Biological Reasoning Engine
What would it take for AI to truly understand biology? Our answer is a novel AI approach that combines step-by-step mechanistic reasoning, grounded in biology, with a breakthrough innovation for the field we call Modality Fusion.
The result is an AI partner that reasons across every biological scale simultaneously—and shows its work. A force multiplier for the R&D leaders making the next decision.
What is Modality Fusion?
The problem with specialization
Great science builds on what came before. To break new ground, we specialize. And then we keep specializing until we’ve lost sight of the elephant.
AI is at its best not when it replaces what humans can do, but when it augments this, allowing us to attain the impossible—sometimes the unimaginable.
But in biology augmentation is not only about faster analysis. It also requires that the AI see what no specialist can, which is the whole system in real time. It’s reasoning across every scale at once.
Fusion at the representation level
The Ingenix Biological Reasoning Engine integrates best-in-class models across biological scales—from molecules to patients—and across the modalities that describe them, including clinical data, omics, literature, imaging, and pathways. It fuses them at the representation level, not the output level. The reasoning head doesn’t poll a committee of specialists; it reasons directly across their embeddings, highlighting biological links that emerge only when modalities are seen together.
Turns out, you can compare apples to oranges.
Trained to reason like a scientist
We then train the engine on proprietary reasoning traces, teaching it to think the way scientists do—hypothesis, evidence, mechanism, caveat.
Every hypothesis arrives in a way that evokes how a colleague presents a paper. The engine provides a stepwise mechanistic trace and evidence chains with source-level provenance. The caveats are laid out alongside the conclusions.
What does this unlock?
Ingenix can generate decision-grade outputs to suit various objectives such as the following.
A translational evidence pack for IND positioning. Biomarker, subpopulation, and mechanism hypotheses with confidence scores and a recommended validation plan, ready for a pre-IND committee or an investor pitch.
An ADC design and prioritization framework. Mechanism-informed comparison of ADC design variants across payload combinations, linker configurations, and indication context, integrating computational predictions with experimental evidence to prioritize high-performance, developable ADCs.
An asset comparison and clinical positioning brief. Mechanism-anchored differentiation, risk flags, and positioning hypotheses against published competitors and withdrawn assets—the document BD&L needs before a licensing conversation.
A rapid translational synthesis. Literature, internal omics, preclinical assays, and competitor trials pulled into a single reasoning trace. The analytical groundwork that normally takes a translational team weeks—ready in hours.
Working on a question the engine can help answer? The Qualified Access Program is open to a limited number of partners bringing real translational and clinical questions.
Learn more about the program →