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Deciphering the gut-brain axis through AI-driven drug discovery for transformative therapeutics.
Accelerating protein-based drug discovery through an autonomous, closed-loop robotic platform.
LabGenius is a clinical-stage biotechnology company that has pioneered 'EVA', an autonomous protein discovery platform. Unlike traditional pharmaceutical research which relies on trial-and-error, LabGenius utilizes a sophisticated active learning loop that integrates machine learning with a fully automated robotic wet-lab. The technical architecture involves generating massive, proprietary datasets through high-throughput DNA synthesis and sequencing, which feed into predictive models. These models navigate the massive 'fitness landscape' of protein sequences to identify therapeutic candidates with optimized potency, stability, and manufacturability simultaneously. By 2026, LabGenius has positioned itself as a critical infrastructure partner for Tier-1 pharmaceutical firms, shifting the industry standard from manual discovery to AI-driven search. Their platform specifically excels in the discovery of multi-specific antibodies and difficult-to-engineer protein therapeutics, providing a significant competitive edge in speed-to-clinic and candidate quality. The system's ability to perform multi-objective optimization ensures that lead candidates are not just effective, but also possess the physical properties required for large-scale production and human delivery.
A proprietary closed-loop system combining Bayesian optimization with robotic execution.
Deciphering the gut-brain axis through AI-driven drug discovery for transformative therapeutics.
Accelerating drug discovery through an end-to-end generative AI pipeline for target identification, molecular design, and clinical trial prediction.
Engineering biology at scale to discover and develop next-generation therapeutics.
Accelerating drug discovery through proteome-wide AI-driven polypharmacology.
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Algorithmically balances competing traits like binding affinity and thermal stability.
Integrated liquid handling and NGS pipelines that run 24/7 without human intervention.
Machine learning models that decide which experiments to run next to maximize information gain.
Unique datasets mapping DNA sequences to complex functional protein behaviors.
Ability to design sequences that do not exist in nature for specific therapeutic targets.
Direct integration of sequencing results into the ML pipeline for real-time model updating.
Designing molecules that can bind to two different antigens simultaneously is computationally and biologically difficult.
Registry Updated:2/7/2026
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Potent antibodies often aggregate or degrade at room temperature, making them unviable products.
Protein drugs can trigger immune responses in patients, neutralizing the treatment.