HomeCompaniesReticular

Interpretable AI for drug discovery

Reticular helps pharma companies discover drugs with AI models like AlphaFold by making them steerable, just like you can prompt LLMs. Today, limited validation data means companies spend millions on failed experiments trying to steer these models through trial and error. We’re piloting our AI interpretability technology with early-stage biotechs and scaling rapidly. Just a week after our pivot, we identified the first interpretable features ever found in protein models, allowing precise control over biological functions. Nithin and John met competing in Biology Olympiads before spending 4 years as roommates at MIT publishing ML/bio research in NeurIPS and Nature. We believe biological models encode far more information than anyone is currently using - our goal is to unlock this potential.
Reticular
Founded:2024
Team Size:2
Location:San Francisco
Group Partner:David Lieb

Active Founders

Nithin Parsan, Founder

MIT AI + Bio. Prev. led clinical ML projects at the MIT-IBM Watson AI lab and was an IBO Gold Medalist.
Nithin Parsan
Nithin Parsan
Reticular

John Yang, Founder

MIT AI + Mathematics '24. Published ML research in NeurIPS and Nature. Prior quant intern at Goldman Sachs. National achievements include USA Brain Bee Champion, USA Biology Olympiad Bronze Medalist, and First Place Euro Challenge Team Captain.
John Yang
John Yang
Reticular

Company Launches

Hey everyone! We're Nithin and John, founders of Reticular.

TL;DR:

Reticular gives pharma companies precise control over protein AI models, enabling reliable drug discovery without millions in wasted experiments. We're unlocking encoded information in these models through foundational AI interpretability research, making steering as easy as prompting ChatGPT.

The Team:

Nithin and John are huge AI + Bio nerds who:

  • Met 7 years ago competing in International Biology and Neuroscience Olympiads and were roommates at MIT for 4 years
  • Published ML/bio research in NeurIPS, Nature, and PLoS ONE

The Challenge:

Information in biology is incredibly scarce and expensive to validate. While protein AI models like AlphaFold have revolutionized drug discovery, they're still black boxes:

  • Companies waste millions testing AI-generated designs and they can't easily control what these models output
  • Limited data makes validation slow and expensive

Our Approach:

Instead of trial-and-error, we're leveraging mechanistic interpretability techniques that excel at extracting sparse knowledge from models even with scarce data. We’re demonstrating that the same advances from frontier AI labs in steering models like Claude are applicable to unlock protein language models.

For our design partners, we’re delivering:

  • Direct steering of protein models towards desired properties
  • Interpretable biological features backing every design
  • Efficient exploration of combinatorially massive design spaces with limited data

See It In Action:

We're steering Green Fluorescent Protein towards more fluorescent sequences by directly controlling the model's internal knowledge.

Learn More at reticular.ai, our proof-of-concept blog post, and at demo.reticular.ai.

We're Looking To Connect With:

  • Startups working with biological language models for design partnerships
  • Pharma teams building generative discovery pipelines
  • Researchers working on AI interpretability

Working with biological AI or have relevant connections? We'd love to chat! Schedule some time at calendly.com/reticular or email us at contact@reticular.ai

YC Sign Photo

YC Sign Photo