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IC_EX:G16C* AND EN_ALLTXT:(coronavirus OR coronaviruses OR coronaviridae OR coronavirinae OR orthocoronavirus OR orthocoronaviruses OR orthocoronaviridae OR orthocoronavirinae OR betacoronavirus OR betacoronaviruses OR betacoronaviridae OR betacoronavirinae OR sarbecovirus OR sarbecoviruses OR sarbecoviridae OR sarbecovirinae OR "severe acute respiratory syndrome" OR sars OR "2019 ncov" OR covid)

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Analysis

1.WO/2022/081920SYSTEMS FOR AND METHODS OF TREATMENT SELECTION
WO 21.04.2022
Int.Class G01N 33/567
GPHYSICS
01MEASURING; TESTING
NINVESTIGATING OR ANALYSING MATERIALS BY DETERMINING THEIR CHEMICAL OR PHYSICAL PROPERTIES
33Investigating or analysing materials by specific methods not covered by groups G01N1/-G01N31/131
48Biological material, e.g. blood, urine; Haemocytometers
50Chemical analysis of biological material, e.g. blood, urine; Testing involving biospecific ligand binding methods; Immunological testing
53Immunoassay; Biospecific binding assay; Materials therefor
566using specific carrier or receptor proteins as ligand binding reagent
567utilising isolate of tissue or organ as binding agent
Appl.No PCT/US2021/055096 Applicant THE REGENTS OF THE UNIVERSITY OF CALIFORNIA Inventor KROGAN, Nevan
The disclosure relates to a system comprising software that predicts responsiveness of subjects to certain disease modifying drugs. Embodiments of the disclosure include methods comprising calculating a differential interaction score (DIS), correlating the DIS with the likelihood that a dysfunctional protein-protein interaction is the causal agent of a disease or disorder, and identifying a subject responsive to a treatment based upon the causal agent.
2.WO/2022/013781PEPTIDE EPITOPE VACCINES FOR COVID-19 AND METHOD OF DESIGNING, MAKING AND USING THE SAME
WO 20.01.2022
Int.Class A61K 39/215
AHUMAN NECESSITIES
61MEDICAL OR VETERINARY SCIENCE; HYGIENE
KPREPARATIONS FOR MEDICAL, DENTAL, OR TOILET PURPOSES
39Medicinal preparations containing antigens or antibodies
12Viral antigens
215Coronaviridae, e.g. avian infectious bronchitis virus
Appl.No PCT/IB2021/056355 Applicant UNIVERSITY OF SOUTHERN CALIFORNIA Inventor BOGDAN, Paul
Computer systems and computer implemented methods are presented for designing and making vaccines to pathogens, particular viral pathogens. Vaccine compositions for COVID-19 are also disclosed, as well as method of using the same.
