05 - Produits pharmaceutiques, vétérinaires et hygièniques
09 - Appareils et instruments scientifiques et électriques
42 - Services scientifiques, technologiques et industriels, recherche et conception
Produits et 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.
2.
Inhibitor of PFKFB2 Kinase Activity and Cellular Glycolysis
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:
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.
A61K 31/506 - PyrimidinesPyrimidines hydrogénées, p. ex. triméthoprime non condensées et contenant d'autres hétérocycles
A61K 31/5377 - 1,4-Oxazines, p. ex. morpholine non condensées et contenant d'autres hétérocycles, p. ex. timolol
A61P 1/04 - Médicaments pour le traitement des troubles du tractus alimentaire ou de l'appareil digestif des ulcères, des gastrites ou des œsophagites par reflux, p. ex. antiacides, antisécrétoires, protecteurs de la muqueuse
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.
A61K 31/506 - PyrimidinesPyrimidines hydrogénées, p. ex. triméthoprime non condensées et contenant d'autres hétérocycles
A61P 29/00 - Agents analgésiques, antipyrétiques ou anti-inflammatoires non centraux, p. ex. agents antirhumatismauxMédicaments anti-inflammatoires non stéroïdiens [AINS]
5.
CHARACTERIZATION OF INTERACTIONS BETWEEN COMPOUNDS AND POLYMERS USING POSE ENSEMBLES
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.
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.
09 - Appareils et instruments scientifiques et électriques
Produits et 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
42 - Services scientifiques, technologiques et industriels, recherche et conception
Produits et 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
05 - Produits pharmaceutiques, vétérinaires et hygièniques
Produits et 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
10.
SYSTEMS AND METHODS FOR IDENTIFYING COMPOUNDS IN COMBINATORIAL LIBRARIES HAVING SPECIFIC MOLECULAR PROPERTIES
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.
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.
C07D 401/14 - Composés hétérocycliques contenant plusieurs hétérocycles comportant des atomes d'azote comme uniques hétéro-atomes du cycle, au moins un cycle étant un cycle à six chaînons avec un unique atome d'azote contenant au moins trois hétérocycles
A61K 31/506 - PyrimidinesPyrimidines hydrogénées, p. ex. triméthoprime non condensées et contenant d'autres hétérocycles
A61K 31/5377 - 1,4-Oxazines, p. ex. morpholine non condensées et contenant d'autres hétérocycles, p. ex. timolol
A61K 31/675 - Composés du phosphore ayant l'azote comme hétéro-atome d'un cycle, p. ex. phosphate de pyridoxal
C07D 401/12 - Composés hétérocycliques contenant plusieurs hétérocycles comportant des atomes d'azote comme uniques hétéro-atomes du cycle, au moins un cycle étant un cycle à six chaînons avec un unique atome d'azote contenant deux hétérocycles liés par une chaîne contenant des hétéro-atomes comme chaînons
C07D 403/12 - Composés hétérocycliques contenant plusieurs hétérocycles, comportant des atomes d'azote comme uniques hétéro-atomes du cycle, non prévus par le groupe contenant deux hétérocycles liés par une chaîne contenant des hétéro-atomes comme chaînons
C07D 405/14 - Composés hétérocycliques contenant à la fois un ou plusieurs hétérocycles comportant des atomes d'oxygène comme uniques hétéro-atomes du cycle et un ou plusieurs hétérocycles comportant des atomes d'azote comme uniques hétéro-atomes du cycle contenant au moins trois hétérocycles
C07D 413/14 - Composés hétérocycliques contenant plusieurs hétérocycles, au moins un cycle comportant des atomes d'azote et d'oxygène comme uniques hétéro-atomes du cycle contenant au moins trois hétérocycles
C07D 491/048 - Systèmes condensés en ortho avec un seul atome d'oxygène comme hétéro-atome du cycle contenant de l'oxygène le cycle contenant de l'oxygène étant à cinq chaînons
C07D 491/107 - Systèmes condensés en spiro avec un seul atome d'oxygène comme hétéro-atome du cycle contenant de l'oxygène
C07F 9/6558 - Composés hétérocycliques, p. ex. contenant du phosphore comme hétéro-atome du cycle contenant au moins deux hétérocycles différents ou différemment substitués ni condensés entre eux ni condensés avec un carbocycle commun ou un système carbocyclique commun
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.
