Goods & Services

Pharmaceuticals for the treatment of immunoinflammatory diseases and disorders; chemical preparations for pharmaceutical or medical purposes for the treatment of immunoinflammatory diseases and disorders. Downloadable software for pharmaceutical product development, for research and data analysis to screen for and identify molecular interactions, compounds and toxicity detection, and for drug discovery and chemical development; downloadable computer software using artificial intelligence (AI) for pharmaceutical product development, for research and data analysis to screen for and identify molecular interactions, compounds and toxicity detection, and for drug discovery and chemical development. Pharmaceutical research services; pharmaceutical products development; pharmaceutical drug development services; conducting scientific and pharmaceutical research and data analysis for others to screen for and identify molecular interactions, compounds and toxicity detection; artificial intelligence as a service (AIAAS) services featuring software using artificial intelligence (AI) for drug discovery and chemical development; providing on-line non-downloadable software for pharmaceutical product development, for research and data analysis to screen for and identify molecular interactions, compounds and toxicity detection, and for drug discovery and chemical development; providing on-line non-downloadable software using artificial intelligence (AI) for pharmaceutical product development, for research and data analysis to screen for and identify molecular interactions, compounds and toxicity detection, and for drug discovery and chemical development.

Abstract

Provided herein are compositions and method of treating a disease or condition with increased 6-phosphofructo-2-kinase/fructose-2,6-biphosphatase 2 (PFKFB2) activity comprising: administering to a subject in need of a treatment for the heart disease or cancer an effective amount of a selective inhibitor of 6-phosphofructo-2-kinase/fructose-2,6-biphosphatase 2 (PFKFB2) comprising: contacting PFKFB2 with a molecule of formula: Provided herein are compositions and method of treating a disease or condition with increased 6-phosphofructo-2-kinase/fructose-2,6-biphosphatase 2 (PFKFB2) activity comprising: administering to a subject in need of a treatment for the heart disease or cancer an effective amount of a selective inhibitor of 6-phosphofructo-2-kinase/fructose-2,6-biphosphatase 2 (PFKFB2) comprising: contacting PFKFB2 with a molecule of formula:

IPC Classes  ?

• A61K 31/433 - Thiadiazoles
• C07D 285/135 - Nitrogen atoms

Abstract

In exemplary embodiments, inhibitors of Tyrosine Kinase 2 (TYK2), pharmaceutical formulations comprising these compounds, and methods of using these compounds to treat Inflammatory Bowel Diseases are provided.

IPC Classes  ?

• A61K 31/506 - PyrimidinesHydrogenated pyrimidines, e.g. trimethoprim not condensed and containing further heterocyclic rings
• A61K 31/5377 - 1,4-Oxazines, e.g. morpholine not condensed and containing further heterocyclic rings, e.g. timolol
• A61P 1/04 - Drugs for disorders of the alimentary tract or the digestive system for ulcers, gastritis or reflux esophagitis, e.g. antacids, inhibitors of acid secretion, mucosal protectants

Abstract

In exemplary embodiments, inhibitors of Tyrosine Kinase 2 (TYK2), pharmaceutical formulations comprising these compounds, and methods of using these compounds to treat Inflammatory Bowel Diseases are provided.

IPC Classes  ?

• C07D 239/48 - Two nitrogen atoms
• A61K 31/506 - PyrimidinesHydrogenated pyrimidines, e.g. trimethoprim not condensed and containing further heterocyclic rings
• A61P 29/00 - Non-central analgesic, antipyretic or antiinflammatory agents, e.g. antirheumatic agentsNon-steroidal antiinflammatory drugs [NSAID]

Abstract

Systems and methods for characterizing an interaction between a compound and a polymer include obtaining a plurality of sets of atomic coordinates. Each set of atomic coordinates comprises the compound bound to the polymer in a corresponding pose in a plurality of poses. Each respective set of atomic coordinates, or an encoding thereof, is sequentially inputted into a neural network, to obtain a corresponding initial embedding as output, thereby obtaining a plurality of initial embeddings. Each initial embedding corresponds to a set of atomic coordinates in the plurality of sets of atomic coordinates. An attention mechanism is applied to the plurality of initial embeddings, in concatenated form, to obtain an attention embedding. A pooling function is applied to the attention embedding to derive a pooled embedding. The pooled embedding is inputted into a model to obtain an interaction score of the interaction between the compound and the polymer.

IPC Classes  ?

• G16B 15/30 - Drug targeting using structural dataDocking or binding prediction
• G06N 3/045 - Combinations of networks
• G06N 3/0464 - Convolutional networks [CNN, ConvNet]
• G06N 3/09 - Supervised learning
• G16B 40/20 - Supervised data analysis

Abstract

Systems and methods for querying a combinatorial synthesis library comprising a plurality of compounds and representing a plurality of reaction types, where each reaction type maps to a plurality of reactants, and each reactant maps to a plurality of synthons, accepts a query in the form of a single graph into a molecular encoder model, thereby obtaining a query vector. The query vector is inputted into a reaction query generator model thereby obtaining a first reaction type and a first plurality of reactants. A synthon is determined for each reactant by inputting the reactant into a synthon query generator model. A set of synthons is therefore determined, each corresponding to a reactant in the first plurality of reactants. A molecular structure in the combinatorial synthesis library is identified that includes the set of synthons arranged in accordance with a synthesis rule associated with the first reaction type.

IPC Classes  ?

