DeepViscosity API

The DeepViscosity API provides deep learning-based prediction of monoclonal antibody viscosity classes. This API is based on the DeepViscosity project, which uses an ensemble deep learning ANN model to predict high-concentration monoclonal antibody viscosity classes (Low ≤ 20 cP, High > 20 cP).

The API supports:

  • Viscosity Classification: Predicts whether a monoclonal antibody will have Low (≤ 20 cP) or High (> 20 cP) viscosity
  • Spatial Descriptors: Provides 30 spatial properties from the DeepSP surrogate model
  • Batch Processing: Process multiple antibodies in a single job

Command Line Interface

Examples

Predict viscosity classes for monoclonal antibodies

lev engine submit deep-viscosity --input-csv input.csv

Flags

  • --input-csv (str) (Required)
    • Path to the input CSV file containing antibody sequences
    • The CSV file must have the following columns:
      • Name: Antibody identifier/name
      • Heavy_Chain: Heavy chain amino acid sequence
      • Light_Chain: Light chain amino acid sequence
    • Example format: 
        Name,Heavy_Chain,Light_Chain
  mAb1,EVQLVESGGGLVQPGRSLRLSCAASGFTFDDYAMHWVRQAPGKGLEWVSAITWNSGHIDYADSVEGRFTISRDNAKNSLYLQMNSLRAEDTAVYYCAKVSYLSTASSLDYWGQGTLVTVSS,DIQMTQSPSSLSASVGDRVTITCRASQGIRNYLAWYQQKPGKAPKLLIYAASTLQSGVPSRFSGSGSGTDFTLTISSLQPEDVATYYCQRYNRAPYTFGQGTKVEIK
  • --comment (str) (Optional)
    • Optional comment to associate with the job

Python Interface

Examples

Predict viscosity classes for monoclonal antibodies:

from engine import EngineClient

client = EngineClient()
client.authorize()

job_id = client.submit_deep_viscosity(
    input_csv="input.csv"
)

Predict viscosity classes with custom comment:

job_id = client.submit_deep_viscosity(
    input_csv="input.csv",
    comment="High concentration mAb viscosity prediction"
)

Parameters

  • input_csv (str) (Required)
    • Path to the input CSV file containing antibody sequences
    • Must follow the same format as described in the command line interface
  • comment (str) (Optional)
    • Optional comment to associate with the job

Outputs

  • DeepViscosity_classes.csv (CSV file)
    • Contains the predicted viscosity classes for each input antibody
    • Columns include:
      • Name: Antibody identifier from input
      • Viscosity_Class: Predicted class (Low or High)
      • Additional metadata and confidence scores
  • DeepViscosity_descriptors.csv (CSV file)
    • Contains the 30 spatial descriptors from the DeepSP surrogate model
    • These descriptors are used as features for the viscosity prediction model

Processing Time

Typical processing times vary by the number of antibodies in the input file, but are generally fast due to the efficient deep learning architecture.

Input Format

The input CSV file must contain exactly three columns:

  1. Name: A unique identifier for each antibody (e.g., “mAb1”, “Antibody_A”)
  2. Heavy_Chain: The complete heavy chain amino acid sequence
  3. Light_Chain: The complete light chain amino acid sequence

Example input file:

Name,Heavy_Chain,Light_Chain
mAb1,EVQLVESGGGLVQPGRSLRLSCAASGFTFDDYAMHWVRQAPGKGLEWVSAITWNSGHIDYADSVEGRFTISRDNAKNSLYLQMNSLRAEDTAVYYCAKVSYLSTASSLDYWGQGTLVTVSS,DIQMTQSPSSLSASVGDRVTITCRASQGIRNYLAWYQQKPGKAPKLLIYAASTLQSGVPSRFSGSGSGTDFTLTISSLQPEDVATYYCQRYNRAPYTFGQGTKVEIK
mAb2,EVQLVESGGGLVQPGGSLRLSCAASGFTFSDSWIHWVRQAPGKGLEWVAWISPYGGSTYYADSVKGRFTISADTSKNTAYLQMNSLRAEDTAVYYCARRHWPGGFDYWGQGTLVTVSA,DIQMTQSPSSLSASVGDRVTITCRASQDVSTAVAWYQQKPGKAPKLLIYSASFLYSGVPSRFSGSGSGTDFTLTISSLQPEDFATYYCQQYLYHPATFGQGTKVEIK

References

Citation

Lateefat A. Kalejaye, Jia-Min Chu, I-En Wu, Bismark Amofah, Amber Lee, Mark Hutchinson, Chacko Chakiath, Andrew Dippel, Gilad Kaplan, Melissa Damschroder, Valentin Stanev, Maryam Pouryahya, Mehdi Boroumand, Jenna Caldwell, Alison Hinton, Madison Kreitz, Mitali Shah, Austin Gallegos, Neil Mody and Pin-Kuang Lai (2025). Accelerating high-concentration monoclonal antibody development with large-scale viscosity data and ensemble deep learning. mAbs, 17(1). https://doi.org/10.1080/19420862.2025.2483944

Updated: