Ioannis N Grigoriadis

Biogenea Pharmaceuticals Ltd, Greece

Title: Quantum Phases and Chern-Simons Geometrics for the generation of a ligand targeting COVID-19-SARS-COV-2 SPIKE D614G binding sites

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Biography

Ioannis G. Grigoriadis, PharmD. is the Chairman of the WAMS International Board of Pharmaceutical Biotechnology and holds a Pharmacy Degree by the Aristotle University Pharmaceutical School, PHARMACIST DEPARTMENT OF PHARMACEUTICAL SCHOOL OF ARISTOTLE UNIVERSITY OF THESSALONICA CLINICIAN PHARMACIST – MANAGER OF EXECUTIVE PHARMACEUTICAL FACILITIES ARISTOTLE UNIVERSITY OF THESSALONIKI Inventor, Scientific Publications in Medical Genetics in Biogenea Pharmaceuticals Ltd and is currently member of the International NETCORD Foundation www.netcord.org . He is also member of the European Committee of the International Society for Cellular Therapies and expert opinion on biotechnology (WIWearn). He is Scientific Director of BIOGENEA Pharmaceuticals Ltd, in charge of communication for the European Medicines Organization (www.ema.europa.eu) and scientific advisor to REGENETECH, a NASA spin-off company. He holds various biotechnology patents and diplomas, being author of numerous scientific papers and publications. He is member of peer-Business Scientific Project committees for international academic conventions and in charge of accreditations for the clinical laboratory of BIOGENEA Pharmaceuticals Ltd by the National Accreditation Authority of Greece (www.esyd.gr). Currently serves as scientific head of Drug Design & Development Unit publicitate his patents to Industrial Property Organization www.obi.gr. Ioannis Grigoriadis is the Inventor of the GlybatomaqTM “MIRACLE MOLECULES” AI Computer-Designed, Quantum Thinking 3D Small Molecules for Brain Cancer.

Abstract

SARS coronavirus 2 (SARS-CoV-2) in the viral spike (S) encoding a SARS-COV-2 SPIKE D614G mutation protein predominate over time in locales  revealing the dynamic aspects of its key viral processes where it is found, implying that this change enhances viral transmission. It has also been observed that retroviruses infected ACE2-expressing cells pseudotyped with SG614 that is presently affecting a growing number of countries markedly more efficiently than those with SD614. The availability of newer powerful computational resources, molecular modeling techniques, and cheminformatics quality data have made it feasible to generate reliable algebraic calculations to design new chemical entities, merging chemicals, recoring natural products, and a lot of other substances fuelling further development and growth of this AI-quantum based drug design  field to balance the trade-off between the structural complexity and the quality of such biophysics predictions that cannot be obtained by any other method. In this paper, we strongly combine topology geometric methods targeting at the atomistic level the protein apparatus of the SARS-COV-2 virus that are simple in machine learning anti-viral characteristics, to propose computer-aided rational drug design strategies efficient in computing docking usage, and powerful enough to achieve very high accuracy levels for this in-silico effort for the generation of the AI-Quantum designed molecule the RoccustyrnaTM small molecule, a multi-targeting druggable scaffold (1S,2R,3S)?2?({[(1S,2S,4S,5R)?4?ethenyl?4?sulfonylbicyclo[3.2.0]heptan?2?yl]oxy}amino)?3?[(2R,5R)?5?(2?methyl?6?methylidene?6,9?dihydro?3H?purin?9?yl)?3?methylideneoxolan?2?yl]phosphirane?1?carbonitrile targeting the COVID-19-SARS-COV-2 SPIKE D614G mutation using Chern-Simons Topology Euclidean Geometric in a Lindenbaum-Tarski generated QSAR automating modeling and Artificial Intelligence-Driven Predictive Neural Networks.