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Multi-Agent Collaboration
Conversation is operation, zero learning curve for AI — multiple specialized Agents automatically divide work around the same R&D goal, collaborating to deliver comprehensive insights under unified orchestration.
Try Multi-AgentHow It Works
Natural Language Input
One sentence to launch R&D
No need to learn complex software — simply describe your R&D goal, constraints, and direction in natural language. The platform automatically handles intent understanding and task decomposition.
Smart Task Assignment
Understand intent, decompose tasks
The orchestration center automatically decomposes complex R&D goals into sub-tasks and assigns them to the most suitable specialized Agents — antibody design, risk assessment, visualization, and next-step recommendations.
Multi-Step Computation
Invoke tools, run computations
Multiple Agents work in series, each invoking core compute pipelines (de novo design, epitope prediction, affinity maturation) or lightweight toolkits (sequence analysis, structure analysis) for efficient task completion.
Knowledge Consolidation
Consolidate results, share knowledge
All analysis results automatically flow into the project knowledge base as structured assets. Team members make decisions based on the same information, eliminating silos and ensuring R&D experience reusability.
Specialized Agent Matrix
Each Agent focuses on a specific domain, working under unified orchestration to form a complete R&D capability matrix.
Antibody Design Agent
Conducts de novo design, sequence optimization, and candidate recommendation around target epitopes, outputting high-quality candidates meeting constraints.
Risk Assessment Agent
Automatically identifies immunogenicity risks, developability bottlenecks (aggregation, oxidation, deamidation sites), and provides optimization suggestions.
Visualization Agent
Automatically generates structure visualizations, sequence alignments, transforming complex data into intuitive visuals.
Next Step Advisor
Intelligently recommends next experimental designs, computational approaches, or validation paths based on current results and context.
Tool Capabilities
Core Pipelines
Complex tasks are automatically orchestrated by the platform, ensuring efficient completion of systematic R&D workflows.
Humanization
Lightweight Toolkit
A diverse collection of easy-to-use tools covering sequence, structure, physicochemical, and antibody-specific analysis.
Rapidly retrieve protein sequences, functional annotations, experimental structures, and AI-predicted structures, with support for single and batch lookups plus automatic domain annotation visualizations.
Multi-sequence alignment and similarity analysis, molecular weight / pI / amino acid composition calculation, concentration unit conversion, and multi-line annotated sequence visualization.
Structural deviation (RMSD) computation, chain sequence extraction, multi-conformation clustering, and rapid antibody 3D structure prediction from sequence.
Antibody numbering & CDR visualization, immunogenicity / developability risk identification, epitope-paratope residue analysis, and molecular interaction visualization reports.
Why Multi-Agent Architecture?
Zero Learning Curve
No need to learn any specialized software — natural language input drives the entire R&D workflow.
Multi-Perspective Analysis
Multiple specialized Agents analyze from different dimensions simultaneously, providing more comprehensive results than any single tool.
Automated Orchestration
No manual pipeline wiring — the platform automatically understands dependencies, orchestrates tools, and aggregates results.
Continuous Evolution
The Agent capability matrix continuously expands. New analysis tools and algorithms can be rapidly integrated into existing workflows.
Experience Multi-Agent Antibody R&D
Simply describe your R&D goal in one sentence and let AI plan the optimal path for you.