Your compound library and clinical trial data are growing. So is your storage spend and your IP exposure.
RNDA encodes molecular structures and trial data and discards the originals. Search millions of compounds — without retaining a single raw structure.
Request a Pharmaceutical POC →The Problem
Drug discovery requires searching chemical libraries of millions of compounds for structural similarity. Storage and retrieval of 3D molecular structures at scale is the infrastructure bottleneck. IP protection requires controlling access to proprietary compound data.
How RNDA Solves It
19.4x compression on real 3D molecular structures
9,379 real 3D molecular structures encoded in POC. Real chemical data, not simplified 2D representations.
Structural similarity at 28ms
Query entire molecular libraries for structurally similar compounds in under 30ms. Similarity scores 0.50–0.63 across real molecular data.
Proprietary compound protection
Competitor compounds can be encoded and queried against your library without your structures ever leaving the system. The signatures carry structural meaning without carrying the structures.
How RNDA Applies
Storage Elimination
Real 3D molecular structures compressed 19.4x. A 500 TB pharma data lake — compounds, clinical trial data, molecular simulations — costs ~$14,300/year post-RNDA versus $138K/year uncompressed, enabling long-term retention of entire chemical libraries at minimal cost.
Privacy Protection
Proprietary compound structures represent billions in R&D investment. RNDA encodes molecular geometry and permanently discards the raw structure — competitors cannot reverse-engineer synthesis pathways or compound topology from a 256-byte signature.
Compliance Management
FDA 21 CFR Part 11 and ICH data integrity requirements demand long-term retention of clinical and analytical records. RNDA-compressed archives satisfy retention mandates while preventing unauthorized access to proprietary compound data.
Intelligent Retrieval
Query entire molecular libraries for structurally similar compounds in 28ms. Proven on 9,379 real 3D molecular structures in SDF format — discrimination gap 0.77 across diverse chemical space. Find structurally similar drug candidates without retaining raw structural data.
Collaborative Intelligence
Biotech partners, contract research organizations, and regulatory agencies receive scoped access to encoded compound libraries without raw structural data crossing boundaries. The IP is protected even as the science is shared.
Storage Impact
Industry stat: Major pharmaceutical companies maintain compound libraries of 1–10 million structures; large pharma data lakes exceed 500 TB of molecular, clinical, and research data
500 TB × 20% × $276/TB ÷ 19.4x compression (molecular data)
500 TB pharma data lake saves ~$14,300/year — 19.4x compression on real 3D molecular structures, 0.77 discrimination gap
Proof of Concept Results
Real data. Measured numbers. No synthetic results.
Source: Real 3D molecular structures (SDF format)
What Becomes Possible
"A competitor files a patent on a novel compound. Your team encodes the compound structure and queries your proprietary library for similar structures in 28ms. The competitor's structure is discarded immediately after encoding."
Ready to see it on your data?
Every number on this page came from a real POC. Yours will be built the same way — against your actual data type, measured compression, real query latency.
Request a Pharmaceutical POC →