• G16C 20/64 - Screening of libraries
• G16C 20/40 - Searching chemical structures or physicochemical data

Goods & Services

Downloadable software for pharmaceutical product development, for research and data analysis to screen for and identify molecular interactions, compounds and toxicity detection, and for drug discovery and chemical development; Downloadable computer software using artificial intelligence (AI) for pharmaceutical product development, for research and data analysis to screen for and identify molecular interactions, compounds and toxicity detection, and for drug discovery and chemical development

Goods & Services

Pharmaceutical research services; Pharmaceutical products development; Pharmaceutical drug development services; conducting scientific and pharmaceutical research and data analysis for others to screen for and identify molecular interactions, compounds and toxicity detection; Artificial intelligence as a service (AIAAS) services featuring software using artificial intelligence (AI) for drug discovery and chemical development; Providing on-line non-downloadable software for pharmaceutical product development, for research and data analysis to screen for and identify molecular interactions, compounds and toxicity detection, and for drug discovery and chemical development; Providing on-line non-downloadable software using artificial intelligence (AI) for pharmaceutical product development, for research and data analysis to screen for and identify molecular interactions, compounds and toxicity detection, and for drug discovery and chemical development

Goods & Services

Pharmaceuticals for the treatment of immunoinflammatory diseases and disorders; chemical preparations for pharmaceutical or medical purposes for the treatment of immunoinflammatory diseases and disorders

Abstract

Systems and methods for identifying compounds in a combinatorial synthesis library (CSL) having specific molecular properties is provided. The CSL is accessed using an auto¬ encoder comprising an encoder and a decoder. The encoder maps compounds in the CSL into latent codes in a learned latent space. The decoder retrieves molecular structures of compounds using such codes. A policy selects a batch of latent codes, from which a plurality of compounds is identified using the decoder. Molecular properties of these compounds are determined and used to determine a reward function value for each compound. A probability density, under a target distribution in which latent codes are sampled through the auto- encoder from the CSL with probability proportional to tempered reward function values, is determined using the reward function values. Policy parameters are updated using at least a difference between the probability density and the target distribution, using an a-divergence based objective function.

IPC Classes  ?

• G16C 20/64 - Screening of libraries

Abstract

In exemplary embodiments, inhibitors of Tyrosine Kinase 2 (TYK2), pharmaceutical formulations comprising these compounds, methods of using these compounds to inhibit TYK2, and treat diseases such as autoimmune and inflammatory diseases are provided.

IPC Classes  ?

• C07D 401/14 - Heterocyclic compounds containing two or more hetero rings, having nitrogen atoms as the only ring hetero atoms, at least one ring being a six-membered ring with only one nitrogen atom containing three or more hetero rings
• A61K 31/506 - PyrimidinesHydrogenated pyrimidines, e.g. trimethoprim not condensed and containing further heterocyclic rings
• A61K 31/5377 - 1,4-Oxazines, e.g. morpholine not condensed and containing further heterocyclic rings, e.g. timolol
• A61K 31/675 - Phosphorus compounds having nitrogen as a ring hetero atom, e.g. pyridoxal phosphate
• A61P 17/06 - Antipsoriatics
• A61P 37/06 - Immunosuppressants, e.g. drugs for graft rejection
• C07D 239/48 - Two nitrogen atoms
• C07D 401/12 - Heterocyclic compounds containing two or more hetero rings, having nitrogen atoms as the only ring hetero atoms, at least one ring being a six-membered ring with only one nitrogen atom containing two hetero rings linked by a chain containing hetero atoms as chain links
• C07D 403/12 - Heterocyclic compounds containing two or more hetero rings, having nitrogen atoms as the only ring hetero atoms, not provided for by group containing two hetero rings linked by a chain containing hetero atoms as chain links
• C07D 405/14 - Heterocyclic compounds containing both one or more hetero rings having oxygen atoms as the only ring hetero atoms, and one or more rings having nitrogen as the only ring hetero atom containing three or more hetero rings
• C07D 413/14 - Heterocyclic compounds containing two or more hetero rings, at least one ring having nitrogen and oxygen atoms as the only ring hetero atoms containing three or more hetero rings
• C07D 491/048 - Ortho-condensed systems with only one oxygen atom as ring hetero atom in the oxygen-containing ring the oxygen-containing ring being five-membered
• C07D 491/107 - Spiro-condensed systems with only one oxygen atom as ring hetero atom in the oxygen-containing ring
• C07F 9/6558 - Heterocyclic compounds, e.g. containing phosphorus as a ring hetero atom containing at least two different or differently substituted hetero rings neither condensed among themselves nor condensed with a common carbocyclic ring or ring system

Abstract

In exemplary embodiments, inhibitors of Tyrosine Kinase 2 (TYK2), pharmaceutical formulations comprising these compounds, methods of using these compounds to inhibit TYK2, and treat diseases such as autoimmune and inflammatory diseases are provided.

IPC Classes  ?

