Pre-clinical intelligence markets
A liquidity-sensitive prediction market for AI-generated therapeutics. Applies LS-LMSR with adaptive Bayesian seeding to preclinical milestone contracts — producing continuous, expert-calibrated probability estimates before capital is committed to IND-enabling studies.
AI platforms generate candidates faster than any evaluation infrastructure can validate them.
Committed before independent probability assessment exists.
Receive no systematic external evaluation before internal triage decisions.
Exist for pre-IND assets. Internal models evaluate their own output.
Every internal triage decision relies on the same computational models that generated the candidates. There is no adversarial check, no calibration against external judgment, no market mechanism to surface systematic overconfidence. Pre-Clinical Prediction Markets is that mechanism.
Mechanism deep-dive
The LS-LMSR (Othman et al., 2013) replaces the fixed liquidity parameter b of standard LMSR with a volume-adaptive parameter. Liquidity depth grows automatically with cumulative trading volume — early markets are price-sensitive (high reward for early conviction), mature markets develop institutional depth. The α parameter is derived per-asset from the oracle-attested confidence score, calibrating market sensitivity to the reliability of the underlying computational signal.
Without an initial position, a market opens at 50/50 regardless of oracle signal — the cold-start problem. The ABMM solves this by placing synthetic stakes derived from oracle-attested computational scores (binding affinity, selectivity, IC50, literature signal). This provides a non-arbitrary opening price surface calibrated to the molecule's computational prior. The effective confidence combines computational and literature scores with a weighted blend, and seeds the initial YES/NO quantity vector accordingly.
As credentialed expert volume accumulates, the ABMM's influence retreats — transitioning the market from machine-seeded to human-informed pricing. The retreat function is parameterized as exponential decay (concave) rather than linear. Concave retreat is correct for two structural reasons: first, early credentialed trades carry the highest informational value (correcting the most error per trade) and should drive rapid initial retreat; second, thin specialty markets may never accumulate sufficient volume to fully exit ABMM dominance under a threshold design, requiring a residual floor for price stability.
Retreat is weighted by trader calibration score (Brier-based) rather than raw volume — a trade from an expert with a strong prediction track record drives more ABMM retreat than one from a newly credentialed participant. This makes the transition responsive to signal quality, not just signal quantity.
A market scoring rule is incentive-compatible if a trader's optimal strategy is to report their true belief. Under standard LMSR this holds by construction. The ABMM introduces a distortion: its large initial synthetic position makes the market expensive to move early, potentially creating incentives for credentialed experts to underreport their true belief (partial trade is cheaper than full correction) or strategically delay (waiting for ABMM retreat reduces the cost of future trades).
The exponential retreat function addresses both distortions. Rapid early retreat reduces the cost of truthful correction precisely when expert signal is most valuable. The calibration weighting ensures that high-Brier experts — whose corrections are most reliable — drive the most retreat, aligning incentives with signal quality.
The formal condition for ε-incentive-compatibility requires that the ABMM's residual weight at any time t satisfies:
Technical foundation
A — The Problem
Generative drug discovery platforms — Recursion, Insitro, Terray — now produce candidates faster than any evaluation infrastructure can validate them. Each molecule carries a probability distribution over downstream clinical success, but virtually none receives the independent analytical infrastructure afforded to traditionally-derived compounds. Every internal triage decision relies on the same computational models that generated the candidates. There is no adversarial check, no calibration against external judgment, no mechanism to surface systematic overconfidence.
B — LS-LMSR
The LS-LMSR (Othman et al., 2013) replaces the fixed liquidity parameter of standard LMSR with a volume-adaptive parameter. Liquidity depth grows automatically with cumulative trading volume — early markets are price-sensitive (high reward for early conviction), mature markets develop institutional depth. The α parameter is derived per-asset from oracle-attested confidence scores, calibrating market sensitivity to the reliability of the underlying computational signal.
α controls price sensitivity per trade. High-confidence molecules (score = 0.9) receive α = 0.0125 — the market is tight and resists large swings. Low-confidence molecules (score = 0.3) receive α = 0.075 — early trades carry high price impact, rewarding early correct conviction.
C — ABMM & Retreat
Without an initial position, a naive LMSR opens at 50/50 regardless of oracle signal — the cold-start problem. The Automated Bioactivity Market Maker (ABMM) seeds each market with synthetic stakes derived from oracle-attested computational scores (binding affinity, selectivity, IC50, literature signal), providing a non-arbitrary opening price surface.
As credentialed expert volume accumulates, the ABMM retreats via an exponential decay function weighted by trader calibration scores (Brier-based). Concave retreat is correct for two structural reasons: early credentialed trades carry the highest informational value and should drive rapid initial retreat; thin specialty markets may never accumulate sufficient volume to fully exit ABMM dominance, requiring a residual stabilizing floor.
D — DeFi Primitives
A working price oracle for pre-clinical assets enables three DeFi primitives that have not previously existed in drug discovery. These are downstream consequences of solving the oracle problem — not speculative extensions.
Partners stake capital into a molecule's M1 market. If M1 resolves YES, capital automatically routes to the M2 pool. If NO, capital returns pro-rata. The prediction market becomes the funding gate — replacing the $2–5M IND commitment with a staged, market-priced capital release mechanism.
Once a continuous probability estimate exists on a molecule, options become possible. A floor contract pays out if a candidate drops below a threshold IND probability — pipeline insurance priced by the market, not by actuarial tables constructed from historical base rates.
AI labs stake their models rather than specific molecules. If a model's output systematically outperforms market priors across resolved milestones, the model earns calibration-weighted returns. This creates a continuous public benchmark for generative drug discovery models — currently unavailable in any form.
These three primitives are downstream of one architectural prerequisite: a working price oracle. The LS-LMSR + ABMM stack is that oracle.
Live demo
The LS-LMSR Platform dashboard runs live LS-LMSR pricing across 9 oncology targets including ALK, BRAF, BTK, CDK4, CDK6, and EGFR. Real b-values. Real trade history. Real-time price updates.
Considerations