C07D 413/14 - Composés hétérocycliques contenant plusieurs hétérocycles, au moins un cycle comportant des atomes d'azote et d'oxygène comme uniques hétéro-atomes du cycle contenant au moins trois hétérocycles
A61K 31/506 - PyrimidinesPyrimidines hydrogénées, p. ex. triméthoprime non condensées et contenant d'autres hétérocycles
A61K 31/5377 - 1,4-Oxazines, p. ex. morpholine non condensées et contenant d'autres hétérocycles, p. ex. timolol
A61K 31/675 - Composés du phosphore ayant l'azote comme hétéro-atome d'un cycle, p. ex. phosphate de pyridoxal
A61P 1/00 - Médicaments pour le traitement des troubles du tractus alimentaire ou de l'appareil digestif
C07D 401/12 - Composés hétérocycliques contenant plusieurs hétérocycles comportant des atomes d'azote comme uniques hétéro-atomes du cycle, au moins un cycle étant un cycle à six chaînons avec un unique atome d'azote contenant deux hétérocycles liés par une chaîne contenant des hétéro-atomes comme chaînons
C07D 401/14 - Composés hétérocycliques contenant plusieurs hétérocycles comportant des atomes d'azote comme uniques hétéro-atomes du cycle, au moins un cycle étant un cycle à six chaînons avec un unique atome d'azote contenant au moins trois hétérocycles
C07D 403/12 - Composés hétérocycliques contenant plusieurs hétérocycles, comportant des atomes d'azote comme uniques hétéro-atomes du cycle, non prévus par le groupe contenant deux hétérocycles liés par une chaîne contenant des hétéro-atomes comme chaînons
C07D 405/14 - Composés hétérocycliques contenant à la fois un ou plusieurs hétérocycles comportant des atomes d'oxygène comme uniques hétéro-atomes du cycle et un ou plusieurs hétérocycles comportant des atomes d'azote comme uniques hétéro-atomes du cycle contenant au moins trois hétérocycles
C07D 491/048 - Systèmes condensés en ortho avec un seul atome d'oxygène comme hétéro-atome du cycle contenant de l'oxygène le cycle contenant de l'oxygène étant à cinq chaînons
C07D 491/107 - Systèmes condensés en spiro avec un seul atome d'oxygène comme hétéro-atome du cycle contenant de l'oxygène
C07F 9/6558 - Composés hétérocycliques, p. ex. contenant du phosphore comme hétéro-atome du cycle contenant au moins deux hétérocycles différents ou différemment substitués ni condensés entre eux ni condensés avec un carbocycle commun ou un système carbocyclique commun
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.
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.
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.
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.
THE UNIVERSITY OF NORTH CAROLINA AT CHAPEL HILL (USA)
ATOMWISE INC. (USA)
Inventeur(s)
Liu, Pengda
Chen, Jianfeng
Brown, Nicholas
Laggner, Christian
Abrégé
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.
C07D 231/10 - Composés hétérocycliques contenant des cycles diazole-1, 2 ou diazole-1, 2 hydrogéné non condensés avec d'autres cycles comportant deux ou trois liaisons doubles entre chaînons cycliques ou entre chaînons cycliques et chaînons non cycliques
C07D 231/02 - Composés hétérocycliques contenant des cycles diazole-1, 2 ou diazole-1, 2 hydrogéné non condensés avec d'autres cycles
C07D 403/14 - Composés hétérocycliques contenant plusieurs hétérocycles, comportant des atomes d'azote comme uniques hétéro-atomes du cycle, non prévus par le groupe contenant au moins trois hétérocycles
18.
COMPOUND 7AI IN TREATING EWING SARCOMA BY INHIBITING OTUD7A
The University of North Carolina at Chapel Hill (USA)
Atomwise Inc. (USA)
Inventeur(s)
Liu, Pengda
Su, Siyuan
Davis, Ian Jonathan
Laggner, Christian
Abrégé
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
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.
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.
G06F 30/20 - Optimisation, vérification ou simulation de l’objet conçu
G06F 30/27 - Optimisation, vérification ou simulation de l’objet conçu utilisant l’apprentissage automatique, p. ex. l’intelligence artificielle, les réseaux neuronaux, les machines à support de vecteur [MSV] ou l’apprentissage d’un modèle
G06G 7/75 - Calculateurs analogiques pour des procédés, des systèmes ou des dispositifs spécifiques, p. ex. simulateurs pour l'analyse de composants, p. ex. de mélanges, de couleurs
G16C 20/50 - Conception moléculaire, p. ex. de médicaments
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).
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.