• C07D 413/14 - Heterocyclic compounds containing two or more hetero rings, at least one ring having nitrogen and oxygen atoms as the only ring hetero atoms containing three or more hetero rings
• A61K 31/506 - PyrimidinesHydrogenated pyrimidines, e.g. trimethoprim not condensed and containing further heterocyclic rings
• A61K 31/5377 - 1,4-Oxazines, e.g. morpholine not condensed and containing further heterocyclic rings, e.g. timolol
• A61K 31/675 - Phosphorus compounds having nitrogen as a ring hetero atom, e.g. pyridoxal phosphate
• A61P 1/00 - Drugs for disorders of the alimentary tract or the digestive system
• A61P 17/06 - Antipsoriatics
• A61P 37/02 - Immunomodulators
• A61P 37/06 - Immunosuppressants, e.g. drugs for graft rejection
• C07D 239/48 - Two nitrogen atoms
• C07D 401/12 - Heterocyclic compounds containing two or more hetero rings, having nitrogen atoms as the only ring hetero atoms, at least one ring being a six-membered ring with only one nitrogen atom containing two hetero rings linked by a chain containing hetero atoms as chain links
• C07D 401/14 - Heterocyclic compounds containing two or more hetero rings, having nitrogen atoms as the only ring hetero atoms, at least one ring being a six-membered ring with only one nitrogen atom containing three or more hetero rings
• C07D 403/12 - Heterocyclic compounds containing two or more hetero rings, having nitrogen atoms as the only ring hetero atoms, not provided for by group containing two hetero rings linked by a chain containing hetero atoms as chain links
• C07D 405/14 - Heterocyclic compounds containing both one or more hetero rings having oxygen atoms as the only ring hetero atoms, and one or more rings having nitrogen as the only ring hetero atom containing three or more hetero rings
• C07D 491/048 - Ortho-condensed systems with only one oxygen atom as ring hetero atom in the oxygen-containing ring the oxygen-containing ring being five-membered
• C07D 491/107 - Spiro-condensed systems with only one oxygen atom as ring hetero atom in the oxygen-containing ring
• C07F 9/6558 - Heterocyclic compounds, e.g. containing phosphorus as a ring hetero atom containing at least two different or differently substituted hetero rings neither condensed among themselves nor condensed with a common carbocyclic ring or ring system

Abstract

In exemplary embodiments, inhibitors of Tyrosine Kinase 2 (TYK2), pharmaceutical formulations comprising these compounds, methods of using these compounds to inhibit TYK2, and treat diseases such as autoimmune and inflammatory diseases are provided.

IPC Classes  ?

• A61K 31/395 - Heterocyclic compounds having nitrogen as a ring hetero atom, e.g. guanethidine or rifamycins
• A61K 31/505 - PyrimidinesHydrogenated pyrimidines, e.g. trimethoprim
• A61K 31/13 - Amines, e.g. amantadine
• A61K 31/33 - Heterocyclic compounds

Abstract

Systems and methods for characterizing an interaction between a test compound and a polymer use coordinates for the polymer and a training dataset of compounds. Each compound has a positive pose with respect to target polymer coordinates with a positive interaction score and a negative pose of the compound with respect to the target polymer coordinates and a negative interaction score. The model is trained by applying, for each compound, at least: (i) a positive score for the positive pose as input to the model, against the positive interaction score of the compound, and (ii) a negative score for the negative pose as input to the model, against the negative interaction score of the compound, thereby adjusting parameters of the model. In turn, an output of the model is used, at least in part, to characterize the interaction between the test compound and the polymer.

IPC Classes  ?

• G16C 20/10 - Analysis or design of chemical reactions, syntheses or processes
• G16C 20/70 - Machine learning, data mining or chemometrics

Abstract

Described herein are compounds of Formula I, wherein the meanings of the substituents are indicated in the description, for modulating a reproductive and respiratory syndrome virus through multiple mechanisms, and to their use as medicaments for the prevention and/or treatment of diseases related to a reproductive and respiratory syndrome virus. Pharmaceutical compositions comprising said compounds of Formula I are also described.

IPC Classes  ?

• A61K 31/498 - Pyrazines or piperazines ortho- or peri-condensed with carbocyclic ring systems, e.g. quinoxaline, phenazine
• A61P 31/14 - Antivirals for RNA viruses

Abstract

Systems and methods for estimating graph edit distance (GED) between compounds are provided. A first graph representing a first compound comprises a plurality of nodes and a plurality of edges. Atoms of the first compound are represented by the nodes and bonds of the first compound are represented by the edges of the first graph. A second graph representing a second compound also comprises a plurality of nodes and a plurality of edges. Atoms of the second compound are represented by the nodes and bonds of the second compound are represented by edges of the second graph. The first graph is inputted into a model to generate a first latent embedding. The second graph is inputted into the model to generate a second latent embedding. An estimate (GED) between the two compounds is determined as the difference between the two latent embeddings.

IPC Classes  ?

• G16C 20/40 - Searching chemical structures or physicochemical data
• G16C 20/70 - Machine learning, data mining or chemometrics

Abstract

Provided is a composition for targeting OTUD7B. The composition includes a component sufficient to block or reduce OTUD7B-mediated deubiquitination of GβL in a cell. The component can include 7Bi and variants thereof. Also provided are methods of treating cancer and related conditions, including administering to a cancer patient a OTUD7B catalytic inhibitor, including a 7Bi or variant thereof.

IPC Classes  ?

• C07D 231/10 - Heterocyclic compounds containing 1,2-diazole or hydrogenated 1,2-diazole rings not condensed with other rings having two or three double bonds between ring members or between ring members and non-ring members
• C07D 231/02 - Heterocyclic compounds containing 1,2-diazole or hydrogenated 1,2-diazole rings not condensed with other rings
• C07D 403/14 - Heterocyclic compounds containing two or more hetero rings, having nitrogen atoms as the only ring hetero atoms, not provided for by group containing three or more hetero rings

Abstract

Disclosed are compositions for targeting 0TUD7A, the compositions having a component sufficient to block and/or reduce OTUD7A-mediated deubiquitination of EWS-FLI1 in a cell. The component can be 7Ai and variants thereof. The compositions can be used to treat Ewing sarcoma (EWS). Methods of using the disclosed compositions are also disclosed, including methods of treating EWS in a subject.t

IPC Classes  ?

