January 1, 2026
The Hidden Flaws in AI Protein Data
Why True Progress Requires Experimental Foundations and How Neomera
is Built on Biological Reality
The field of AI-driven protein design is moving at breakneck speed.
Models leveraging diffusion, flow matching, and multimodal
architectures promise to revolutionize biology by generating
intricate structures and exploring vast protein landscapes. These
tools appear poised to transform drug discovery by enabling the
rapid design of novel therapeutics. However, a growing body of
evidence reveals a fundamental flaw: the data underpinning these
models is often unreliable. This leads to outputs that look
impressive in a digital environment but fail in real-world
biological applications.
October 6, 2025
AI and Biology: It Takes Two to Tango
Neomera’s Integrated Approach to AI Drug Discovery
Generative AI is revolutionizing drug discovery, promising to design
biologics, small molecules, nucleic acids, and more that target
proteins with precision. It’s thrilling, AI models can generate
thousands of potential drug hits with blazing speed, igniting dreams
of cures for every disease. But here’s the reality check: most of
these hits fizzle out during experimental validation, forcing the
process to restart. Why? Because biology governs the human body, and
current AI prediction models alone rarely make the leap to
preclinical success. At Neomera, we bridge this gap. Our platform
fuses AI’s power with experimental validation, delivering
target-specific, wet lab-tested hits backed by our unique training
dataset. This approach boosts the odds of creating real drug
candidates in just weeks. Here’s how we’re different and why it
works.
