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Project-Level Team Collaboration

Build structured workspaces around R&D projects — parallel multi-track progress, auto-aggregated knowledge bases, full-process traceability, making collaboration more efficient and R&D experience more reusable.

Feature Overview

Core Collaboration Features

Parallel Multi-Track Progress

Multiple design tracks can run simultaneously under one project. Each track is managed independently with full decision history, enabling tracing and comparison at any time.

Independent Verification & Collaboration

Whether individual exploration or team review, AI assistants stand ready at every step. Researchers can analyze independently then submit for team discussion, ensuring R&D quality.

Auto-Aggregated Knowledge Base

Project materials, computational results, and experimental data automatically flow into the knowledge base. Teams make decisions based on the same information, eliminating silos.

Reviewable · Traceable · Transferable

Complete records of the entire project progression. R&D experience transforms from individual knowledge into sustainable, reusable team assets.

Project Knowledge Base

All data assets generated during project progression are automatically aggregated into a structured, searchable, and traceable team knowledge base.

Antigen Epitope Data
Sequence Information
Analysis Reports
Antibody Candidate Lists
AI Risk Assessments
Discussions & Decisions
Computational Results
Experimental Data

Roles & Scenarios

Project Lead

  • Create projects, set goals and constraints
  • Assign team member roles and permissions
  • Review track results and make go/no-go decisions
  • View project progress and point consumption

Computational Researcher

  • Use AI tools for sequence design and optimization
  • Run core pipelines (de novo design, affinity maturation, etc.)
  • Record analysis processes, submit to knowledge base
  • Share computational results and recommendations

Wet Lab Researcher

  • View computationally recommended candidate sequences
  • Upload validation data (BLI, FACS, ELISA, etc.)
  • Annotate experimental conclusions, provide feedback to computational team
  • Participate in candidate review and screening decisions

Typical Project Workflow

01

Create Project

Set target, constraints, and design direction

02

Track Design

Launch parallel tracks with AI-assisted planning

03

Compute-Experiment Loop

Computation → Validation → Feedback

04

Review & Select

Team collaborative review, multi-dimensional screening

05

Deliver Report

Auto-generate project summary, permanent knowledge base

Start Efficient Team Antibody R&D

Project management + multi-track progress + knowledge consolidation — every R&D effort becomes a reusable asset.