• A61K 31/519 - PyrimidinesHydrogenated pyrimidines, e.g. trimethoprim ortho- or peri-condensed with heterocyclic rings
• A61P 35/00 - Antineoplastic agents

Abstract

Systems and methods for querying a combinatorial synthesis library comprising a plurality of compounds and representing a plurality of reaction types, where each reaction type maps to a plurality of reactants, and each reactant maps to a plurality of synthons, accepts a query in the form of a single graph into a molecular encoder model, thereby obtaining a query vector. The query vector is inputted into a reaction query generator model thereby obtaining a first reaction type and a first plurality of reactants. A synthon is determined for each reactant by inputting the reactant into a synthon query generator model. A set of synthons is therefore determined, each corresponding to a reactant in the first plurality of reactants. A molecular structure in the combinatorial synthesis library is identified that includes the set of synthons arranged in accordance with a synthesis rule associated with the first reaction type.

IPC Classes  ?

• C40B 50/00 - Methods of creating libraries, e.g. combinatorial synthesis
• C40B 60/02 - Integrated apparatus specially adapted for creating libraries, screening libraries and for identifying library members
• C40B 60/14 - Apparatus specially adapted for use in combinatorial chemistry or with libraries for creating libraries
• G06N 99/00 - Subject matter not provided for in other groups of this subclass

Abstract

Systems and methods for characterizing an interaction between a compound and a polymer include obtaining a plurality of sets of atomic coordinates. Each set of atomic coordinates comprises the compound bound to the polymer in a corresponding pose in a plurality of poses. Each respective set of atomic coordinates, or an encoding thereof, is sequentially inputted into a neural network, to obtain a corresponding initial embedding as output, thereby obtaining a plurality of initial embeddings. Each initial embedding corresponds to a set of atomic coordinates in the plurality of sets of atomic coordinates. An attention mechanism is applied to the plurality of initial embeddings, in concatenated form, to obtain an attention embedding. A pooling function is applied to the attention embedding to derive a pooled embedding. The pooled embedding is inputted into a model to obtain an interaction score of the interaction between the compound and the polymer.

IPC Classes  ?

• G06F 30/20 - Design optimisation, verification or simulation
• G06F 30/27 - Design optimisation, verification or simulation using machine learning, e.g. artificial intelligence, neural networks, support vector machines [SVM] or training a model
• G06G 7/75 - Analogue computers for specific processes, systems, or devices, e.g. simulators for component analysis, e.g. of mixtures, of colours
• G16C 20/50 - Molecular design, e.g. of drugs
• G16B 40/20 - Supervised data analysis
• B82Y 35/00 - Methods or apparatus for measurement or analysis of nanostructures
• G06F 16/28 - Databases characterised by their database models, e.g. relational or object models
• G06F 30/33 - Design verification, e.g. functional simulation or model checking
• G06G 7/58 - Analogue computers for specific processes, systems, or devices, e.g. simulators for chemical processes
• G16B 15/30 - Drug targeting using structural dataDocking or binding prediction
• G16C 20/70 - Machine learning, data mining or chemometrics
• G16N 20/00 -

Abstract

Disclosed herein are compositions comprising a compound of Formula (I) and methods for treating or prophylaxis of porcine reproductive and respiratory syndrome (PRRS) therewith (I).

IPC Classes  ?

• A61K 31/5377 - 1,4-Oxazines, e.g. morpholine not condensed and containing further heterocyclic rings, e.g. timolol
• A61K 31/498 - Pyrazines or piperazines ortho- or peri-condensed with carbocyclic ring systems, e.g. quinoxaline, phenazine
• A61P 31/14 - Antivirals for RNA viruses

Abstract

Inhibitors of a critical brain enzyme, N-acetyltransferase (ANAT), and methods of discovering, making and using the same for the treatment of disease are disclosed.

IPC Classes  ?

• A61K 31/498 - Pyrazines or piperazines ortho- or peri-condensed with carbocyclic ring systems, e.g. quinoxaline, phenazine
• A61K 31/505 - PyrimidinesHydrogenated pyrimidines, e.g. trimethoprim
• A61K 31/433 - Thiadiazoles
• A61K 31/4365 - Heterocyclic compounds having nitrogen as a ring hetero atom, e.g. guanethidine or rifamycins having six-membered rings with one nitrogen as the only ring hetero atom ortho- or peri-condensed with heterocyclic ring systems the heterocyclic ring system having sulfur as a ring hetero atom, e.g. ticlopidine
• A61K 31/4245 - Oxadiazoles

Abstract

Systems and methods for characterizing an interaction between a test compound and a polymer use coordinates for the polymer and a training dataset of compounds. Each compound has a positive pose with respect to target polymer coordinates with a positive interaction score and a negative pose of the compound with respect to the target polymer coordinates and a negative interaction score. The model is trained by applying, for each compound, at least: (i) a positive score for the positive pose as input to the model, against the positive interaction score of the compound, and (ii) a negative score for the negative pose as input to the model, against the negative interaction score of the compound, thereby adjusting parameters of the model. In turn, an output of the model is used, at least in part, to characterize the interaction between the test compound and the polymer.

IPC Classes  ?

• G16C 20/50 - Molecular design, e.g. of drugs
• G16B 15/30 - Drug targeting using structural dataDocking or binding prediction

Abstract

Disclosed are compositions for targeting 0TUD7A, the compositions having a component sufficient to block and/or reduce OTUD7A-mediated deubiquitination of EWS-FLI1 in a cell. The component can be 7Ai and variants thereof. The compositions can be used to treat Ewing sarcoma (EWS). Methods of using the disclosed compositions are also disclosed, including methods of treating EWS in a subject.t

IPC Classes  ?

