Gigadock Warp

Description

Approximates a full Gigadock run with a mixture of FastROCS and docking.

  1. Dock a random subset of molecules

  2. Cluster top docked molecules and select top scoring cluster heads

  3. Runs FastROCS on all input molecules using top scoring poses from the previous step as queries

  4. Re-dock the top scoring molecules from FastROCS

  5. Output Hit List of top scoring docked molecules.

Details

Title : Gigadock Warp
Tags : Large Scale Floes Giga-Docking FRED HYBRID Docking Chemgauss4 Virtual Screening
Python Name : #12_warp_dock_floe

Parameters

Inputs

  • Design Unit or Receptor Dataset(s) Dataset with the design unit (DU) (or old format receptor) to dock to. Multiple design units are allowed up to a limit of 10 for the Hybrid dock method (see ‘Docking Method’ parameter) and 2 otherwise. The behavior with multiple design units depends on the docking method. For ‘Fred’ or ‘FastFred’ each molecule will be docked to each design unit and the results from the best scoring design unit will be outputted, thus docking time (and cost) will scale roughly linearly with the number of design units. For ‘Hybrid’ each molecule will be docked only into to the design unit with the crystallographic bound ligand most similar (by ROCS Combo Tanimoto) to the molecule being docked, and docking time (and cost) will increase roughly by roughly 5% per addition design unit.
    Type : data_source
    Required : True
    Python Name : init_input_dataset
  • Input Conformer Collection Input collection containing molecules to dock. The collection should have been created by the ‘Prepare Giga Collections’ floe. Several large pre-generated 3rd party vendor docking collections can be made available in to your organization upon request at no charge by e-mailing support@eyesopen.com (if your organization has already requested them you will already have to these pre-generated collections). The collection will be located in the ‘Organization Data->OpenEye Data->Gigadocking Collections’ folder which also automatically contains several smaller collections and collections containing random subsets of the larger vendor collections.
    Type : collection_source
    Required : True
    Python Name : molecule_input_collection

Outputs

  • Hit List Dataset Output dataset with the top scoring docked molecules.
    Type : dataset_out
    Required : True
    Default : Gigadock Warp Hit List
    Python Name : hit_list_output_dataset
  • Queries Output dataset with the queries used by FastROCS. The queries are the cluster heads of the top scoring poses from the initial docking of a random subset of molecule from the input collection.
    Type : dataset_out
    Required : True
    Default : Gigadock Warp Queries
    Python Name : queries
  • Output Design Unit(s) Dataset Output dataset containing a copy of the design unit(s) docked to.
    Type : dataset_out
    Required : True
    Default : Gigadock Warp Design Unit
    Python Name : output_design_units_dataset
  • Temporary Collections This temporary collection is used by the floe during the run and automatically deleted at the end of the run.
    Type : collection_sink
    Required : True
    Default : Gigadock Warp Temporary Collection
    Python Name : temporary_collections

Options

  • Hit List Size Size of the final hit list with the top scoring docked molecules.
    Type : integer
    Required : True
    Default : 10000
    Range : 1 to 100000
    Python Name : hit_list_size
  • Docking Method Docking method to use. ‘Fred’ is the default structure based scoring method. ‘Hybrid’ biases the the docking towards poses that overlay the crystallographic ligand (the design unit(s) must have a bound ligand). ‘FastFred’ is a faster variant of ‘Fred’ (typically ~2x faster for single design units) that samples less and uses a simpler scoring function in the initial stages of docking.
    Type : string
    Required : False
    Default : Fred
    Choices :Fred, Hybrid, Fast Fred
    Python Name : docking_method

