At BioPlus, Clickmab Uses AI to Decode a $2B Upfront TCE Molecule

This BioPlus Antibody Annual Conference was themed "Antibody 3.0," focusing on next-generation antibody technologies:
· TCE / MCE
· Bispecifics, multispecifics, and conditionally activated antibodies
· In-vivo CAR-T
· Next-gen ADC
· AI-assisted antibody R&D

As an innovation platform at the intersection of AI and antibody R&D, Clickmab continues to explore the value of AI in antibody discovery, engineering optimization, target analysis, and R&D decision making, aiming to advance antibody development into a new stage of collaborative innovation through more precise, efficient, and accessible intelligent tools.

At this conference, Tianyuan Wang, CEO of Clickmab, delivered a keynote titled "How to Design a $2B Upfront TCE Molecule Using an AI Agent Platform." Drawing on the recently spotlighted autoimmune TCE molecule Cizutamig, the talk traced industry deals, molecular mechanisms, TCE geometric design, and AI-driven antibody R&D workflows, demonstrating how the Click.mAb. platform supports next-generation antibody design.

The keynote analyzed Cizutamig — the core asset behind UCB's acquisition of Candid Therapeutics — pointing out that its value derives not from a single target, but from an integrated TCE platform strategy for autoimmune diseases.

Centering on directions such as BCMA × CD3 autoimmune TCEs, Clickmab broke down the key design dimensions:
① Target combinations and immune-reset logic
② TCE format selection
③ Membrane distance and immune synapse formation
④ Epitope position and its effect on activity
⑤ Safety window control of the CD3 arm
⑥ Balance between CRS risk and T-cell activation

Building on the case study above, Clickmab unveiled the Click.mAb. Multispecific Antibody Design Workbench under development. With AI agents, researchers can perform:
◆ Target research and competitive landscape analysis
◆ Antibody discovery and optimization support
◆ Sequence and structure analysis
◆ Developability risk assessment
◆ Literature and patent intelligence integration
◆ Experimental design and R&D decision support
◆ Multispecific antibody format design
◆ TCE geometry and immune synapse simulation
◆ Distance, angle, and steric hindrance analysis
◆ One-click sequence assembly, codon optimization, and vector construction

Click.mAb. is not just an AI chatbot — it is a professional AI agent platform purpose-built for antibody drug R&D. Designed around the R&D workflow, the platform helps teams to:
· Unify literature, patents, databases, and internal knowledge
· Move from target research to antibody discovery with intelligent analysis
· Bridge experimental design and project decisions with AI collaboration
· Enable information sharing and knowledge accumulation across roles and teams

Compared to traditional fragmented tools, Click.mAb. emphasizes:
✓ Deep optimization for antibody R&D scenarios
✓ Decomposition of complex scientific tasks and multi-agent collaboration
✓ Continuous accumulation and reuse of R&D knowledge
✓ Improving R&D efficiency and decision quality for teams

Through Click.mAb., Clickmab aims to make AI a true "digital R&D partner" for antibody research teams.

As antibody drug development moves into an era of multi-modality, multi-mechanism, and cross-team collaboration, traditional R&D models face new challenges. Click.mAb. is building AI infrastructure for the future of antibody R&D:

01 An AI platform that truly understands antibody R&D
Beyond general AI capabilities, the platform is deeply constructed around antibody R&D scenarios:
· Antibody R&D knowledge system
· Domain-specific biomedical semantic understanding
· Antibody R&D workflow coordination
· Agent orchestration for scientific tasks

02 From "information retrieval" to "R&D collaboration"
Click.mAb. helps R&D teams:
· Access key R&D information faster
· Complete complex analyses more efficiently
· Integrate knowledge at lower cost
· Make project judgments and risk assessments more systematically

03 AI as productivity, not just an efficiency tool
The value of AI in antibody R&D goes beyond search and summarization. It participates in:
· R&D analysis · R&D reasoning · Scientific decisions
· Team collaboration · Knowledge accumulation

Going forward, Click.mAb. will continue to iterate AI agent capabilities around antibody R&D scenarios, evolving AI from an assisting tool to genuine R&D infrastructure.

As complex modalities such as bispecifics, multispecifics, ADCs, TCEs, and in-vivo CAR-T evolve rapidly, antibody R&D faces:
⚠ Rapidly growing data volumes
⚠ Increasing target and mechanism complexity
⚠ Rising pressure on R&D cycles and costs
⚠ Greater difficulty in cross-team collaboration

AI is becoming critical infrastructure for next-generation antibody R&D. Clickmab believes that future antibody R&D platforms need not only powerful algorithms, but also the ability to:
· Address real R&D scenarios
· Deeply understand antibody R&D logic
· Integrate into scientists' workflows
· Support multi-role collaborative decision making

Through AI agent technology, Click.mAb. aims to deliver truly deployable intelligent capabilities for antibody R&D.