3.202141019538IN SILICO EVALUATION AND SYNTHESIS OF NOVEL SULFONAMIDES AS PROMISING ANTI-VIRAL LEAD MOLECULES DOCKED AGAINST ANTI-COVID-19 PROTEIN TARGETS: SARS-COV-2 MAIN PROTEASE
IN 07.05.2021
Int.Class A61K /
AHUMAN NECESSITIES
61MEDICAL OR VETERINARY SCIENCE; HYGIENE
KPREPARATIONS FOR MEDICAL, DENTAL, OR TOILET PURPOSES
Appl.No 202141019538 Applicant SAMPATH CHINNAM Inventor SAMPATH CHINNAM
COVID-19 pandemic has led us to design and develop novel organic molecules as medicinally promising lead molecules which can prevent the SARS-CoV-2 virus of the infected patients. The current invention provides potential anti-viral drugs docked against anti-COVID-19 protein targets: SARS-CoV-2 main protease, drug-likeness, efficacy, molecular docking, physicochemical and pharmacokinetic studies of novel synthesized sulfonamide analogues. Physicochemical and pharmacokinetic properties have been evaluated on the basis of certain parameters like Lipinski rule of 5 (RO5 rule) and ADMET (absorption, distribution, metabolism, excretion and toxicity). All the synthesized compounds follow Lipinski rule of five (RO5 rule) and the compounds followed the range of rotational bonds, hydrogen bond acceptors (HBA), hydrogen bond donors (HBD), topological surface area (TPSA), and number of violations, etc. All these compounds shown good pharmacokinetic properties, zero renal OCT2 substrate toxicity and negligible toxicity values. BOILED-egg model was carried out for evaluating the gastrointestinal absorption and brain penetration effect. Compounds 3b and 3d comes under white region of egg and exhibited good gastrointestinal absorption, whereas, 3a, 3c, 3e and 3f compounds fall under yellow region (yolk) of egg which showed good brain penetration effect. All novel sulfonamide analogues including commercially available anti-COVID-19 drugs, Hydroxychloroquine and Umifenovir docked with anti-COVID-19 protein targets, i.e., PDB: 6VWW & 6Y2E. Compound 3c when docked with PDB: 6VWW shown maximum energy of -22.06 kcal/mol with two hydrogen binding interactions which are better than marketed drugs. Similarly, compound 3a exhibited highest energy of -14.00 kcal/mol.
4.202200591943D PHARMACOPHORE MODEL FOR THE RAPID COMPUTATIONAL SCREENING OF SARS-COV-2 MODULATORS AND COMPOSITIONS AND METHODS THEREOF
US 24.02.2022
Int.Class G16C 20/40
GPHYSICS
16INFORMATION AND COMMUNICATION TECHNOLOGY SPECIALLY ADAPTED FOR SPECIFIC APPLICATION FIELDS
CCOMPUTATIONAL CHEMISTRY; CHEMOINFORMATICS; COMPUTATIONAL MATERIALS SCIENCE
20Chemoinformatics, i.e. ICT specially adapted for the handling of physicochemical or structural data of chemical particles, elements, compounds or mixtures
40Searching chemical structures or physicochemical data
Appl.No 17409433 Applicant FluoroMe, Inc., Inventor Dimitrios VLACHAKIS

The invention encompasses compositions and compounds for inhibiting CoV2 Spike GP and human ACE2 proteins and a 3D pharmacophore model described herein provides the means for rapid, high-throughput virtual screening of potential anti-CoV2 modulators thus facilitating, optimizing and speeding up the search for the discovery of a potent anti-COVID-19 agent and methods of treatment and prevention thereof.

5.202221008681INHIBITORY POTENTIAL OF RESVERATROL AND ITS NATURAL ANALOGUES AGAINST RNA DEPENDANT RNA POLYMERASE (RDRP) OF RHIZOPUS ORYZAE IN MUCORMYCOSIS THROUGH IN SILICO INVESTIGATIONS
IN 11.03.2022
Int.Class C07K /
CCHEMISTRY; METALLURGY
07ORGANIC CHEMISTRY
KPEPTIDES
Appl.No 202221008681 Applicant MITHUN RUDRAPAL Inventor MITHUN RUDRAPAL
Mucormycosis (or black fungus infection) is a life-threatening, but rare fungal infection with predominant occurrence in immune-suppressed patients following the SARS-CoV-2 infection/ COVID-19 illness. Rhizopus oryzae causes about 70% of all cases of mucormycosis. The present invention provides insights to in silico investigations of resveratrol and its two natural analogues, piceatannol, and 3,5,4'-trimethoxy-trans-stilbene for their development as successful antifungal agents targeting the R. O. specific RNA dependant RNA polymerase (R. O. RdRp) to combat the deadly mucormycosis. In in silico studies, molecular docking, molecular dynamics (MD) simulations, density functional theory (DFT), drug-likeness and ADMET predictions were performed. The Ramachandran plot validated the predicted structure, and the the 3D structure predicted using the homology model was of acceptable quality. The findings of docking and MD simulations revealed that the resveratrol and its two natural analogues could potentially inhibit the R. O. RdRp. The present invention proves that resveratrol and its two natural analogues are the potential inhibitors of R. O. RdRp. Resveratrol and piceatannol possess promising in silico inhibitory potential against R. oryzae RdRp based on docking, molecular dynamics and DFT studies.
