3D Hitlist Clustering

Category Paths

Follow one of these paths in the Orion user interface, to find the floe.

Description

This floe clusters datasets using 3D OEShape similarity calculations and modified directed sphere exclusion clustering that uses the user-provided score field for sorting and ranking output clusters.

You must provide 3D molecules on the input records, or the floe will fail. If multiple conformers are provided, only the active conformer will be used in the floe.

Promoted Parameters

Title in user interface (promoted name)

Advanced: Cube Memory Allocation

Memory for Report Cube (report_memory): For large datasets, try increasing the memory limit.

  • Type: decimal

  • Default: 8000

Outputs

Cluster Cores (cores): Representative members, one for each cluster

  • Type: dataset_out

  • Default: 3D_hitlist_cluster_cores

Failed Records (failed): Dataset with failed records.

  • Required

  • Type: dataset_out

  • Default: 3D_hitlist_cluster_failed

Cluster Members (members): Name of output member dataset, containing all cluster members.

  • Required

  • Type: dataset_out

  • Default: 3D_hitlist_cluster_members

Floe Report Name (cluster_report_name):

  • Type: string

  • Default: 3D_hitlist_cluster_report

Singletons (singletons): Name of output singletons dataset, containing clusters with only one member.

  • Type: dataset_out

  • Default: 3D_hitlist_cluster_singletons

Advanced: Large Scale Clustering Cubes

Memory (MB) for Cluster Head Generation (gen_heads_memory_mb): Memory (in MB) allocated to serial cube that generates cluster heads.

  • Type: decimal

  • Default: 8000

Memory (MB) for Parallel Sphere Exclusion (dse_memory_mb): Memory (in MB) allocated to parallel sphere exclusion cube that assigns members to clusters.

  • Type: decimal

  • Default: 8000

Sphere Exclusion Item Count (dse_item_count): Number of records processed at a time, by parallel sphere exclusion cube. Each record creates a unit of work, which is comparing similarity of all cluster heads found so far to a single member to be assigned.

  • Type: integer

  • Default: 10

Cluster Head Percentage (head_percentage): Ratio of cluster heads per cycle, to batch size

  • Type: decimal

  • Default: 0.01

Minimum Batch Size (batch_size_floor): Minimum batch size for generate heads cube.

  • Type: integer

  • Default: 20

Minimum Cluster Heads Per Cycle (num_clusters_per_cycle_floor): Minimum cluster heads found per cycle, for generate heads cube.

  • Type: integer

  • Default: 1

Batch Size Percentage (starting_batch_percentage): Starting ratio of batch size to total input size

  • Type: decimal

  • Default: 0.01

3D Similarity Calculation

3D Similarity Score Function (score_type):

  • Type: string

  • Default: Tanimoto Combo

  • Choices: [‘Shape Tanimoto’, ‘Color Tanimoto’, ‘Tanimoto Combo’]

Align Molecules (use_align): If set to True, molecules will be aligned before similarity calculation; otherwise, they will retain input coordinates.

  • Type: boolean

  • Default: True

  • Choices: [True, False]

Similarity Score Cutoff (sim_cutoff): Similarity scores below this value will be calculated as 0

  • Type: decimal

  • Default: 0.05

Sphere Exclusion Radius (sphere_exclusion_radius): Radius from cluster head, used to determine that head’s cluster members. This corresponds to a distance metric, or 1.0 - similarity_score. Scores are normalized from 0 to 1. For example, a TanimotoCombo similarity score of 1.5 is normalized to a score of 0.75 and a distance of 0.25. A larger radius will generally result in fewer clusters with more members, and a smaller radius will result in more clusters with fewer members.

  • Required

  • Type: decimal

  • Default: 0.5

Required: Sort Input Using Score Field

Score Field (rank_field): Score field to be used for sorting during sphere exclusion and analyzed in floe report.

  • Required

  • Type: field_parameter

Score Sort Order (sort_order): Sort order for scores. Descending means higher scores are more desirable. Ascending means lower scores are more desirable.

  • Type: string

  • Default: Descending

  • Choices: [‘Descending’, ‘Ascending’]

Inputs

Input Dataset (data_in): The dataset(s) to read records from

  • Required

  • Type: data_source

  • Default: data_in

Cluster Batch Size (batch_size): Batch size for clustering. Set to 1% of the number of input records for optimal results.

  • Required

  • Type: integer