• A61K 31/41 - Heterocyclic compounds having nitrogen as a ring hetero atom, e.g. guanethidine or rifamycins having five-membered rings with two or more ring hetero atoms, at least one of which is nitrogen, e.g. tetrazole
• A61K 31/415 - 1,2-Diazoles
• A61K 31/4162 - 1,2-Diazoles condensed with heterocyclic ring systems
• C07D 231/10 - Heterocyclic compounds containing 1,2-diazole or hydrogenated 1,2-diazole rings not condensed with other rings having two or three double bonds between ring members or between ring members and non-ring members
• C07D 487/02 - Heterocyclic compounds containing nitrogen atoms as the only ring hetero atoms in the condensed system, not provided for by groups in which the condensed system contains two hetero rings
• C07D 487/04 - Ortho-condensed systems

Abstract

Methods and compositions are provided for the treatment of Parkinson's Disease. Aspects of the methods include administering Miro1 reducer. Also provided are reagents and kits for practicing the subject methods. In some embodiments, a method is provided for reducing undesirable levels of Miro1 in a cell having depolarized or otherwise damaged mitochondria. In some embodiments the cell is in vivo, e.g. in an animal model for PD, in an individual diagnosed with PD, in a clinical trial for treatment of PD, and the like.

IPC Classes  ?

• A61K 31/506 - PyrimidinesHydrogenated pyrimidines, e.g. trimethoprim not condensed and containing further heterocyclic rings
• A61K 31/13 - Amines, e.g. amantadine
• A61K 31/198 - Alpha-amino acids, e.g. alanine or edetic acid [EDTA]
• A61K 31/365 - Lactones
• A61P 25/16 - Anti-Parkinson drugs
• G01N 33/50 - Chemical analysis of biological material, e.g. blood, urineTesting involving biospecific ligand binding methodsImmunological testing
• G01N 33/68 - Chemical analysis of biological material, e.g. blood, urineTesting involving biospecific ligand binding methodsImmunological testing involving proteins, peptides or amino acids

Goods & Services

Pharmaceuticals; chemical preparations and biological preparations for the treatment of diseases and disorders; veterinary preparations; herbicides, insecticides and pesticides. Conducting research and development services for others in the fields of drug discovery and development for humans; conducting research and development services for others in the fields of drug discovery and development for animals, and drug and chemical discovery and development for veterinary treatments, agrochemicals and agriculture; conducting scientific, pharmaceutical and agrochemical research and data analysis for others to screen for and identify molecular interactions, compounds and toxicity detection; providing scientific, pharmaceutical and agrochemical database research services for others; scientific, pharmaceutical and agrochemical artificial intelligence research services for others to identify molecules and compounds for drug discovery and chemical development.

Goods & Services

Pharmaceuticals; chemical preparations and biological preparations for the treatment of diseases and disorders; veterinary preparations; herbicides, insecticides and pesticides. Conducting research and development services for others in the fields of drug discovery and development for humans; conducting research and development services for others in the fields of drug discovery and development for animals, and drug and chemical discovery and development for veterinary treatments, agrochemicals and agriculture.

Abstract

Disclosed herein are compositions comprising a compound of Formula (I) and methods for treating or prophylaxis of porcine reproductive and respiratory syndrome (PRRS) therewith (I).

IPC Classes  ?

• A61K 31/498 - Pyrazines or piperazines ortho- or peri-condensed with carbocyclic ring systems, e.g. quinoxaline, phenazine
• A61K 31/18 - Sulfonamides
• A61K 31/5377 - 1,4-Oxazines, e.g. morpholine not condensed and containing further heterocyclic rings, e.g. timolol
• C07D 241/44 - Benzopyrazines with hetero atoms or with carbon atoms having three bonds to hetero atoms with at the most one bond to halogen, e.g. ester or nitrile radicals, directly attached to carbon atoms of the hetero ring
• A61P 31/12 - Antivirals

Goods & Services

conducting research and development services for others in the fields of drug discovery and development for humans;

Goods & Services

conducting research and development services for others in the fields of drug discovery and development for humans;

Abstract

Inhibitors of a critical brain enzyme, N-acetyltransferase (ANAT), and methods of discovering, making and using the same for the treatment of disease are disclosed.

IPC Classes  ?

