Famous Labs Launches Heisenberg, a Quantum-Informed AI System Built to Accelerate Real-World Drug Discovery

Heisenberg

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While AI drug discovery has proven capable of generating molecular candidates at scale, experimental resources remain finite. As a result, the central constraint in drug discovery is no longer molecule generation, but learning efficiency grounded in physical synthesis and testing.

Heisenberg is designed to address this constraint by prioritizing chemistry decisions that maximize learning per synthesis cycle and translate directly to laboratory execution.

The platform operates during the most leverage-sensitive stages of drug discovery, from hit identification through preclinical candidate selection and IND-enabling chemistry decisions. In these phases, synthesis cost, iteration speed, and learning efficiency determine whether a program advances.

"Chemistry superintelligence won't come from scaling molecular volume in silico. It will come from learning more from each synthesis. By combining quantum-derived molecular insight with large-scale chemical reasoning, Heisenberg enables a form of chemistry superintelligence defined by context, speed, and learning efficiency," said Ryan Noorbehesht, Head of Molecular AI at Famous Labs and founder of the Heisenberg platform.

Heisenberg is a quantum-informed medicinal chemistry decision engine built on proprietary AI methods for molecular decision analysis and learning optimization.

Starting from an existing molecule or chemical series, the system reasons across chemistry, physics, and biology to recommend a limited number of synthesis-ready molecules. Each recommendation is designed to answer a specific scientific question, reduce uncertainty, or validate a hypothesis that advances a program toward a viable preclinical candidate.

Every molecule evaluated by Heisenberg is treated as a complete decision unit rather than a datapoint. The system integrates structural fingerprints, quantum-derived electronic representations, physicochemical and ADMET risk signals, three-dimensional structural context when available, and synthesis feasibility through retrosynthetic analysis and purchasable building blocks.

Heisenberg layers electron-density-derived molecular representations on top of traditional fingerprint space, expanding chemical space along a physics-native axis. Quantum information directly influences molecule prioritization rather than being applied after generation.

The platform intentionally produces a limited number of recommendations. Each molecule includes a defined learning objective, an explicit hypothesis, and an explanation of how its outcome will inform subsequent chemistry decisions. Negative results are treated as high-value information, and redundant experiments are actively avoided.

After synthesis and testing, experimental results are fed back into the system, including activity data, selectivity, ADMET signals, and synthetic observations. Heisenberg updates its reasoning for the specific program, refines hypotheses, eliminates unproductive regions of chemical space, and recommends the next most informative molecules to synthesize, allowing learning to compound across experimental cycles.

Heisenberg is designed to support IND-enabling chemistry decisions by producing developability-aware molecular series, synthesis-ready routes and building block strategies, and a traceable learning history across structure-activity relationship evolution.

About Heisenberg

Heisenberg is a quantum-informed AI platform developed by Famous Labs to support small-molecule drug discovery. It is designed to help drug discovery teams prioritize synthesis, reduce experimental waste, and accelerate the path from early hits to preclinical candidates by maximizing learning from each experiment.

Learn more at https://heisenbergbio.com 

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