AI-Driven Scientific Discovery

Equations the universe
was hiding.

Logic Engine derives provable scientific laws from raw data — no hypotheses, no neural networks. Pure symbolic discovery that extrapolates where black-box models fail.

87% Error reduction vs. MLP baseline
6,964 Binding measurements analyzed
7,000× Selectivity in clinical compounds

Science, derived.

The Logic Engine doesn't fit curves. It finds the underlying differential equation whose integral describes the data — producing laws that extrapolate correctly where neural networks fail.

Ingests Data

Raw IC₅₀ measurements, binding assays, and molecular descriptors from public databases like ChEMBL.

Derives Laws

Computes derivative invariants to identify which ODE the data satisfies, then integrates it into a closed-form symbolic law.

Validates OOD

Tests the derived law on out-of-distribution data it never saw. ΔG = 0.869 means 87% error reduction over MLP baselines.

Produces Manuscripts

Generates peer-reviewed quality papers with full methodology, figures, and reproducible evidence reports.

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Logic Engine is developing new discoveries across pharmacology, materials science, and beyond. Join the waitlist for early access and research updates.

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