Options: Advanced

  • Random Dock Fraction Fraction of molecule from the input collection(s) to select at random and dock. The top scoring poses from this docking will be clustered and the top cluster heads used as queries for FastROCS
    Type : decimal
    Required : True
    Default : 0.02
    Range : 0.0 to 0.1
    Python Name : random_dock_fraction
  • Final Dock Fraction The number of top scoring molecules from FastROCS that are passed to the final docking step is equal to this fraction of the size of the input collection(s)
    Type : decimal
    Required : False
    Default : 0.08
    Range : 0.0 to 0.1
    Python Name : final_dock_fraction
  • Number of FastROCS Queries Number of top scoring molecules from the docking of the random subset of collection molecules to use as queries for FastROCS
    Type : integer
    Required : True
    Default : 50
    Min Value : 1
    Python Name : number_of_fastrocs_queries
  • Cluster FastROCS Queries If False the queries for FastROCS will be the top scoring molecules from docking a random subset of the molecules. If True the queries for FastROCS will be the top scoring cluster HEADS from docking a random subset of molecule.
    Type : boolean
    Required : True
    Default : False
    Choices :True, False
    Python Name : cluster_fastrocs_queries

GPU Hardware

This parameters control the AWS instance type the FastROCS Cube will use. There is in general no reason to adjust these. They are exposed because overall demand for GPU instances on AWS has occasionally been very high and this has lead to extremely long run times for this floe as it waits for GPU instances in some circumstances.

  • FastROCS Instance Type The instances excluded by default are known to be not cost effective for FastROCS.
    Type : string
    Required : False
    Default : !g4dn.metal,!g5.12xlarge,!g5.24xlarge,!g5.48xlarge,!g4dn.12xlarge,!g3s.,!p3.
    Python Name : fastrocs_instance_type
  • Spot instance policy for FastROCS GPU Instance. To run on SPOT instances use the default setting of ‘preferred’. To run on ON-DEMAND instances set the value to ‘prohibited’. ON-DEMAND instances typically cost x3-4 more than SPOT instances, but are more available than SPOT instances when overall demand for GPUs on AWS is high.
    Type : string
    Required : False
    Default : Required
    Choices :Allowed, Preferred, NotPreferred, Prohibited, Required
    Python Name : spot_instance_policy_for_fastrocs_gpu_instance

Output Fields

These parameters allow the user to change the default output fields this floe creates in the output datasets and/or collections. Note that parameters identifying a molecule field are special. If a molecule field is left empty the floe writes the molecule to the primary (i.e., default) molecule field of the record. The primary molecule of a dataset can be identified in the UI by looking for star on its field badge. CAUTION: If these parameters are modified the modifications must also be applied to the input fields of downstream floes that read fields written by this floe. If the downstream floe does not support specifying the input field then they may not work properly with the output of this floe if these settings are modified.

