We generate the other 94%.
94% of drug failure data is never published.
Inactiva Labs runs automated robotic assays to produce the negative pharmaceutical data that AI models need — but no one collects.
The missing majority of pharmaceutical data.
Publication bias silently distorts drug development. Negative results vanish, billions are wasted, and only a fraction of candidates survive — all because most of the evidence never makes it to the table.
Publication bias breakdown
Built on peer-reviewed research from NEJM, ScienceDirect, and the Congressional Budget Office
A 58% performance gap.
Billions left on the table.
MCC (Matthews Correlation Coefficient) measures how well AI models predict drug outcomes. With balanced data, accuracy jumps dramatically.
Based on published benchmarks comparing biased vs. balanced training datasets.
Current AI drug prediction accuracy
Projected accuracy with balanced negative data
We ran the experiment. Here's the data.
Phase 1 complete: a computational degradation study using real NCATS kinetic solubility data and the SC2 blind benchmark. No synthetic data. No assumptions.
Blue line (primary):MCC at logS < −5 threshold. As very insoluble compounds are systematically removed from training data, model accuracy on the SC2 benchmark drops from 0.20 to 0.05 — a 75% collapse. The gray dashed line at ~65% represents AqSolDB's actual imbalance level, where current state-of-the-art models operate. Inactiva's data moves models left on this curve.
The world's first negative
pharmaceutical data foundry.
High-throughput robotic foundry generating standardized, validated negative data — starting with our primary focus:
Solubility
Compounds that fail dissolution — the most common early-stage failure mode. Our robotic assays screen thousands of compounds for solubility, generating the negative data that AI models need.
Know which compounds dissolve before wasting months in the lab.
Permeability
Compounds that cannot cross biological membranes — critical for oral bioavailability.
Predict oral drug viability early.
Stability
Compounds that degrade under physiological conditions before reaching their target.
Eliminate unstable candidates before costly trials.
Toxicity
The most expensive failure mode. Structured negative toxicity data no public dataset contains.
Catch toxic compounds before they reach patients.
Ready to move forward? Partner with us.
Get In TouchFrom robot to dataset
in hours, not months.
End-to-end automated pipeline — robot to ML-ready dataset.
Custom Robot
Custom-built 3D-printed liquid handler (<$600 to build) designed for high-throughput screening.
PEG Stress Test
Automated stress test pushes proteins across hundreds of conditions per run to find their breaking points.
Optical Measurement
Real-time light-based measurement detects if a protein clumps — a key failure signal.
Data Logged
Every result — pass or fail — is logged into clean, standardized datasets ready for machine learning.
A cost advantage
competitors can't match.
- Locked proprietary software
- Expensive service contracts
- Months to reconfigure an assay
- Unified Python orchestration
- Push code to change protocol
- Full fleet for price of one arm
Lower operating costs vs United States
Robot fleet for the price of one arm
Direct competitors in negative pharma data
Let's talk.
Whether you're an investor, pharma partner, or researcher — we'd love to hear from you.