If you look at the history of human progress, almost every industry has transitioned from “craft” to “engineering.” We used to build bridges by trial and error; now we use physics simulations. We used to navigate by the stars; now we use GPS.
But biology? Biology has largely remained a craft.
The current standard for discovering a new drug reads like a brute-force algorithm from the 1980s. It takes, on average, 12 to 15 years and $2.5 billion to bring a single new drug to market. The failure rate is staggering, over 90% of candidates that enter clinical trials never make it to the pharmacy shelf.
Imagine if SpaceX rockets exploded 90% of the time. We wouldn’t call that an industry; we’d call it a gamble.
We founded our startup on a single, radical premise: Drug discovery shouldn’t be a lottery. It should be an engineering discipline.
We are not just “using AI” to speed up the old process. We are rebuilding the entire stack, treating biology as a data problem that can be solved with the right architecture. Here is the blueprint of how we are doing it, the tech stack we are betting on, and why this shifts the paradigm for investors and developers alike.
The Paradigm Shift: Why “In Silico” is the New “In Vitro”
For decades, the bottleneck in pharma was the “wet lab.” You had to synthesize a molecule physically, put it in a petri dish, and wait to see what happened. It was slow, expensive, and analog.
Today, the bottleneck has shifted. We have massive datasets of genomic sequences, protein structures, and chemical interactions. The problem is no longer generating data; it’s reasoning through it.
We are moving the primary workspace from the wet lab to the server room—a shift known as in silico discovery. By simulating interactions digitally before we ever touch a pipette, we can fail fast and cheap.
But to do this, you can’t just buy a subscription to ChatGPT. You need a bespoke architecture designed for scientific rigor.
The Architecture: A “Digital Brain” for Biology
Our platform is built on four pillars. For the developers reading this, this is our stack. For the investors, this is our moat.
1. The Research Engine: Large Language Models (LLMs) as Reasoning Agents
In the context of biology, LLMs (like Gemini, Claude, or Llama) are often misunderstood. Most people use them to write emails. We use them to understand the language of life.
Biology is, at its core, a language. DNA is a four-letter code; proteins are sequences of amino acids that “fold” into functional sentences. LLMs are uniquely suited to pattern-match these sequences.
We deploy fine-tuned models to act as high-speed research assistants. They ingest millions of unstructured data points, scientific papers, patent filings, and clinical trial logs—to identify novel targets.
However, an LLM on its own is like a brilliant student who hallucinates. It might invent a chemical compound that violates the laws of physics. That is where our second pillar comes in.
2. The Guardrails: Retrieval-Augmented Generation (RAG)
In high-stakes industries like healthcare, “hallucination” isn’t a quirk; it’s a liability. We cannot afford for our AI to make up citations.
We utilize Retrieval-Augmented Generation (RAG) to ground our AI in ground truth. Think of RAG as giving the AI an open-book exam, but the “textbook” is a curated, verified database of molecular structures and peer-reviewed research.
When our system queries a potential drug target, the workflow looks like this:
- Query: “Identify small molecules that inhibit Protein X.”
- Retrieval: The system vector-searches our internal Knowledge Graph for known inhibitors and structural data.
- Generation: The LLM synthesizes this specific, retrieved data into a hypothesis.
- Citation: Every claim is linked back to the source paper or dataset.
If the data isn’t in our “Library of Truth,” the AI is programmed to say, “I don’t know,” rather than guessing. This reliability is what separates a tech demo from a clinical tool.
3. The Infrastructure: Microservices as a “Modular Lab”
Drug discovery is a pipeline of distinct tasks: Target identification $rightarrow$ Molecule generation $rightarrow$ Toxicity prediction $rightarrow$ Docking simulation.
Monolithic software architectures fail here because biology is messy and constantly changing. If a new toxicity model comes out tomorrow, we don’t want to rewrite our entire codebase.
We utilize a Microservices Architecture. Each step of the discovery process is an independent service wrapped in a container.
- Service A generates 10,000 candidate molecules.
- Service B runs a toxicity screen on them.
- Service C simulates their metabolic stability.
- Shutterstock
This allows us to be agile. If we develop a better algorithm for protein folding, we just swap out that specific microservice. The rest of the “lab” keeps running without interruption. It allows us to scale horizontally—running thousands of simulations in parallel on the cloud—drastically reducing the time-to-insight.
The Efficiency Hack: Introducing TOON (Token-Oriented Object Notation)
Here is a detail that developers will love and investors should appreciate (because it saves money).
LLMs charge by the “token.” The more text you feed them, the more it costs. Standard data formats like JSON are incredibly wasteful for this. They are full of repetitive keys, brackets, and quotes.
The Problem with JSON:
JSON
{
"molecules": [
{"id": 101, "name": "Compound_A", "weight": 340.5, "toxicity": "low"},
{"id": 102, "name": "Compound_B", "weight": 312.2, "toxicity": "high"}
]
}
In a dataset of a million molecules, you are paying to transmit the words “id,” “name,” and “weight” a million times. It’s burning capital on syntax.
The Solution: TOON
We implemented TOON—a concise, token-optimized format designed for LLMs.
The TOON Advantage:
Plaintext
molecules[2]{id,name,weight,toxicity}:
101,Compound_A,340.5,low
102,Compound_B,312.2,high
By switching to TOON, we reduced our token usage by nearly 60%.
- For Devs: This means lower latency and cleaner prompts. The model focuses on the data, not the formatting structure.
- For Investors: This cuts our API inference costs in half. In an AI-heavy startup, that is the difference between burn rate and runway.
The Investment Thesis: Why This Matters Now
Why is this the moment for AI-driven drug discovery? Because we have reached a convergence of three exponential curves:
- Compute Power: GPUs are finally fast enough to simulate molecular physics at scale.
- Data Availability: We have sequenced enough genomes to understand the “source code” of disease.
- Algorithmic Maturity: Transformers (the architecture behind LLMs) have given us the ability to reason across domains.
We are not just building a “faster horse.” We are building an engine.
For the investor, this represents a shift from a high-risk, high-capital model to a tech-enabled, scalable model. We are reducing the cost of failure. If a molecule is going to fail, we want it to fail in our server on a Tuesday afternoon for $50, not in a Phase III clinical trial five years later for $500 million.
For the developer, this is the ultimate frontier. You aren’t optimizing ad clicks or social media feeds. You are optimizing the code that could cure Alzheimer’s. You are debugging the source code of life itself.
The Future is Engineered
We are still in the early days. The “GPT-3 moment” for biology hasn’t fully arrived yet, but it is close.
We are building the infrastructure for that future. A future where a doctor can input a patient’s unique genetic profile, and our system can design a personalized therapy, verify its safety via RAG, simulate its efficacy via microservices, and output the formula—all within days, not decades.
We are moving from discovery by luck to engineering by design. It’s complex, it’s difficult, and it’s the most exciting problem in the world to solve.
If you are a developer who wants to work on RAG pipelines that matter, or an investor looking at the intersection of Bio and Tech, keep watching this space. We are just getting started.
Disclaimer: The technologies discussed (LLMs, RAG) are tools to assist scientists, not replace them. All AI-generated candidates undergo rigorous validation by human experts.