• A61K 31/166 - Amides, e.g. hydroxamic acids having aromatic rings, e.g. colchicine, atenolol, progabide having the carbon atom of a carboxamide group directly attached to the aromatic ring, e.g. procainamide, procarbazine, metoclopramide, labetalol
• A61K 31/24 - Esters, e.g. nitroglycerine, selenocyanates of carboxylic acids having an aromatic ring attached to a carboxyl group having an amino or nitro group
• A61K 31/277 - NitrilesIsonitriles having a ring, e.g. verapamil
• A61K 31/381 - Heterocyclic compounds having sulfur as a ring hetero atom having five-membered rings
• A61K 31/40 - Heterocyclic compounds having nitrogen as a ring hetero atom, e.g. guanethidine or rifamycins having five-membered rings with one nitrogen as the only ring hetero atom, e.g. sulpiride, succinimide, tolmetin, buflomedil
• A61K 31/4152 - 1,2-Diazoles having oxo groups directly attached to the heterocyclic ring, e.g. antipyrine, phenylbutazone, sulfinpyrazone
• A61K 31/4178 - 1,3-Diazoles not condensed and containing further heterocyclic rings, e.g. pilocarpine, nitrofurantoin
• A61K 31/4184 - 1,3-Diazoles condensed with carbocyclic rings, e.g. benzimidazoles
• A61K 31/4196 - 1,2,4-Triazoles
• A61K 31/421 - 1,3-Oxazoles, e.g. pemoline, trimethadione
• A61K 31/422 - Oxazoles not condensed and containing further heterocyclic rings
• A61K 31/4245 - Oxadiazoles
• A61K 31/427 - Thiazoles not condensed and containing further heterocyclic rings
• A61K 31/433 - Thiadiazoles
• A61K 31/4365 - Heterocyclic compounds having nitrogen as a ring hetero atom, e.g. guanethidine or rifamycins having six-membered rings with one nitrogen as the only ring hetero atom ortho- or peri-condensed with heterocyclic ring systems the heterocyclic ring system having sulfur as a ring hetero atom, e.g. ticlopidine
• A61K 31/4418 - Non-condensed pyridinesHydrogenated derivatives thereof having a carbocyclic ring directly attached to the heterocyclic ring, e.g. cyproheptadine
• A61K 31/451 - Non-condensed piperidines, e.g. piperocaine having a carbocyclic ring directly attached to the heterocyclic ring, e.g. glutethimide, meperidine, loperamide, phencyclidine, piminodine
• A61K 31/496 - Non-condensed piperazines containing further heterocyclic rings, e.g. rifampin, thiothixene or sparfloxacin
• A61K 31/498 - Pyrazines or piperazines ortho- or peri-condensed with carbocyclic ring systems, e.g. quinoxaline, phenazine
• A61K 31/4985 - Pyrazines or piperazines ortho- or peri-condensed with heterocyclic ring systems
• A61K 31/505 - PyrimidinesHydrogenated pyrimidines, e.g. trimethoprim
• A61P 25/28 - Drugs for disorders of the nervous system for treating neurodegenerative disorders of the central nervous system, e.g. nootropic agents, cognition enhancers, drugs for treating Alzheimer's disease or other forms of dementia

Abstract

Systems and methods for reducing a number of test objects in a test object dataset are provided. A target model with a first computational complexity is applied to a subset of test objects from the test object dataset and a target object, thereby obtaining a subset of target results. A predictive model with a second computational complexity is trained using the subset of test objects and the subset of target results. The predictive model is applied to the plurality of test objects, thereby obtaining a plurality of predictive results. A portion of the test objects are eliminated from the plurality of test objects based at least in part on the plurality of predictive results. The method determines whether one or more predefined reduction criteria are satisfied. When the predefined reduction criteria are not satisfied, an additional subset of test objects and target results are obtained, and the method is repeated.

IPC Classes  ?

• G16H 50/70 - ICT specially adapted for medical diagnosis, medical simulation or medical data miningICT specially adapted for detecting, monitoring or modelling epidemics or pandemics for mining of medical data, e.g. analysing previous cases of other patients
• G16H 70/40 - ICT specially adapted for the handling or processing of medical references relating to drugs, e.g. their side effects or intended usage
• G06N 20/00 - Machine learning
• G06T 15/10 - Geometric effects
• G16B 5/20 - Probabilistic models
• G16B 40/00 - ICT specially adapted for biostatisticsICT specially adapted for bioinformatics-related machine learning or data mining, e.g. knowledge discovery or pattern finding

Abstract

Systems and methods for reducing a number of test objects in a test object dataset are provided. A target model with a first computational complexity is applied to a subset of test objects from the test object dataset and a target object, thereby obtaining a subset of target results. A predictive model with a second computational complexity is trained using the subset of test objects and the subset of target results. The predictive model is applied to the plurality of test objects, thereby obtaining a plurality of predictive results. A portion of the test objects are eliminated from the plurality of test objects based at least in part on the plurality of predictive results. The method determines whether one or more predefined reduction criteria are satisfied. When the predefined reduction criteria are not satisfied, an additional subset of test objects and target results are obtained, and the method is repeated.

IPC Classes  ?

• G06N 20/10 - Machine learning using kernel methods, e.g. support vector machines [SVM]
• G16B 35/00 - ICT specially adapted for in silico combinatorial libraries of nucleic acids, proteins or peptides
• G16B 40/20 - Supervised data analysis

Abstract

Systems and methods for classifying a test object are provided. For each respective target object in a plurality of target objects, a first procedure is performed comprising (a) posing the test object against the respective target thereby obtaining an interaction between the test and target, and (b) scoring the interaction with a first classifier. Each such score across the plurality of targets forms a test vector that is inputted into a second classifier thereby obtaining an indication of a target object. The second classifier is trained on training vectors, each being the output from instances of the first classifier after inputting a corresponding training object in a plurality of training objects in accordance with the first procedure. Each object in one subset of the training objects is uniquely associated with one of the targets. Another subset of the training objects is not associated with the targets.

IPC Classes  ?

