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Basic Information

The Virtual Lab repository provides a platform for AI-human collaboration in scientific research where a human researcher interacts with a team of large language model agents. It supports structured interactions via team meetings, in which multiple agents discuss a research agenda, and individual meetings between the researcher and a single agent to address specific tasks. The project demonstrates a real-world application to nanobody design for SARS-CoV-2 and includes a computational pipeline built from existing bioinformatics tools. The README points to an example notebook that shows how to create agents, run meetings, and execute the end-to-end workflow. The package is installable via pip and requires an OpenAI API key for its LLM backend.

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App Details

Features
The repository implements a multi-agent architecture that orchestrates team meetings and one-on-one agent meetings to address scientific tasks. It includes example code and a notebook demonstrating nanobody design workflows. The Virtual Lab integrates external computational tools used in protein design, specifically ESM, AlphaFold-Multimer, and Rosetta, to form an automated design pipeline. The project is distributed as a Python package installable via pip and supports local installation from the cloned repository. It relies on OpenAI models for language capabilities and documents the need to set an OPENAI_API_KEY. The README cites a published paper and experimental validation of designed nanobodies as part of the project evidence.
Use Cases
This repository helps researchers orchestrate LLM agents to accelerate and structure scientific workflows by combining language-model reasoning with established computational biology tools. It supplies reusable agent patterns and meeting protocols to decompose research problems, an example pipeline and notebook that illustrate how to run an end-to-end design experiment, and packaging that simplifies installation. By integrating sequence and structure prediction tools into an agent-driven workflow, the Virtual Lab can generate candidate designs that were experimentally validated in the cited study, enabling reproducibility and a starting point for domain-specific adaptation. Documentation also explains required setup steps such as installing the package and configuring an OpenAI API key.

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