PROJECT JupitR

 JupitR predicts whether drug candidates will succeed or fail in human trials by simulating how molecules interact with human biology—capturing quantum charge redistribution, receptor harmonics, solvent behavior, and aggregation risk that traditional docking and AI methods miss

 Yes. JupitR is operational and has already been used to analyze many drug. It runs via custom code modules and validated ligand–receptor datasets 

 Biopharma, biotech, and translational researchers looking to prioritize clinical candidates, reduce late-stage attrition, and flag off-target risks early. 

 Future versions will expand into companion diagnostics, synthetic pathway prediction, and biomarker stratification to support full pipeline decision-making 

 It calculates receptor–ligand field harmonics using the Hadron UVE engine: a physics-based model that runs a fixed sequence of quantum and biophysical corrections to simulate biologically relevant outcomes 

Because JupitR is built from a proprietary field-theoretic approach—focused on waveform collapse- not traditional docking or black-box machine learning. It integrates physics, biology, and chemistry in a unique locked-sequence engine 

AI often learns statistical correlations. JupitR simulates mechanistic causation. Where AI may guess, JupitR models how and why biological effects occur, using deterministic or adaptive UVE scoring modes 

 Yes. The UVE framework, harmonic scoring logic, and the receptor prioritization algorithm are protected via trade secrets and controlled dissemination. Future filings may include patents on derivative modules.