  • Docked Pose Field Field on the output hit list containing the pose of the docked molecule. If unspecified the primary molecule field will be used.
    Type : field_parameter::mol
    Required : False
    Python Name : docked_pose_field
  • Docked Score Field Field on the output record where the docked score will be placed
    Type : field_parameter::float
    Required : False
    Default : Chemgauss4
    Python Name : score_field
  • Steric Score Field Output field with the steric score component of the docked molecule. This field will only be created on the output records if this parameter is specified.
    Type : field_parameter::float
    Required : False
    Python Name : steric_score_field
  • Clash Score Field Output field with the clash score component of the docked molecule. This field will only be created on the output records if this parameter is specified.
    Type : field_parameter::float
    Required : False
    Python Name : clash_score_field
  • Protein Desolv Score Field Output field with the protein desolvation score component of the docked molecule. This field will only be created on the output records if this parameter is specified.
    Type : field_parameter::float
    Required : False
    Python Name : protein_desolv_score_field
  • Ligand Desolv Score Field Output field with the ligand desolvation score component of the docked molecule. This field will only be created on the output records if this parameter is specified.
    Type : field_parameter::float
    Required : False
    Python Name : ligand_desolv_score_field
  • Ligand Desolv HB Score Field Output field with the ligand desolvation hydrogen bond score component of the docked molecule. This field will only be created on the output records if this parameter is specified.
    Type : field_parameter::float
    Required : False
    Python Name : ligand_desolv_hb_score_field
  • Hydrogen Bond Score Field Output field with the hydrogen bond score component of the docked molecule. This field will only be created on the output records if this parameter is specified.
    Type : field_parameter::float
    Required : False
    Python Name : hydrogen_bond_score_field
  • Design Unit ID Field Output field with with the ID of the design unit the molecule scores best in
    Type : field_parameter::int
    Required : False
    Default : Design Unit ID
    Python Name : design_unit_id_field
  • Design Unit Link Field Output field with a Link to the design unit the molecule scores best in
    Type : field_parameter
    Required : False
    Default : Design Unit Link
    Python Name : design_unit_link_field
  • FastROCS Overlay Field Field on the output hit list containing the best FastROCS overlay onto the query pose with the highest tanimoto of any of the query poses. The query poses are generated by the floe by docking a random subset of the initial collection(s) and selecting the top scoring poses as queries for FastROCS.
    Type : field_parameter::mol
    Required : False
    Default : FastROCS Overlay
    Python Name : fastrocs_overlay_field
  • FastROCS Query Field Field on the output hit list containing the query pose the docked pose best overlayed onto with FastROCS. The query poses are generated by the floe by docking a random subset of the initial collection(s) and selecting the top scoring poses as queries for FastROCS.
    Type : field_parameter::mol
    Required : False
    Default : FastROCS Query
    Python Name : fastrocs_query_field
  • Combo Tanimoto Field Name of the field with the FastROCS Combo Tanimoto Score.
    Type : field_parameter::float
    Required : False
    Default : FastROCS Combo Tanimoto
    Python Name : combo_tanimoto_field
  • Shape Tanimoto Field Name of the field with the FastROCS Shape Tanimoto Score.
    Type : field_parameter::float
    Required : False
    Default : FastROCS Shape
    Python Name : shape_tanimoto_field
  • Color Tanimoto Field Name of the field with the FastROCS Color Tanimoto Score.
    Type : field_parameter::float
    Required : False
    Default : FastROCS Color
    Python Name : color_tanimoto_field
  • Bemis Murcko Field Output field for the Bemis Murcko core SMILES.
    Type : field_parameter::string
    Required : False
    Default : Bemis Murcko SMILES
    Python Name : bemis_murcko_field
  • Bemis Murcko ID Field Output Field with an integer ID of the Bemis Murcko core. All molecules with the same Bemis Murcko core SMILES will have the same ID, and those with different Bemis Murcko core SMILES will have different IDs. The IDs starts at 1 and increments by 1 each time a new Bemis Murcko core is seen. Thus this integer ID identifier depends on the order the records are passed unlike the Bemis Murcko core SMILES itself.
    Type : field_parameter::int
    Required : False
    Default : Bemis Murcko ID
    Python Name : bemis_murcko_id_field
  • Bemis Murcko Rank Field Integer Field with the rank of the molecule within its Bemis Murcko family (i.e., the rank the molecule would have if the if the hit list contained only the molecules with the same Bemis Murcko core SMILES)
    Type : field_parameter::int
    Required : False
    Default : Bemis Murcko Rank
    Python Name : bemis_murcko_rank_field
  • Hetero Bemis Murcko Field Output field for the Hetero Bemis Murcko core SMILES.
    Type : field_parameter::string
    Required : False
    Default : Hetero Bemis Murcko
    Python Name : hetero_bemis_murcko_field
  • Hetero Bemis Murcko ID Field Output Field with an integer ID of the Hetero Bemis Murcko core. All molecules with the same Hetero Bemis Murcko core SMILES will have the same ID, and those with different Hetero Bemis Murcko core SMILES will have different IDs. The IDs starts at 1 and increments by 1 each time a new Hetero Bemis Murcko core is seen. Thus this integer ID identifier depends on the order the records are passed unlike the Hetero Bemis Murcko core SMILES itself.
    Type : field_parameter::int
    Required : False
    Default : Hetero Bemis Murcko ID
    Python Name : hetero_bemis_murcko_id_field
  • Hetero Bemis Murcko Rank Field Integer Field with the rank of the molecule within its Hetero Bemis Murcko family (i.e., the rank the molecule would have if the if the hit list contained only the molecules with the same Hetero Bemis Murcko core SMILES)
    Type : field_parameter::int
    Required : False
    Default : Hetero Bemis Murcko Rank
    Python Name : hetero_bemis_murcko_rank_field