the file projName.dtb
`input=`	dock directly from an `sdf` file (other modes require input specification in the PROJECT.dtb file)
`input=`	dock peptides from an `se` file containing sequence(s) of aminoacid residues, which may have modifications (modres)
`-a`	dock and save ALL molecules, ignore filters and score threshold (default if less than 100 molecules)	icm $ICMHOME/_dockScan -a /home/dock/PROJECT
`-d`	docking only, NO scoring
`-E`	evaluates binding score for several poses, resorts them but does save all the stacks in files (convenient for screening) `-S`
`-p`	"probe" mode: dock probes from the input sdf file, encourage spacial coverage
`-r`	RIGID ligand docking
`-s`	save stacks of multiple docking poses for each ligand
`-S`	same as `-s` but reSCORE ALL saved poses, see also `-E`
`-f`	apply property filters to exclude molecules outside ranges specified in PROJECT.dtb (this is the default if there are more than 100 molecules being docked)
`confs=`	score/save only up to poses (with `-S/-E`)
`-n`	generate and score near-native conformation as provided in input
`-Cn`	explicit option to auto assign formal charges according to NN pKa model
`from=` `to=`	range of molecules from an indexed .sdf file or molcart ids	from=1 to=10000
`jobs=`	spaws `n` processes, default is 1
`name=`	the result file with poses will be named accordingly (the default is 'answers')
`outdir=`	directory for the output files
`output=`	process answers.ob into a hitlist sdf file at the end of the run
`seed=`	random seed from the previous docking to reproduce the results exactly
`effort=`	a 'thoroughness' factor that allows one to extend the docking effort or reduce it (default is 1., reasonable range from 0.5 to 20. )
`proc=`	run specified number of parallel jobs (eg use #cores )
`scoreCutoff=`	accept ligs with score better than Default: -32. (from .dtb file)

Example:

Docking an sdf file (first configure the receptor, make the grid maps and setup the ligand input source in GUI).

icm _dockScan /home/gpcr/PROJECTNAME -a -S confs=10 effort=3.

This will dock all compounds with 3-fold longer (more thorough) simulations, and rescore up to 10 conformations per ligand.

If you have a cluster license without graphics you will need to use -vlscluster flag after calling icm.

icm -vlslcuster  _dockScan /home/gpcr/PROJECTNAME -a -S confs=10 effort=3.

Template constrained docking

It is possible to use a template object to tether part of the ligand structure to a preferred position during the docking simulation. Prepare an object file containing the group of atoms to be tethered to. Edit the .dtb docking setup file, setting the s_templateObj field to the name of the template object file (it is 'none' by default). The variable l_superByName controls the way correspondence is established between the ligand and template atoms. If it is 'no', chemical substructure search is performed and tethers imposed according to the substructure match. If l_superByName is 'yes', simple matching according to atom names is performed. Tethers can be individually weighted by assigning b-factor values to the template atoms. Weights are reversely proportional to b-factor, default b-factor of 20. corresponds to the weight of 1.

Running VLS via SLURM or SGE

In the /bin directory you will find a script called docksub.icm. This script prepares your docking run and distributes it via SGE or SLURM job queueing system on your cluster or cloud.

use:

$ICMHOME/icm  docksub.icm  -vlscluster <chemTable.sdf|.inx> {<dockProj>|<APF_template.mol>} [jobs=100] [-sub] [-apf] [qtype=sge|slurm] [<dockProj_and_dockScan_options>|<APF_options>]

e.g. submit 100 slurm jobs

icm64 docksub.icm qtype=slurm chemTable.inx DOCK1 jobs=100 –sub

or submit 2 slurm jobs and each one will use 18 cores

icm64 docksub.icm input.inx DOCK1 jobs=2 proc=18

Ligand AIDE

Ligand AIDE is a de novo ligand generation workflow based on Artificial Intelligent Design Evolution. It uses an iterative evolutionary strategy to grow and optimize ligands directly in the context of a protein binding site.

The process begins with a population of docked small fragments. From this starting point, new designs are generated by adding or replacing R-groups using the groupGen neural network, as well as by performing atom-level substitutions using the Atom Predictor. Each newly generated cohort of compounds is then re-docked into the binding site.

Designs are refined through multiple evolutionary cycles, typically three to five iterations. At each step, a Darwinian selection process removes poor designs based on several criteria, including RTCNN scores, physics-based binding scores with penalty terms, drug-like property filters, and synthesizability.

