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Abstract

In this paper we outline a non-perturbative quantum relativity theory. Subsequently an actual design of a nanotech energy reactor is based on spacetime vacuum fluctuation of the said quantum relativity theory. Using a compact heap of Fullerene nano particle moduli of a nano matrix device we propose that by maximizing the Casimir forces between these particles as a desirable effect, we can achieve a gradual rather than a sudden implosion pressure. We expect that this will result in a mini holographic universe from which energy can be extracted in a way to constitute a nano energy reactor and function effectively on a hybrid principle somewhere between a Casimir effect and a cold fusion process based on the fusion algebra of a highly structured golden ring quantum field theory. The present theory depends upon many concepts and results, in particular J. Schwinger’s source theory as well as the modern theory of quantum sets, nonlinear dynamics, chaos and chaotic fractals with applications on a Non-Perturbative Quantum Relativity Theory Leading to a Casimir-Dark Energy Nanotech Reactor Proposal on a combined molecular docking-based and pharmacophore-based target prediction strategy through a probabilistic fusion method for target ranking of anti-HIV-I P24-derived peptide mimic promising pharmacophores.

Keywords

Non-Perturbative; Quantum Relativity Theory; Leading Casimir-Dark Energy; Nanotech Reactor; combined; molecular docking-based; pharmacophore-based; target; prediction strategy; probabilistic fusion; ranking; anti-HIV-I P24-derived peptide mimic; promising pharmacophores;

Article Type

Research Article – Abstract

Publication history

Received: Sep 20, 2017
Accepted: Sep 25, 2017
Published: Oct 01, 2017

Citation

Grigoriadis Ioannis, Grigoriadis George, Grigoriadis Nikolaos, George Galazios (2017) On a Non-Perturbative Quantum Relativity Theory Leading to a Casimir-Dark Energy Nanotech Reactor Proposal on a combined molecular docking-based and pharmacophore-based target prediction strategy through a probabilistic fusion method for target ranking of anti-HIV-I P24-derived peptide mimic promising pharmacophores.

Authors Info

Grigoriadis Nikolaos
Department of IT Computer Aided Personalized Myoncotherapy, Cartigenea-Cardiogenea, Neurogenea-Cellgenea, Cordigenea-HyperoligandorolTM,
Biogenea Pharmaceuticals Ltd,
Thessaloniki, Greece;

Grigoriadis Ioannis
Department of Computer Drug Discovery Science, BiogenetoligandorolTM,
Biogenea Pharmaceuticals Ltd,
Thessaloniki, Greece;

Grigoriadis George
Department of Stem Cell Bank and ViroGeneaTM,
Biogenea Pharmaceuticals Ltd,
Thessaloniki, Greece;

George Galazios
Professor of Obstetrics and Gynecology,
Democritus University of Thrace,
Komotini, Greece;

E-mail: biogeneadrug@gmail.com

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