HELIOS — ONGOING
Optimizing hit-to-lead workflows for drug discovery
Led end-to-end product design of B2B cloud-based web application streamlining hit-to-lead optimization for drug discovery research.
ROLE
Co-Founder &
Founding Designer
TEAM
Nabeel Trimzi (PM)
Parker Gurney (Dev)
Sanjna Sunil (Dev)
TOOLS
Figma, v0, Cursor
SKILLS
User Research, Prototyping, Vibe Coding, Front-end Development
BACKGROUND
Thinking as a co-founder first, designer second
As part of the startup incubator LavaLab, my co-founders and I were tasked with identifying a problem space and building a business venture solution within 8 weeks.
My co-founders and I all had backgrounds in building software solutions and directly leading healthcare and biomedical research, which we leaned on to identify our problem space.
OUTCOMES
Judge's Choice Best Product
LavaLab F25 Industry Pitch Comeptition
$75k+
Projected annual recurring revenue
4 Signed Letters of Intent
With veteran biotech founders and early-stage research teams
SOLUTION PREVIEW
Drug discovery teams waste years and millions searching for the next miracle drug.
Presenting Helios: Scientific AI models for early-stage drug discovery
We took the guesswork out of screening, analyzing compounds by their molecular structure to provide lab-validated predictions of their behavior.
Batch process candidate compounds
This allows researchers to quickly run bulk analyses.
Helios analyzes each compound structure
With testing performance markers all in one place, researchers can quickly assess which candidates to proceed with.
Validate predictions with experimental data
Queried lab results on structurally similar compounds provide additional context for behavioral predictions.
Update and track candidate status
Status tags help research teams keep track of progressing candidates.
PROCESS
Testing becomes a massive operational bottleneck, especially for early-stage teams operating on short runways.
Across 20+ interviews with medicinal chemists and biotech founders, we were able to learn more about the drug discovery research timeline and the critical pain points throughout the process. We relied on a mixture of personal connections and cold contacts—I took the charge on outreaching to biotech startups across Los Angeles and San Diego, hot spots for biomedical research.
“Things will look good in testing for a few months, but then will fail the FDA assay, forcing us to start over.”
Milton Greenberg
“The biggest challenge is utilizing the information already out to determine if your candidate is the best."
Mike Bishop
Resource barriers to tooling access
While the biggest pharmaceutical companies have the internal research departments, data, and budget to develop in-house tooling, independent research teams are left with rudimentary methods (like Google Spreadsheets) to log and test chemicals.
FIG 1. Learning about our design partner's research methods on-call (blurred for confidentiality)
Prioritizing features and identifying opportunities for integration
We received a lot of helpful insights on existing pain points and potential solutions from our interviews; however, given the timeline of our program, we could only solve for a few.
To help our team define our goals and prioritize certain features, I led several brainstorming sessions where we mapped out what a potential software flow could look like alongside a researcher's flow.
Based upon our discussions, we decided on the following key features to prioritize for our MVP launch.
KEY FEATURES
AI/ML-powered behavior prediction
Calculate predictive values for main biochemical indicators
Comparison against structural neighbors
Identify similar chemicals structurally to cross-analyze behavior
Batch processing
Conduct analyses in bulk for faster R&D
RESULTS
Shipping my first product as a founding designer, winning Best Product along the way
After iterating intensely, we were able to push out our MVP in just 6 weeks (faster than our initial 8-week timeline) and secure partnerships with leading biotech startups across the country! At the same time, we pitched Helios at LavaLab's Fall 2025 Industry Pitch Competition, ultimately winning Judge's Choice Best Product.
FIG 2. The dream team, pictured with the investors/judges!