• G06N 3/08 - Learning methods
• G06F 18/00 - Pattern recognition
• G06F 18/214 - Generating training patternsBootstrap methods, e.g. bagging or boosting
• G06F 18/2413 - Classification techniques relating to the classification model, e.g. parametric or non-parametric approaches based on distances to training or reference patterns
• G06N 3/04 - Architecture, e.g. interconnection topology
• G06N 3/045 - Combinations of networks
• G06N 5/04 - Inference or reasoning models
• G06V 10/774 - Generating sets of training patternsBootstrap methods, e.g. bagging or boosting
• G16B 40/00 - ICT specially adapted for biostatisticsICT specially adapted for bioinformatics-related machine learning or data mining, e.g. knowledge discovery or pattern finding
• G16C 20/30 - Prediction of properties of chemical compounds, compositions or mixtures
• G16C 20/70 - Machine learning, data mining or chemometrics
• G06N 3/126 - Evolutionary algorithms, e.g. genetic algorithms or genetic programming
• G06N 5/01 - Dynamic search techniquesHeuristicsDynamic treesBranch-and-bound
• G06N 7/01 - Probabilistic graphical models, e.g. probabilistic networks
• G06N 20/10 - Machine learning using kernel methods, e.g. support vector machines [SVM]
• G16B 35/20 - Screening of libraries
• G16C 20/64 - Screening of libraries

Abstract

Systems and methods for test object classification are provided in which the test object is docked with a target object in a plurality of different poses to form voxel maps. The maps are vectorized and fed into a convolutional neural network comprising an input layer, a plurality of individually weighted convolutional layers, and an output scorer. The convolutional layers include initial and final layers. Responsive to vectorized input, the input layer feeds values into the initial convolutional layer. Each respective convolutional layer, other than the final convolutional layer, feeds intermediate values as a function of the weights and input values of the respective layer into another of the convolutional layers. The final convolutional layer feeds values into one or more fully connected layers as a function of the final layer weights and input values. The one or more full connected layers feed values into the scorer which scores each input vector to thereby classify the test object.

IPC Classes  ?

• G06K 9/66 - Methods or arrangements for recognition using electronic means using simultaneous comparisons or correlations of the image signals with a plurality of references, e.g. resistor matrix references adjustable by an adaptive method, e.g. learning
• G06N 3/08 - Learning methods
• G06T 1/20 - Processor architecturesProcessor configuration, e.g. pipelining
• G06T 1/60 - Memory management
• G06K 9/52 - Extraction of features or characteristics of the image by deriving mathematical or geometrical properties from the whole image
• G06T 7/60 - Analysis of geometric attributes
• G06K 9/62 - Methods or arrangements for recognition using electronic means
• G16B 15/00 - ICT specially adapted for analysing two-dimensional or three-dimensional molecular structures, e.g. structural or functional relations or structure alignment
• G16B 20/00 - ICT specially adapted for functional genomics or proteomics, e.g. genotype-phenotype associations
• G16B 40/00 - ICT specially adapted for biostatisticsICT specially adapted for bioinformatics-related machine learning or data mining, e.g. knowledge discovery or pattern finding
• G06K 9/46 - Extraction of features or characteristics of the image
• G06T 15/08 - Volume rendering
• G06N 20/00 - Machine learning

Abstract

Systems and methods for test object classification are provided in which the test object is docked with a target object in a plurality of different poses to form voxel maps. The maps are vectorized and fed into a convolutional neural network comprising an input layer, a plurality of individually weighted convolutional layers, and an output scorer. The convolutional layers include initial and final layers. Responsive to vectorized input, the input layer feeds values into the initial convolutional layer. Each respective convolutional layer, other than the final convolutional layer, feeds intermediate values as a function of the weights and input values of the respective layer into another of the convolutional layers. The final convolutional layer feeds values into one or more fully connected layers as a function of the final layer weights and input values. The one or more full connected layers feed values into the scorer which scores each input vector to thereby classify the test object.

IPC Classes  ?

• G06K 9/66 - Methods or arrangements for recognition using electronic means using simultaneous comparisons or correlations of the image signals with a plurality of references, e.g. resistor matrix references adjustable by an adaptive method, e.g. learning
• G06N 3/08 - Learning methods
• G06T 1/20 - Processor architecturesProcessor configuration, e.g. pipelining
• G06T 1/60 - Memory management
• G06K 9/52 - Extraction of features or characteristics of the image by deriving mathematical or geometrical properties from the whole image
• G06T 7/60 - Analysis of geometric attributes
• G06K 9/62 - Methods or arrangements for recognition using electronic means
• G16B 15/00 - ICT specially adapted for analysing two-dimensional or three-dimensional molecular structures, e.g. structural or functional relations or structure alignment
• G16B 20/00 - ICT specially adapted for functional genomics or proteomics, e.g. genotype-phenotype associations
• G16B 40/00 - ICT specially adapted for biostatisticsICT specially adapted for bioinformatics-related machine learning or data mining, e.g. knowledge discovery or pattern finding
• G06T 15/08 - Volume rendering
• G06K 9/46 - Extraction of features or characteristics of the image
• G06N 20/00 - Machine learning

Abstract

Systems and methods for classifying a test object are provided. For each respective target object in a plurality of target objects, a first procedure is performed comprising (a) posing the test object against the respective target thereby obtaining an interaction between the test and target, and (b) scoring the interaction with a first classifier. Each such score across the plurality of targets forms a test vector that is inputted into a second classifier thereby obtaining an indication of a target object. The second classifier is trained on training vectors, each being the output from instances of the first classifier after inputting a corresponding training object in a plurality of training objects in accordance with the first procedure. Each object in one subset of the training objects is uniquely associated with one of the targets. Another subset of the training objects is not associated with the targets.

IPC Classes  ?

