msq.mosaeQ.com: A Quantum Ontology for Translational Preclinical Safety Concordance

This preprint describes the proprietary framework behind msq.mosaeQ.com (Molecular Structure Quantization), a quantum ontology designed by the author to improve translational drug safety. Traditional preclinical-to-clinical concordance often fails due to two bottlenecks: the "vocabulary gap" in mapping animal findings to human adverse events, and "plasma-tissue dissociation," where systemic blood levels fail to predict localized tissue toxicity.  Methodology: Every molecular entity is encoded as a triplet of angular coordinates on a unit sphere via a proprietary Non-Euclidean Molecular Partitioning (NEMP) algorithm. These coordinates are mapped into a unit quaternion on an S3 hypersphere. This allows for geodesic distance-based comparisons of molecules based on their structural "instruction set" (LogP, TPSA, and Molecular Weight) rather than nomenclature.  

Key Contributions:

• Name-Matching Resolution: Demonstrates how structural proximity on

  S3 can resolve name-matching failures in public databases (e.g., resolving Melflufen to its parent alkylator Melphalan).

• Concordance Scored against Public Databases: Systematic cross-referencing against FDA FAERS, NTP DrugMatrix, Open TG-GATEs, and NLM DailyMed.

• Sequestration Mapping: Incorporates a structural framework to identify potential "tissue hotspots" and lysosomal sequestration liabilities that are invisible to standard plasma-based pharmacokinetics.

The platform serves as a New Approach Methodology (NAM) to provide a computational baseline for compounds where human post-market data is absent or divergent.