LatticeBio: The Virtual Wet Lab Platform

Inspiration

Biomedical research is slow, fragmented, and heavily dependent on manual literature review. We were inspired by the idea that AI could act as a “virtual scientist,” rapidly reading, synthesizing, and structuring knowledge from vast amounts of scientific papers. Our goal was to reduce the time it takes to move from hypothesis to insight.

What it does

LatticeBio acts as a virtual research assistant that reads, organizes, and connects biomedical literature at scale. Instead of manually reviewing papers, users can ask questions and receive synthesized insights, structured data, and emerging hypotheses. By coordinating multiple AI agents, the platform turns scattered scientific information into a coherent, queryable knowledge base that accelerates early-stage discovery.

How we built it

We built LatticeBio using a multi-agent architecture where specialized AI agents handle tasks such as literature retrieval, summarization, knowledge extraction, and synthesis. We integrated LLMs with a pipeline that processes scientific papers into structured formats, enabling downstream querying and analysis. The system emphasizes modularity, allowing agents to collaborate and refine outputs iteratively.

Challenges we ran into

One major challenge was ensuring accuracy and consistency across multiple AI agents, especially when synthesizing complex scientific information. We also faced difficulties in structuring unstandardized scientific literature and reducing hallucinations. Balancing depth of insight with speed and computational efficiency was another key challenge.

Accomplishments that we're proud of

We successfully built a working multi-agent system capable of turning unstructured research papers into structured, queryable knowledge. The platform demonstrates meaningful improvements in research efficiency and showcases how AI can assist in hypothesis generation and literature synthesis.

What we learned

We learned that multi-agent systems can significantly enhance the quality of outputs compared to single-model approaches, but require careful orchestration. We also gained insight into the importance of grounding AI outputs in reliable data and designing systems that prioritize transparency and interpretability.

What's next for LatticeBio: The Virtual Wet Lab Platform

Next, we aim to extend LatticeBio beyond literature synthesis into a full virtual wet lab. This includes simulating experiments, generating testable hypotheses, and integrating experimental datasets. Our long-term vision is to create an end-to-end AI platform that accelerates the entire biomedical research lifecycle.

Built With

• biostudies
• groq
• node.js
• postgresql
• python