Finding cures. All of them.
We approach therapeutic discovery as a unified optimization problem across the entire landscape of human disease.
Drug discovery is reaching its structural limits
For decades, modern drug discovery followed a familiar path: select a biological target, design a molecule around it, test it experimentally, and advance the most promising candidates. This process produced many important medicines, but it is showing clear signs of exhaustion.
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Industry-wide, the cost per approved drug has ballooned to $2.5 billion, development timelines have extended to 12–15 years, and the probability that a candidate entering clinical trials will reach approval has fallen below 7%. These trends reflect the increasing complexity of human biology and the gradual exhaustion of the most accessible therapeutic mechanisms.
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Addressing today’s unmet medical needs requires discovering new patterns, new mechanisms, and new molecular opportunities — capabilities that the traditional, hypothesis-driven model was not designed to deliver at scale.
Our Philosophy:
Systematic Discovery
Rather than selecting a narrow set of targets and designing molecules around them, we pursue a strategy of systematic, large-scale exploration across biological space. By generating broad interaction data across protein families and diverse chemical domains, we build a high-resolution map of therapeutic opportunities that is not constrained by human intuition or historical assumptions.
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The sheer scale of biological space makes exhaustive exploration impossible. AI provides the mechanism for directing this exploration efficiently and intelligently.
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By designing the system with no human in the loop, we enable unbiased, data-driven progress at a speed and scale unattainable through traditional workflows.
Partnerships and Collaboration
Systematic, computational exploration does not replace the detailed biological research conducted in academic laboratories, medical centers, and clinical settings. Instead, it enhances and accelerates it.
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Researchers provide deep expertise in disease mechanisms, genetic factors, patient phenotypes, and clinical context. Our platform can apply these insights at scale — testing hypotheses in days rather than months, exploring related mechanisms, and generating optimized molecular candidates for experimental validation.