Clickmab at CBA-China 2026: AI-Driven Antibody R&D Practice and Exploration

In antibody R&D, the industry has been grappling with shared challenges:

1. How can we find effective candidate molecules faster?
2. How can we reduce experimental screening costs?
3. How can we improve R&D success rates?
4. How can we effectively retain and coordinate complex R&D processes?

The value of AI is shifting from "improving efficiency" toward "restructuring the R&D process." During the conference, Dr. Tianyuan Wang, CEO of Clickmab, gave a talk on "AI-Driven Structural Biology Innovation to Accelerate Novel Drug Target Discovery and Precision R&D," introducing the core capabilities and design philosophy of Click.mAb.—a precise, efficient, and easy-to-use AI agent platform for full-process antibody drug R&D.

At this conference, Clickmab highlighted its practical capabilities across several core R&D scenarios.
1. Epitope-Specific De Novo Antibody Design
Generating candidate antibodies directly around specified epitopes enables rational design driven by functional requirements. This has long been regarded as a highly challenging problem in antibody design, but the combination of AI and structural biology is making targeted design increasingly feasible. These capabilities have been validated across multiple target projects, yielding candidates capable of hitting specific epitopes.

2. AI-Driven Affinity Maturation
Traditional affinity optimization often relies on extensive random screening, while AI can now identify complex spatial coordination relationships between residues. Compared to single-point mutation strategies, cooperative mutation optimization achieves more effective affinity improvement using smaller libraries. This not only means greater efficiency, but also signals a shift in R&D logic from "trial-and-error driven" to "structure- and data-driven".

3. Antibody and Nanobody Humanization
In humanization, Clickmab demonstrated the combined application of AI structure prediction and a large-scale human framework database. Unlike traditional empirical approaches, AI can search a broader sequence space for candidate solutions, improving humanization levels while maintaining activity, expression, and stability. For nanobodies (VHH), this structure-level holistic assessment is significantly reducing experimental screening costs.

Beyond core algorithmic capabilities, another frequently discussed topic is how AI truly integrates into R&D teams. As AI-assisted design capabilities continue to strengthen, new questions are emerging:

1. How do we track the R&D process?
2. How do we manage multiple exploration paths?
3. How do we retain key findings?
4. How do teams collaborate based on unified information?

To address these questions, the Click.mAb. platform is continuously enhancing project-level collaboration capabilities, including: multi-path parallel exploration, automated R&D process archiving, automatic project knowledge accumulation, and team sharing with collaborative discussions—enabling AI to serve not just individual tasks, but the entire R&D workflow.

During the conference, Clickmab engaged with visitors from pharmaceutical companies, biotech firms, research institutions, and industry partner organizations at Booth D72, exploring real-world application scenarios for the Click.mAb. platform.

If you are interested in the Click.mAb. platform and would like to understand its real capabilities in antibody design task decomposition, computational scheduling, structural analysis, and result integration, we welcome you to reach out for a discussion. Going forward, we will continue to center our work around real R&D scenarios, driving the deep integration of multi-agent AI technology with antibody drug discovery, and exploring more efficient and intelligent R&D models.