Acknowledging the "garbage in, garbage out" principle, Haya heavily invests in generating high-quality, layered, and paired multi-omic data from the same biological material. This curated input is considered the most critical component for building effective AI models to unlock new biology.

Instead of using AI for pure discovery, Variant Bio applies it to a specific bottleneck: data overwhelm. With over 25,000 gene associations per search, they deploy AI agents to sift through proprietary data, identify findings absent from existing literature, and flag novel drug targets for human researchers.

The future of AI in drug discovery is shifting from merely speeding up existing processes to inventing novel therapeutics from scratch. The paradigm will move toward AI-designed drugs validated with minimal wet lab reliance, changing the key question from "How fast can AI help?" to "What can AI create?"

The company's BioSeeker AI platform goes beyond discovery. After analyzing genomic data, it directly outputs the functional components for development: the 'guides' for their CRISPR therapeutics and the 'primers and probes' for their diagnostic tests, making AI a rapid creation tool.

Haya's approach redefines the drug target. Instead of focusing on single proteins or pathways, they identify the "causal unit" of disease as the cellular behaviors that dictate how patients feel, function, and survive, and then work backwards to find a target.

Instead of screening billions of nature's existing proteins (a search problem), AI-powered de novo design creates entirely new proteins for specific functions from scratch. This moves the paradigm from hoping to find a match to intentionally engineering the desired molecule.

Beyond accelerating timelines, AI's real value lies in its ability to design molecules for targets previously considered 'hard-to-drug.' These models operate on different principles than traditional lab methods and are indifferent to historical challenges, opening up entirely new therapeutic possibilities.

The current, tangible breakthrough for AI in drug discovery is not identifying completely novel biological targets. Instead, it's rapidly designing effective molecules for known targets that have historically been considered "undruggable," compressing years of screening work into a month.

While AI offers some time savings, A-Alpha Bio's CEO argues this is minimal in the overall drug development timeline. The transformative impact is AI's ability to engineer therapeutics with novel properties, like binding to previously inaccessible epitopes, that traditional discovery methods could never find.