A61K 31/4365 - Composés hétérocycliques ayant l'azote comme hétéro-atome d'un cycle, p. ex. guanéthidine ou rifamycines ayant des cycles à six chaînons avec un azote comme seul hétéro-atome d'un cycle condensés en ortho ou en péri avec des systèmes hétérocycliques le système hétérocyclique ayant le soufre comme hétéro-atome du cycle, p. ex. ticlopidine
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.
THE UNIVERSITY OF NORTH CAROLINA AT CHAPEL HILL (USA)
ATOMWISE INC. (USA)
Inventeur(s)
Liu, Pengda
Su, Siyuan
Davis, Ian Jonathan
Laggner, Christian
Abrégé
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
A61K 31/41 - Composés hétérocycliques ayant l'azote comme hétéro-atome d'un cycle, p. ex. guanéthidine ou rifamycines ayant des cycles à cinq chaînons avec plusieurs hétéro-atomes cycliques, l'un au moins étant l'azote, p. ex. tétrazole
A61K 31/4162 - 1,2-Diazoles condensés avec des systèmes hétérocycliques
C07D 231/10 - Composés hétérocycliques contenant des cycles diazole-1, 2 ou diazole-1, 2 hydrogéné non condensés avec d'autres cycles comportant deux ou trois liaisons doubles entre chaînons cycliques ou entre chaînons cycliques et chaînons non cycliques
C07D 487/02 - Composés hétérocycliques contenant des atomes d'azote comme uniques hétéro-atomes dans le système condensé, non prévus par les groupes dans lesquels le système condensé contient deux hétérocycles
The Board of Trustees of the Leland Junior University (USA)
Atomwise Inc. (USA)
Inventeur(s)
Wang, Xinnan
Hsieh, Chung-Han
Li, Li
Nguyen, Kong
Abrégé
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.
G01N 33/50 - Analyse chimique de matériau biologique, p. ex. de sang ou d'urineTest par des méthodes faisant intervenir la formation de liaisons biospécifiques par ligandsTest immunologique
G01N 33/68 - Analyse chimique de matériau biologique, p. ex. de sang ou d'urineTest par des méthodes faisant intervenir la formation de liaisons biospécifiques par ligandsTest immunologique faisant intervenir des protéines, peptides ou amino-acides
05 - Produits pharmaceutiques, vétérinaires et hygièniques
42 - Services scientifiques, technologiques et industriels, recherche et conception
Produits et 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.
05 - Produits pharmaceutiques, vétérinaires et hygièniques
42 - Services scientifiques, technologiques et industriels, recherche et conception
Produits et 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.
28.
INHIBITORS OF PORCINE REPRODUCTIVE AND RESPIRATORY SYNDROME VIRUS
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).
A61K 31/5377 - 1,4-Oxazines, p. ex. morpholine non condensées et contenant d'autres hétérocycles, p. ex. timolol
C07D 241/44 - Benzopyrazines avec des hétéro-atomes ou avec des atomes de carbone comportant trois liaisons à des hétéro-atomes, avec au plus une liaison à un halogène, p. ex. radicaux ester ou nitrile, liés directement aux atomes de carbone de l'hétérocycle
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.
A61K 31/166 - Amides, p. ex. acides hydroxamiques ayant des cycles aromatiques, p. ex. colchicine, aténolol, progabide ayant l'atome de carbone d'un groupe carboxamide lié directement au cycle aromatique, p. ex. procaïnamide, procarbazine, métoclopramide, labétalol
A61K 31/24 - Esters, p. ex. nitroglycérine, sélénocyanates d'acides carboxyliques ayant un noyau aromatique lié au groupe carboxyle ayant un groupe amino ou nitro
A61K 31/277 - NitrilesIsonitriles ayant un cycle, p. ex. vérapamil
A61K 31/381 - Composés hétérocycliques ayant le soufre comme hétéro-atome d'un cycle ayant des cycles à cinq chaînons
A61K 31/40 - Composés hétérocycliques ayant l'azote comme hétéro-atome d'un cycle, p. ex. guanéthidine ou rifamycines ayant des cycles à cinq chaînons avec un azote comme seul hétéro-atome d'un cycle, p. ex. sulpiride, succinimide, tolmétine, buflomédil
A61K 31/4152 - 1,2-Diazoles ayant des groupes oxo liés directement à l'hétérocycle, p. ex. antipyrine, phénylbutazone, sulfinpyrazone
A61K 31/4178 - 1,3-Diazoles non condensés et contenant d'autres hétérocycles, p. ex. pilocarpine, nitrofurantoïne
A61K 31/4184 - 1,3-Diazoles condensés avec des carbocycles, p. ex. benzimidazoles
