MODEL / 01
AI Model for Antibody Design
Supports antibody candidate generation, experimental screening and iterative optimization.
05AI-ENABLED R&D
Two biopharma AI models driven by wet-lab data
The AI platform connects computational design, experimental screening and data feedback in a continuously iterating R&D workflow for antibody and oligonucleotide candidates.
MODEL / 02
AI Model for Oligonucleotide Design
Supports oligonucleotide candidate generation, activity assessment and sequence prioritization.
PROPRIETARY AI / INTERNALExperimental data drive candidate design
Experimental data drive candidate design
and continuous iteration
Built on internal experimental data, the platform supports candidate design, screening and evaluation, and feeds validation results into subsequent model iterations.
AIcandidate design
LABexperimental validation
IPcore information confidential
Data-driven candidate design
Internal R&D data support candidate generation, ranking and prioritization.
Experimental feedback and model iteration
Stage-gated experimental results inform the next round of candidate design and evaluation.
Design+Screen+Validate→Iterate
ANTIBODYAntibody candidate design
Supports candidate generation, screening and experimental validation.
OLIGONUCLEOTIDEOligonucleotide candidate design
Supports candidate generation, activity assessment and prioritization.
Specific targets, model architecture, training data and scoring methods are undisclosed. For internal research and R&D use only.
Model design→Experimental screening→Data feedback→Model iteration