Through successive generations, fragments evolve into more complete and optimized ligands. This iterative grow-and-select strategy enables efficient exploration of chemical space while maintaining binding quality, drug-like behavior, and practical synthetic feasibility.

To run:

Setup the docking project.
Use the dndAI.icm script (see below) in $ICMHOME/bin

Usage> icm64 dndAI.icm prj= [ngen=4] [ngengr=10] [ncand=50] [synth=0.5] seed=[custom_answers.ob] [-f]

prj= Docking Project Name
ngen= This option controls the size of the result ligand. Default (4) is generally for mid-size ligands. For large pockets the value can be increased.
ncand= This option defines how many ligands are retained and evolved in each generation, balancing chemical diversity against runtime.
synth= Sets the minimum synthesizability score required for ligands to survive selection, enforcing practical synthetic feasibility (0= not synthesizable to 1= fully synthesizable).
seed= To provide custom starting ligands you can use seed= - the .ob file is the answer file containing the 3D ligand conformations from a docking run in ICM.

GINGER

GINGER - Graph Internal-coordinate Neural-network conformer Generator with Energy Refinement

Overview:

Conformer generation is an essential step of a variety of molecular modeling and computer-assisted drug discovery workflows such as 3D ligand-based virtual screening or fast GPU docking. GINGER (Graph Internal-coordinate Neural-network conformer Generator with Energy Refinement) is Molsoft's new cutting-edge software designed for ultra-rapid high quality conformer library generation on GPUs.

RIDGE

RIDGE - Rapid Docking GPU Engine

RIDGE (Rapid Docking GPU Engine) is an extremely fast and accurate structure-based virtual ligand screening method.

RIDGE combines MolSoft's experience and knowledge in GPU programming to create a docking engine that fully runs on the GPU. The central part of the engine is fully RAM and GPU optimized, and it also utilizes low-level CPU multithreading to ensure a balanced workload between the CPU and GPU. Additionally, it is fully compatible with existing ICM docking projects, requiring no extra setup. Read More...

To run you RIDGE you first need to setup a docking project either automated in the command line or via the GUI.
You need to download one or more conformer databases from here
To run RIDGE on the command line use the _ridge script in the $ICMHOME directory. See the syntax below:

$ICMHOME/icm $ICMHOME/_ridge DockingProjectName <projFile> output= .sdf [<options>] input= DB_3D_Confs.molt

Options:

confs= : score/save only up to top poses (with -S/-E)
randomSelect= : dock N random compounds (default - 0, dock everything )
fr=,to=: dock range i_fr/i_to (default, dock everything )
index=,,... : dock specific ID numbers
output=<.sdf> : process answers.ob into a hitlist sdf file or icb at the end of the run
gpuid= : use specific GPU (default -1) Note that GPU ids start with 0. You can check available devices with nvidia-smi command line. -1 means “auto” first GPU with CUDA of required capability available.
scoreCutoff=: accept ligs with score better than Default: -25
clashWeight= : clash penalty weight (default 20.)
clashScale= : clash van der Waals scale (default 0.82)
threads= : GPU threads per block (default 1024)
-C : perform cartesian optimization for best poses
-S : rescore with physics-based ICM VLS score

We recommend to use if possible a most recent NVIDIA GPU. For optimal performance you would use a GEFORCE 4090 but RIDGE will run on older versions (e.g. 3090 2080).

Please note multiple .molt files can be screened using comma separated filenames.

Example

Dock 1000 random compounds from /path/to/conf_db.molt with Cartesian minimization (-C)

$ICMHOME/icm64 $ICMHOME/_ridge <mydockProj> -C input=/path/to/conf_db.molt randomSelect=1000

CombiRIDGE

CombiRIDGE is a new innovative GPU-accelerated solution for high-throughput ligand docking and screening which leverages MolSoft's generative neural network conformer enumeration method GINGER, ultra-fast GPU docking technology RIDGE and advanced graph neural network scoring RTCNN. The approach allows you to optimize specific R-groups and screen vast ultra-large or combinatorial libraries efficiently.

icm <dockProj> [input=<MEL.sdf] [space=<molsoft_space.icb>] [output=<.sdf|.icb] [fr=<i_fragFrom>] [to=<i_fragTo>] [mnhits=<N_tophits>] [mnmol=<N_Enumeration_Limit>] [effort=1.] [-S] [-C]

input: accepts RIDGE or VLS results from MEL screening
space: ICB file with space which corresponds to the input MEL
output: result SDF or ICB

Options:

fr=/to= : Explicitly specify which fragments to enumerate and optimize
mnmol= : maximum total_number_of compounds to enumerate (10M default)
mnhits= : Number of top best hits (by RTCNN) to keep and save. They can optionally be re-socred by VLS score
clashWeight= : RTCNN clash weight (default 20.)
effort= : Thoroughness of conformation generator (GINGER)
-S: Re-score top with full atom VLS score
-C: auto set formal charges
-filter: apply structural and property filters
-f: force overwriteing result file if exists
threads= : Default 256 is optimal for RTX 4090
strain={yes/no} : calculate free strain based on torsion statistical profile (experimental)

V-SYNTHES

V-SYNTHES is a method that allows you to screen the Enamine REAL Space library (~21 Billion Chemicals). Read more here. V-SYNTHES requires an additional license - once the license is acquired MolSoft will send you the following files:

scripts.zip # scripts and protocol description
markush.zip # Enamine enumeration stuff
files_MEL_example.zip # small example Minimal Enumeration Library (MEL) file for testing if you have a trial license
files_MEL_all.zip # full MEL

Unpack the files:

unpack scripts.zip and copy eveything into working folder ( e.g: 'vsyn_example' )
unpack markush.zip and MEL library

Create a link to MEL library and Markush

cd vsyn_example
ln -s /path/to/files_MEL_example files   # or files_MEL_all
ln -s /path/to/markush markush

Create your docking Project

You can setup your docking project files in the GUI or fully automated in the command line using _dockBatch

STEP 1 - Minimal Enumeration Library (MEL) Docking
Create directory 'run' in the main 'vsyn_example'

mkdir run

Copy docking project files to the 'run' directory .dtb _rec.ob _*.map

Submit docking job(s) in 'run' directory Single Machine

$ICMHOME/icm64 _dockScan <dockproj> input=../files/files_MEL_275592_.inx effort=2. scoreCutoff=-18. name=files_MEL_275592_ proc=<n_parallel>
<c>
$ICMHOME/icm64 _dockScan <dockproj> input=../files/files_MEL_2comp_.inx effort=2. scoreCutoff=-18. name=files_MEL_2comp_ proc=<n_parallel>

Cluster using docksub.icm script in /bin directory

$ICMHOME/icm64 docksub.icm ../files/files_MEL_275592_.inx <dockproj> jobs=<n_jobs> effort=2. scoreCutoff=-18. name=files_MEL_275592_ proc=<n_proc>

$ICMHOME/icm64 docksub.icm ../files/files_MEL_2comp_.inx <dockproj> jobs=<n_jobs> effort=2. scoreCutoff=-18. name=files_MEL_2comp_  proc=<n_proc>

At the end of this stage you expect multiple answer files in the 'run' directory.

_files_MEL_275592_*.ob _files_MEL_2comp_*.ob

STEP 2.1 (Load and Process Hits)

cd ..   #  go to main 'vsyn_example' directory)
$ICMHOME/icm64 icm_load_hits_enamine_CARC_062021.icm
bash CapSelectMP_full.sh

STEP 2.2 (Enumerate)

# Default number of output molecules is 2500000
# This can be changed by editing script icm_enumerate_REAL_frags_112021_chunkify_charge_CARC.icm
# MAX_MOLS="2500000"

$ICMHOME/icm64 icm_enumerate_REAL_frags_112021_chunkify_charge_CARC.icm

# Result files will be placed into

run/processing_files/enumerated_best_frags_<dockproj>__<MAX_MOLS>/*.sdf

STEP 3 (Dock final hits)

concatenate individual SDF into final docking selection

cat run/processing_files/enumerated_best_frags_<dockproj>__<MAX_MOLS>/*.sdf > run/enumerated_best_frags_all.sdf

Index and Dock run/enumerated_best_frags_all.sdf to get final poses

GigaScreen

The GigaScreen method combines machine learning and deep learning tools to tackle the computational intensity of screening very large chemical databases. To overcome these challenges several protocols are employed:

MolSoft's RIDGE as a structure-based docking engine.
MolSoft's neural network scoring function RTCNN.
Chemical GCNN model.