Round 1 Winners!
Proof of Usefulness Report

Haystack Sciences

Analysis completed on 3/17/2026

-28.75
Proof of Usefulness Score
Lab Mode

While the project description details an innovative biopharmaceutical application using NGS, DELs, and machine learning, the submission exhibits severe red flags indicative of spam or a fraudulent entry. Claims such as 'everyone' for target audience, 'most people have used my product' for a niche B2B biotech platform, and a nonsensical revenue/market cap input contradict the scientific nature of the platform. Consequently, the traction and response quality scores have been heavily penalized, resulting in a negative overall score in alignment with calibration guidelines for submissions with red flags.

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Score Breakdown

Real World Utility+25.0
Audience Reach Impact+0.0
Technical Innovation+15.0
Evidence Of Traction-62.5
Market Timing Relevance+10.0
Functional Completeness-12.5
Subtotal-25
Usefulness Multiplierx1.15
Final Score-29

Project Details

Description
Haystack Sciences is an emerging biopharmaceutical company that is positioned at the intersection of three recent technology breakthroughs: • Low cost, high-efficiency “next generation” sequencing (NGS) of DNA, • Very large collections of synthetic drug leads tethered to DNA (DELs), and • Machine learning (ML) applied to pharmaceutical discovery. The company’s core competency lies in the creation and systematic evaluation of DNA-Encoded Libraries (DELs). Haystack’s DELs feature: • Programmed chemistry for ligands (instead of simple recording), • Encoding to enable rapid refinement of chemical properties, and • Drug-like ligands that shorten time-to-market for new targets. In addition, Haystack’s proprietary evaluation platform, nDexer™, enables low-cost, high fidelity discrimination of binding as well as estimations of affinity and chemical yield. With today’s NGS hardware, we produce over a billion data points per week. For active compounds, we quantify affinity at significantly less than $1 per compound. This ultra-high-throughput correlation of chemical structure with protein affinity will allow us to develop a robust machine learning model of ligand protein interactions without complex computational gymnastics. Haystack’s management team includes experienced cofounders with more than 50 years combined experience in pharmaceutical discovery technologies. In addition, Haystack was cofounded by Prof. Scott Pegg, an experienced computational biologist (Gladstone Institute/UCSF) with deep industry experience (Amyris, Impossible Foods).

Algorithm Insights

Market Position
Early stage requiring focused development
User Engagement
Documented reach suggests active user community
Technical Stack
Modern tech stack aligned with sponsor technologies

Recommendations to Increase Usefulness Score

Document User Growth

Provide specific metrics on user acquisition and retention rates

Showcase Revenue Model

Detail sustainable monetization strategy and current revenue streams

Expand Evidence Base

Include testimonials, case studies, and third-party validation

Technical Roadmap

Share development milestones and feature completion timeline