LabGenius
Accelerating protein-based drug discovery through an autonomous, closed-loop robotic platform.
Accelerating drug discovery through deep physics and generative AI without experimental data training.
Aqemia is a next-generation pharmatech company that leverages a unique combination of deep physics and generative AI to revolutionize drug discovery. Unlike traditional AI platforms that rely on existing experimental data to train their models, Aqemia's proprietary technology is 'data-free' at the outset. It uses massively parallelized statistical mechanics and quantum-inspired algorithms to predict the binding affinity between a small molecule and a therapeutic target with high precision. This physics-based approach allows Aqemia to explore massive chemical spaces (up to 10^15 molecules) and generate its own data points, effectively bypassing the 'cold start' problem in drug design for novel targets. By 2026, Aqemia has positioned itself as the premier partner for multi-billion dollar pharmaceutical collaborations, providing a full-stack discovery engine that identifies and optimizes lead candidates in months rather than years. Its architecture is optimized for High-Performance Computing (HPC) environments, allowing for the simultaneous optimization of affinity, selectivity, and ADMET properties using a generative loop that refines chemical structures in real-time based on physical first principles.
Uses statistical mechanics algorithms to calculate free energy of binding without requiring prior experimental training sets.
Accelerating protein-based drug discovery through an autonomous, closed-loop robotic platform.
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.
Verified feedback from the global deployment network.
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Ability to screen and optimize over 10^15 molecules using generative AI combined with ultra-fast physics scoring.
Simultaneously optimizes for binding affinity, solubility, metabolic stability, and toxicity within the generative cycle.
High-resolution docking algorithms that simulate electronic interactions and hydration effects.
Integrated AI that ensures all generated molecules can be practically synthesized in a lab environment.
Architecture designed for cloud-native or on-premise high-performance computing clusters.
Dynamically updates generative models based on physics results generated in real-time.
Identifying a potent inhibitor for a novel kinase with no existing small-molecule data.
Registry Updated:2/7/2026
Synthesize top 5 candidates
Finding novel chemical structures that maintain potency but circumvent competitor patents.
A lead candidate has high affinity but is cleared too quickly by the liver.