Abstract
Gallilean Black Hole Transformations for the Anti COVID19 RoccuffirnaTM Drug Design
Author(s): Grigoriadis IoannisSARS coronavirus 2 (SARS-CoV-2) encoding a D614G mutation in the viral spike (S) protein predominate over time in locales where it is found, implying that this change enhances viral transmission. It has also been observed that retroviruses pseudotyped with SG614 infected ACE2-expressing cells markedly more efficiently than those with SD614. It is thought that all of the rich content in the present-day Universe based on an array of recent observations developed through gravitational amplification of primeval density fluctuations generated in the very early phase of cosmic evolution. In this paper, we strongly combine machine learning characteristics, efficient in computing resource usage, and powerful to achieve very high accuracy levels for the in-silico generation of the RoccuffirnaTM small molecule, a less toxic nano-ligand targeted the COVID-19-D614G mutation using Quantum Kerr-(A)dS and Myers–Perry black microBlackHole-Inspired Gravitationals for both Euclidean and Lorentzian signatures in Practice. We provide also an extensive toolbox of methods for performing quantum communication, Neural Matrix Factorizations, cryptography, Schrodinger inspired docking algorithms, teleportation and other information-theoretic tasks in MathCast programming language, and compared these algorithms by means of mean percentile free energy ranking, in a new recall-based evaluation metric for the in-silico design of a Novel Series of Sivirinavir TMQMMMCoRoNNARRFr anti-(nCoV-19) annotated ligands. We finally, discuss various general results including heuristic horizon topology, and nearhorizon fragmentation symmetry ranging from supergravity theories to enhance the Roccuffirna’s gravity to trap the SARS-COV-2 viruses in practice.