Platform Changelog

R&D Document Generation Launched

• The platform now supports target research, literature analysis, invention disclosures, and project summary reports. It can gather information, run tool-assisted analyses, and generate editable documents to accelerate early-stage research and capture project findings.

Candidate List Management and Developability Risk Analysis

• Added candidate list management, allowing researchers to consolidate antibody sequences into a single list for comparison, tracking, and follow-up analysis.
• Added five-dimensional developability risk analysis covering aggregation, chemical liabilities, humanness, polyreactivity, and viscosity to help assess sequence-level risks and support candidate selection and optimization.

Antibody Design Workflow Upgrades

• Added a CDR diversity engineering workflow, supporting sequence- and structure-pattern-based antibody optimization to expand AI-assisted antibody engineering capabilities.
• Upgraded the affinity maturation workflow to support key constraint-site configuration for more flexible, targeted optimization.

AI Agent Capability Upgrade

• Added multi-step task planning, enabling AI agents to break down R&D goals into tasks, plan workflows, and execute them continuously for more complex antibody design and analysis.

Click.mAb. V3.0 Officially Launched

The new AI antibody R&D collaboration platform is now officially launched. Built around project spaces, research goals, and AI agent interactions, the platform enables individual researchers and teams to conduct antibody design, analysis, and collaboration through natural language. It provides several core antibody R&D capabilities, including:

• Epitope-specific de novo antibody design
• Antibody affinity maturation
• Antibody humanization
• Nanobody humanization
• Epitope prediction
• Lightweight tools for antibody analysis and visualization

The platform also supports structured management and visualization of computational results, including antibody sequence analysis, structural model display, and design result comparison, helping researchers better understand computational outputs and advance subsequent research.