A61K 31/4365 - Composés hétérocycliques ayant l'azote comme hétéro-atome d'un cycle, p. ex. guanéthidine ou rifamycines ayant des cycles à six chaînons avec un azote comme seul hétéro-atome d'un cycle condensés en ortho ou en péri avec des systèmes hétérocycliques le système hétérocyclique ayant le soufre comme hétéro-atome du cycle, p. ex. ticlopidine
A61K 31/4418 - Pyridines non condenséesLeurs dérivés hydrogénés ayant un carbocycle lié directement à l'hétérocycle, p. ex. cyproheptadine
A61K 31/451 - Pipéridines non condensées, p. ex. pipérocaïne ayant un carbocycle lié directement à l'hétérocycle, p. ex. glutéthimide, mépéridine, lopéramide, phencyclidine, piminodine
A61K 31/496 - Pipérazines non condensées contenant d'autres hétérocycles, p. ex. rifampine, thiothixène ou sparfloxacine
A61K 31/498 - Pyrazines ou pipérazines condensées en ortho ou en péri avec des systèmes carbocycliques, p. ex. quinoxaline, phénazine
A61K 31/4985 - Pyrazines ou pipérazines condensées en ortho ou en péri avec des systèmes hétérocycliques
A61K 31/505 - PyrimidinesPyrimidines hydrogénées, p. ex. triméthoprime
A61P 25/28 - Médicaments pour le traitement des troubles du système nerveux des troubles dégénératifs du système nerveux central, p. ex. agents nootropes, activateurs de la cognition, médicaments pour traiter la maladie d'Alzheimer ou d'autres formes de démence
32.
SYSTEMS AND METHODS FOR SCREENING COMPOUNDS IN SILICO
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.
G16H 50/70 - TIC spécialement adaptées au diagnostic médical, à la simulation médicale ou à l’extraction de données médicalesTIC spécialement adaptées à la détection, au suivi ou à la modélisation d’épidémies ou de pandémies pour extraire des données médicales, p. ex. pour analyser les cas antérieurs d’autres patients
G16H 70/40 - TIC spécialement adaptées au maniement ou au traitement de références médicales concernant des médicaments, p. ex. leurs effets secondaires ou leur usage prévu
G16B 40/00 - TIC spécialement adaptées aux biostatistiquesTIC spécialement adaptées à l’apprentissage automatique ou à l’exploration de données liées à la bio-informatique, p. ex. extraction de connaissances ou détection de motifs
33.
SYSTEMS AND METHODS FOR SCREENING COMPOUNDS IN SILICO
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.
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.
G06F 18/214 - Génération de motifs d'entraînementProcédés de Bootstrapping, p. ex. ”bagging” ou ”boosting”
G06F 18/2413 - Techniques de classification relatives au modèle de classification, p. ex. approches paramétriques ou non paramétriques basées sur les distances des motifs d'entraînement ou de référence
G06N 3/04 - Architecture, p. ex. topologie d'interconnexion
G06N 5/04 - Modèles d’inférence ou de raisonnement
G06V 10/774 - Génération d'ensembles de motifs de formationTraitement des caractéristiques d’images ou de vidéos dans les espaces de caractéristiquesDispositions pour la reconnaissance ou la compréhension d’images ou de vidéos utilisant la reconnaissance de formes ou l’apprentissage automatique utilisant l’intégration et la réduction de données, p. ex. analyse en composantes principales [PCA] ou analyse en composantes indépendantes [ ICA] ou cartes auto-organisatrices [SOM]Séparation aveugle de source méthodes de Bootstrap, p. ex. "bagging” ou “boosting”
G16B 40/00 - TIC spécialement adaptées aux biostatistiquesTIC spécialement adaptées à l’apprentissage automatique ou à l’exploration de données liées à la bio-informatique, p. ex. extraction de connaissances ou détection de motifs
G16C 20/30 - Prévision des propriétés des composés, des compositions ou des mélanges chimiques
G16C 20/70 - Apprentissage automatique, exploration de données ou chimiométrie
G06N 3/126 - Algorithmes évolutionnaires, p. ex. algorithmes génétiques ou programmation génétique
G06N 5/01 - Techniques de recherche dynamiqueHeuristiquesArbres dynamiquesSéparation et évaluation
G06N 7/01 - Modèles graphiques probabilistes, p. ex. réseaux probabilistes
G06N 20/10 - Apprentissage automatique utilisant des méthodes à noyaux, p. ex. séparateurs à vaste marge [SVM]
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.