6.202111010632A NOVEL DRUG FOR SARS-COV-2
IN 28.05.2021
Int.Class G16B /
GPHYSICS
16INFORMATION AND COMMUNICATION TECHNOLOGY SPECIALLY ADAPTED FOR SPECIFIC APPLICATION FIELDS
BBIOINFORMATICS, i.e. INFORMATION AND COMMUNICATION TECHNOLOGY SPECIALLY ADAPTED FOR GENETIC OR PROTEIN-RELATED DATA PROCESSING IN COMPUTATIONAL MOLECULAR BIOLOGY
Appl.No 202111010632 Applicant Abhay J. Gandhi Inventor Abhay J. Gandhi
COVID-19, a new strain of coronavirus (CoV), was identified in Wuhan, China, in 2019. It then extended across the globe and was termed as pandemic in 2020. Though no vaccine or drug is available to combat this disease, it is necessary to look over it through alternative sciences. Available symptoms of covid 19 was thoroughly studied and reviewed through Ayurveda classics to understand the nature of the disease into Ayurevdic perspective. Other available sources from internet, pre prints, etc. The molecular Docking were done by Pyrx Software with Autodock. The Lipinski Rule of Five data generated from Swiss ADME software and Target prediction of selected phytoconstituents were done from Swiss target prediction.In Ayurveda, it can be considered as Janapadaudhwans, vaat-kafaj sannipatik jwara, Aupsargik vyadhi and Dhatupaka awastha. In the molecular docking study the binding energy and inhibition of 6 Gingesulphonic acid from Zingiber Officinalis are greater than Hydroxychloroquine and quinine. Most of the selected phytoconstituents follow Lipinski rule of five. Target prediction of selected phytoconstituents were done on target of SARS-COV-2, humoral immunity and antiviral. Every selected phytoconstituents were work on minimum of the target.Thus, from the above results obtained from reviewing Ayurveda classics and from the results obtained after virtual screening of selected drugs we can conclude that Ayurveda drugs can have appreciable results in combating Covid 19.
7.WO/2022/098068SERVER AND METHOD FOR PREDICTING INFECTION RISK LEVEL OF PATHOGEN IN TARGET AREA USING MOLECULAR DIAGNOSTIC TEST DATA, AND NON-TRANSITORY COMPUTER-READABLE STORAGE MEDIUM
WO 12.05.2022
Int.Class G16H 50/80
GPHYSICS
16INFORMATION AND COMMUNICATION TECHNOLOGY SPECIALLY ADAPTED FOR SPECIFIC APPLICATION FIELDS
HHEALTHCARE INFORMATICS, i.e. INFORMATION AND COMMUNICATION TECHNOLOGY SPECIALLY ADAPTED FOR THE HANDLING OR PROCESSING OF MEDICAL OR HEALTHCARE DATA
50ICT specially adapted for medical diagnosis, medical simulation or medical data mining; ICT specially adapted for detecting, monitoring or modelling epidemics or pandemics
80for detecting, monitoring or modelling epidemics or pandemics, e.g. flu
Appl.No PCT/KR2021/015744 Applicant SEEGENE, INC. Inventor CHOI, Youn Chul
The disclosure relates to a server for predicting the infection risk level of pathogen in a target area using molecular diagnostic test data, a method therefor, and a non-transitory computer-readable storage medium thereof.
