Скачать или смотреть Accelerating Cures. How AI compresses drug discovery from decades to minutes.

What if we could fundamentally change how new medicines are discovered?

Today, bringing a single drug from the lab to patients takes 10–15 years, costs an average of $2.6 billion, and still fails nearly 90% of the time. Despite over $200 billion spent annually on R&D, the process remains slow, manual, and deeply inefficient.

In this video, we explore how a new AI-powered discovery engine is designed to break that bottleneck.

We walk through:

Why drug discovery timelines are so long and failure rates so high

How data fragmentation and manual analysis slow research to a crawl

Why most late-stage failures are financially catastrophic

Then we introduce the Hypothesis Acceleration System (HAS) — an AI-driven platform built to operate like a team of autonomous scientists, working 24/7 across massive biological datasets.

HAS accelerates discovery by:

Identifying biomarkers in minutes instead of months

Reducing per-sample analysis costs by an order of magnitude

Increasing predictive accuracy to over 95%

Validating findings using AI and CRISPR-based modeling before lab testing

The result is a 3.2× acceleration across the drug discovery timeline — dramatically shrinking the gap between research and real-world treatments.

We also examine how this technology scales into a sustainable business:

A SaaS platform accessible to universities, biotech startups, and global pharma

A defensible 46-step pipeline with regulatory-grade auditability

A growing network effect that strengthens the system over time

Most importantly, we look at the real-world impact:

Smaller, faster clinical trials for rare diseases

Higher success rates in oncology research

Faster approval timelines for new diagnostics

This isn’t just about efficiency or cost savings.
It’s about turning decades-long scientific bottlenecks into high-speed pathways to cures — and delivering hope to patients faster than ever before.

If AI can compress a 15-year process into minutes, the real question becomes:
What other world-changing problems could be accelerated next?