The challenge with AI in drug discovery isn’t capability. It’s verification.
We built a methodology specifically to solve this, which is why EMET outperforms the leading frontier model by 18 points on critical discovery tasks.
Scaling generative AI is an evaluation problem
LLMs are confident by design. They produce fluent, authoritative-sounding outputs whether they're right or wrong. In preclinical R&D, where a missed safety signal or a fabricated citation can derail a program, it's not a tolerable failure mode.
Decisions biopharma scientists actually make
Rather than a general-purpose benchmark score, we built our evaluation around the decisions biopharma scientists actually bring to EMET — anchored in Selector (our proprietary database of reagents and published evidence), literature locked behind subscription paywalls, and the knowledge graph connecting both.
Neuro-symbolic biological ground truth
The neuro-symbolic evaluation loop pairs EMET's knowledge graph — 858M nodes, 2.2B relationship edges, curated by 60 PhD scientists — with LLM generation, using structured biological ground truth as a constant anchor.
Continuous ground-truth refinement
Every output is cross-referenced, edge cases are surfaced, and the system improves with each iteration through a combination of human expert validation, automated silver dataset generation, and continuous model refinement.
18 points above the top frontier model
Validated across 600+ tests and 8+ benchmarks on the decisions that matter at the bench.
20 key questions in 10 critical drug discovery areas
Reagent selection win rate
Across every reasoning mode tested, EMET outscored the competitor on recommending validated antibodies, proteins, and cell products — winning head-to-head on two-thirds of matched questions, backed by exact catalogue numbers and published figure counts.
Reduced paywall hit rate
On questions behind subscription paywalls, competitor models hit paywalls on 42% of questions versus just 16% for EMET. EMET delivered the better overall answer on 50% of these questions, versus 30% for the competitor.
Biological reasoning win ratio
On multi-step reasoning questions that replace hours of manual literature review, EMET was roughly twice as likely to win as to lose against the competitor, holding that advantage at every effort level.
How it works
A closed neuro-symbolic loop where accuracy compounds with each iteration.
Knowledge graph anchor
EMET's 858M-node biological knowledge graph acts as a symbolic reference point for every output — a structured, verifiable record that LLM generation is continuously checked against.
Expert calibration
PhD scientists construct Golden Datasets: curated sets of expert-validated questions and correct answers that define the ground truth for each scientific domain EMET operates in.
Generative scaling
LLM evaluation and automated processing generates Silver Datasets at scale — extending expert-validated ground truth across a far broader range of inputs than human curation alone could cover.
Synthesis & enrichment
Outputs from each cycle feed back into both the model and the knowledge graph, making the system more accurate on the next iteration — not just larger.
Compounding accuracy
Each cycle tightens the loop: better ground truth, sharper model, more accurate outputs. Accuracy compounds. It doesn't plateau.
Drug programs fail when biology is misunderstood. EMET exists to close that gap — before it closes your pipeline.
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