8.202111015404DISCOVEY OF CATALPOL FOR TREATING CORONAVIRIDAE FAMILY OF VIRUS”
IN 28.01.2022
Int.Class G16C /
GPHYSICS
16INFORMATION AND COMMUNICATION TECHNOLOGY SPECIALLY ADAPTED FOR SPECIFIC APPLICATION FIELDS
CCOMPUTATIONAL CHEMISTRY; CHEMOINFORMATICS; COMPUTATIONAL MATERIALS SCIENCE
Appl.No 202111015404 Applicant Sanskriti University Inventor Dr. Vishal M. Balaramnavar
A method (300) for identifying a binding site of an Catalpol , the method comprising: developing pharmacophore models to extract features from the; validating the developed pharmacophore models by comparing with pre-defined models of existing coronavirus drugs; performing a ligand-based virtual screening (first virtual screening) of a database of drugs with the validated pharmacophore models; performing a structure-based virtual screening (second virtual screening) of the validated pharmacophore models by structural docking of a target protein into the validated pharmacophore models; assigning a score to each pharmacophore model of the Catalpol in order to identify the validated pharmacophore models with a high binding affinity and efficiency; and comparing the score obtained from the ligand-based virtual screening (first virtual screening) and the structure-based virtual screening (second virtual screening) for classifying the scored pharmacophore models based on the target protein binding affinity and efficiency for the coronaviridae family of virus.
9.202111053494COMPUTATIONAL REPURPOSING OF THE LOSARTAN AS NOVEL SARS-COV-2 INHIBITORY POTENTIAL
IN 28.01.2022
Int.Class G16C /
GPHYSICS
16INFORMATION AND COMMUNICATION TECHNOLOGY SPECIALLY ADAPTED FOR SPECIFIC APPLICATION FIELDS
CCOMPUTATIONAL CHEMISTRY; CHEMOINFORMATICS; COMPUTATIONAL MATERIALS SCIENCE
Appl.No 202111053494 Applicant Galgotias University Inventor Mukesh Masand
A method (300) for identifying a binding site of a Losartan drug, the method comprising: developing pharmacophore models to extract features from the Losartan drug; validating the developed pharmacophore models by comparing with pre-defined models of existing coronavirus drugs; performing a ligand-based virtual screening (first virtual screening) of a database of drugs with the validated pharmacophore models; performing a structure-based virtual screening (second virtual screening) of the validated pharmacophore models by structural docking of a target protein into the validated pharmacophore models; assigning a score to each pharmacophore model of the Losartan drug in order to identify the validated pharmacophore models with a high binding affinity and efficiency; and comparing the score obtained from the ligand-based virtual screening (first virtual screening) and the structure-based virtual screening (second virtual screening) for classifying the scored pharmacophore models based on the target protein binding affinity and efficiency for the coronaviridae family of virus.
10.202111053981COMPUTATIONAL REPURPOSING OF THE VALSARTAN AS NOVEL SARS-COV-2 INHIBITORY POTENTIAL
IN 28.01.2022
Int.Class G16C /
GPHYSICS
16INFORMATION AND COMMUNICATION TECHNOLOGY SPECIALLY ADAPTED FOR SPECIFIC APPLICATION FIELDS
CCOMPUTATIONAL CHEMISTRY; CHEMOINFORMATICS; COMPUTATIONAL MATERIALS SCIENCE
Appl.No 202111053981 Applicant Galgotias University Inventor Mukesh Masand
A method (300) for identifying a binding site of a Valsartan drug, the method comprising: developing pharmacophore models to extract features from the Valsartan drug; validating the developed pharmacophore models by comparing with pre-defined models of existing coronavirus drugs; performing a ligand-based virtual screening (first virtual screening) of a database of drugs with the validated pharmacophore models; performing a structure-based virtual screening (second virtual screening) of the validated pharmacophore models by structural docking of a target protein into the validated pharmacophore models; assigning a score to each pharmacophore model of the Valsartan drug in order to identify the validated pharmacophore models with a high binding affinity and efficiency; and comparing the score obtained from the ligand-based virtual screening (first virtual screening) and the structure-based virtual screening (second virtual screening) for classifying the scored pharmacophore models based on the target protein binding affinity and efficiency for the coronaviridae family of virus.