G06K 9/66 - Méthodes ou dispositions pour la reconnaissance utilisant des moyens électroniques utilisant des comparaisons ou corrélations simultanées de signaux images avec une pluralité de références, p.ex. matrice de résistances avec des références réglables par une méthode adaptative, p.ex. en s'instruisant
G06K 9/62 - Méthodes ou dispositions pour la reconnaissance utilisant des moyens électroniques
G16B 15/00 - TIC spécialement adaptées à l’analyse de structures moléculaires bidimensionnelles ou tridimensionnelles, p. ex. relations structurelles ou fonctionnelles ou alignement de structures
G16B 20/00 - TIC spécialement adaptées à la génomique ou protéomique fonctionnelle, p. ex. corrélations génotype-phénotype
G16B 40/00 - TIC spécialement adaptées aux biostatistiquesTIC spécialement adaptées à l’apprentissage automatique ou à l’exploration de données liées à la bio-informatique, p. ex. extraction de connaissances ou détection de motifs
G06K 9/46 - Extraction d'éléments ou de caractéristiques de l'image
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.
G06K 9/66 - Méthodes ou dispositions pour la reconnaissance utilisant des moyens électroniques utilisant des comparaisons ou corrélations simultanées de signaux images avec une pluralité de références, p.ex. matrice de résistances avec des références réglables par une méthode adaptative, p.ex. en s'instruisant
G06K 9/62 - Méthodes ou dispositions pour la reconnaissance utilisant des moyens électroniques
G16B 15/00 - TIC spécialement adaptées à l’analyse de structures moléculaires bidimensionnelles ou tridimensionnelles, p. ex. relations structurelles ou fonctionnelles ou alignement de structures
G16B 20/00 - TIC spécialement adaptées à la génomique ou protéomique fonctionnelle, p. ex. corrélations génotype-phénotype
G16B 40/00 - TIC spécialement adaptées aux biostatistiquesTIC spécialement adaptées à l’apprentissage automatique ou à l’exploration de données liées à la bio-informatique, p. ex. extraction de connaissances ou détection de motifs
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.
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.
G06F 19/16 - pour la structure moléculaire, p.ex. alignement de la structure, relations structurales ou fonctionnelles, repliement protéique, topologies de domaine, ciblage de médicaments utilisant des données de structure, impliquant des structures bidimensionnelles ou tridimensionnelles
42 - Services scientifiques, technologiques et industriels, recherche et conception
Produits et 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.
42 - Services scientifiques, technologiques et industriels, recherche et conception
Produits et 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.
42 - Services scientifiques, technologiques et industriels, recherche et conception
Produits et 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
42 - Services scientifiques, technologiques et industriels, recherche et conception
Produits et 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
42 - Services scientifiques, technologiques et industriels, recherche et conception
Produits et 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
44.
SYSTEMS AND METHODS FOR APPLYING A CONVOLUTIONAL NETWORK TO SPATIAL DATA
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.
G06K 9/46 - Extraction d'éléments ou de caractéristiques de l'image
G06K 9/66 - Méthodes ou dispositions pour la reconnaissance utilisant des moyens électroniques utilisant des comparaisons ou corrélations simultanées de signaux images avec une pluralité de références, p.ex. matrice de résistances avec des références réglables par une méthode adaptative, p.ex. en s'instruisant
G06N 5/04 - Modèles d’inférence ou de raisonnement
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.
G06K 9/62 - Méthodes ou dispositions pour la reconnaissance utilisant des moyens électroniques
G06K 9/66 - Méthodes ou dispositions pour la reconnaissance utilisant des moyens électroniques utilisant des comparaisons ou corrélations simultanées de signaux images avec une pluralité de références, p.ex. matrice de résistances avec des références réglables par une méthode adaptative, p.ex. en s'instruisant
G06F 19/16 - pour la structure moléculaire, p.ex. alignement de la structure, relations structurales ou fonctionnelles, repliement protéique, topologies de domaine, ciblage de médicaments utilisant des données de structure, impliquant des structures bidimensionnelles ou tridimensionnelles
G06F 19/24 - pour l'apprentissage automatique, l'exploration de données ou les bio statistiques, p.ex. détection de motifs, extraction de connaissances, extraction de règles, corrélation, agrégation ou classification
G06F 19/18 - pour la génomique ou la protéomique fonctionnelle, p.ex. associations génotype-phénotype, déséquilibre de liaison, mutagénèse, génotypage ou annotation génomique, interactions protéines-protéines ou interactions protéines-acides nucléiques