• G06N 5/04 - Inference or reasoning models
• G06N 3/08 - Learning methods
• G06N 3/04 - Architecture, e.g. interconnection topology
• G16C 20/30 - Prediction of properties of chemical compounds, compositions or mixtures
• G16C 20/70 - Machine learning, data mining or chemometrics

Abstract

Systems and methods for classifying a test object are provided. For each respective target object in a plurality of target objects, a first procedure is performed comprising (a) posing the test object against the respective target thereby obtaining an interaction between the test and target, and (b) scoring the interaction with a first classifier. Each such score across the plurality of targets forms a test vector that is inputted into a second classifier thereby obtaining an indication of a target object. The second classifier is trained on training vectors, each being the output from instances of the first classifier after inputting a corresponding training object in a plurality of training objects in accordance with the first procedure. Each object in one subset of the training objects is uniquely associated with one of the targets. Another subset of the training objects is not associated with the targets.

IPC Classes  ?

• G06F 19/16 - for molecular structure, e.g. structure alignment, structural or functional relations, protein folding, domain topologies, drug targeting using structure data, involving two-dimensional or three-dimensional structures

Goods & Services

Conducting scientific, pharmaceutical and agrochemical research and data analysis for others to screen for and identify molecular interactions, compounds and toxicity detection; providing scientific, pharmaceutical and agrochemical database research services for others; scientific, pharmaceutical and agrochemical artificial intelligence research services for others to identify molecules and compounds for drug discovery and chemical development.

Goods & Services

Conducting scientific, pharmaceutical and agrochemical research and data analysis for others to screen for and identify molecular interactions, compounds and toxicity detection; providing scientific, pharmaceutical and agrochemical database research services for others; scientific, pharmaceutical and agrochemical artificial intelligence research services for others to identify molecules and compounds for drug development.

Goods & Services

Conducting scientific, pharmaceutical [ and agrochemical ] research and data analysis for others to screen for and identify molecular interactions, compounds and toxicity detection; providing scientific, pharmaceutical [ and agrochemical ] database research services for others; scientific, pharmaceutical [ and agrochemical ] artificial intelligence research services for others to identify molecules and compounds for drug discovery and chemical development

Goods & Services

Conducting scientific, pharmaceutical [ and agrochemical ] research and data analysis for others to screen for and identify molecular interactions, compounds and toxicity detection; providing scientific, pharmaceutical [ and agrochemical ] database research services for others; scientific, pharmaceutical [ and agrochemical ] artificial intelligence research services for others to identify molecules and compounds for drug discovery and chemical development

Goods & Services

Conducting scientific, pharmaceutical [ and agrochemical ] research and data analysis for others to screen for and identify molecular interactions, compounds and toxicity detection; providing scientific, pharmaceutical [ and agrochemical ] database research services for others; scientific, pharmaceutical [ and agrochemical ] artificial intelligence research services for others to identify molecules and compounds for drug development

Abstract

Systems and methods for test object classification are provided in which the test object is docked with a target object in a plurality of different poses to form voxel maps. The maps are vectorized and fed into a convolutional neural network comprising an input layer, a plurality of individually weighted convolutional layers, and an output scorer. The convolutional layers include initial and final layers. Responsive to vectorized input, the input layer feeds values into the initial convolutional layer. Each respective convolutional layer, other than the final convolutional layer, feeds intermediate values as a function of the weights and input values of the respective layer into another of the convolutional layers. The final convolutional layer feeds values into one or more fully connected layers as a function of the final layer weights and input values. The one or more full connected layers feed values into the scorer which scores each input vector to thereby classify the test object.

IPC Classes  ?

• G06K 9/46 - Extraction of features or characteristics of the image
• G06K 9/66 - Methods or arrangements for recognition using electronic means using simultaneous comparisons or correlations of the image signals with a plurality of references, e.g. resistor matrix references adjustable by an adaptive method, e.g. learning
• G06N 5/04 - Inference or reasoning models
• G06N 3/08 - Learning methods

Abstract

Systems and methods for test object classification are provided in which the test object is docked with a target object in a plurality of different poses to form voxel maps. The maps are vectorized and fed into a convolutional neural network comprising an input layer, a plurality of individually weighted convolutional layers, and an output scorer. The convolutional layers include initial and final layers. Responsive to vectorized input, the input layer feeds values into the initial convolutional layer. Each respective convolutional layer, other than the final convolutional layer, feeds intermediate values as a function of the weights and input values of the respective layer into another of the convolutional layers. The final convolutional layer feeds values into one or more fully connected layers as a function of the final layer weights and input values. The one or more full connected layers feed values into the scorer which scores each input vector to thereby classify the test object.

IPC Classes  ?

• G06K 9/62 - Methods or arrangements for recognition using electronic means
• G06K 9/66 - Methods or arrangements for recognition using electronic means using simultaneous comparisons or correlations of the image signals with a plurality of references, e.g. resistor matrix references adjustable by an adaptive method, e.g. learning
• G06N 3/08 - Learning methods
• G06T 1/20 - Processor architecturesProcessor configuration, e.g. pipelining
• G06T 1/60 - Memory management
• G06K 9/52 - Extraction of features or characteristics of the image by deriving mathematical or geometrical properties from the whole image
• G06T 7/60 - Analysis of geometric attributes
• G06F 19/16 - for molecular structure, e.g. structure alignment, structural or functional relations, protein folding, domain topologies, drug targeting using structure data, involving two-dimensional or three-dimensional structures
• G06F 19/24 - for machine learning, data mining or biostatistics, e.g. pattern finding, knowledge discovery, rule extraction, correlation, clustering or classification
• G06F 19/18 - for functional genomics or proteomics, e.g. genotype-phenotype associations, linkage disequilibrium, population genetics, binding site identification, mutagenesis, genotyping or genome annotation, protein-protein interactions or protein-nucleic acid interactions
• G06T 15/08 - Volume rendering
• G06K 9/46 - Extraction of features or characteristics of the image