Kerr-(A)Ds, Myers–Perr black-hole Galilean ον gravitational
transformations for the anti-COVID-19 RoccuffirnaTM evolutionary drug
design.
Abstract
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 to achieve very high accuracy levels
for the in-silico generation of the RoccuffirnaTM small molecule, a
ligand targeted the SARS-COV-2 virus main protease (M pro ) using
Quantum Kerr-(A)dS and Myers–Perry black microBlackHole-Inspired
Gravitational for both Euclidean and Lorentzian signatures in Practice.
We provide also an extensive toolbox of methods for performing quantum
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 the Novel Series of the RoccuffirnTMQMMMCoRoNNARRFr
anti-(nCoV-19) ligands. In this paper we in-silico designed new drug
leads that target the COVID-19 virus main protease (M pro ). M pro, a
key CoV enzyme, which plays a pivotal role in mediating viral
replication and transcription, and discuss various general results
including Galilean transformation to a rigid QMMM heuristic horizon
topology, and near-horizon fragmentation symmetry ranging from
supergravity theories to enhance the Roccuffirna’s gravity to trap the
SARS-COV-2 viruses in practice.