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公开(公告)号：US20220391709A1
公开(公告)日：2022-12-08
申请号：US17842247
申请日：2022-06-16
Applicant: Insilico Medicine IP Limited
Inventor： Aleksandr Aliper , Aleksandrs Zavoronkovs , Alexander Zhebrak , Artur Kadurin , Daniil Polykovskiy , Rim Shayakhmetov
IPC: G06N3/08
Abstract: A method for generating an object includes: providing a dataset having object data and condition data; processing the object data to obtain latent object data and latent object-condition data; processing the condition data to obtain latent condition data and latent condition-object data; processing the latent object data and the latent object-condition data to obtain generated object data; processing the latent condition data and latent condition-object data to obtain generated condition data; comparing the latent object-condition data to the latent condition-object data to determine a difference; processing the latent object data and latent condition data and one of the latent object-condition data or latent condition-object data to obtain a discriminator value; and selecting a selected object based on the generated object data.
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公开(公告)号：US20210383898A1
公开(公告)日：2021-12-09
申请号：US17331493
申请日：2021-05-26
Applicant: Insilico Medicine IP Limited
Inventor： Maksim Kuznetsov , Daniil Polykovskiy , Aleksandrs Zavoronkovs
Abstract: A computing method for normalizing molecule graph data for hierarchical molecular generation can include: providing molecule graph data of a molecule having a node; recursively splitting the node into two nodes; iteratively recursively spilling other nodes into two nodes; generating generated molecular graph data of a generated molecule from node splitting; and providing a report with the generated molecular graph. A computing method can include: providing molecule graph data into a latent code generator having multiple levels with a forward and inverse; and generating latent codes by processing molecule graph data through multiple levels of operations, wherein each level of operations has a sequence of sublevels of operations in forward path and inverse path, wherein the sublevels of operations include node merging operation and node splitting operation; generating at least one molecular structure from latent codes; and outputting generate molecule graph data having the at least one molecular structure.
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3.发明公开公开(公告)号：US20240152763A1
公开(公告)日：2024-05-09
申请号：US18538870
申请日：2023-12-13
Applicant: INSILICO MEDICINE IP LIMITED
Inventor： Aleksandr Aliper , Aleksandrs Zavoronkovs , Alexander Zhebrak , Daniil Polykovskiy , Maksim Kuznetsov , Yan Ivanenkov , Mark Veselov , Vladimir Aladinskiy , Evgeny Putin , Yuriy Volkov , Arip Asadulaev
IPC: G06N3/092 , G06N3/0455 , G06N3/0475 , G06N3/084
CPC classification number: G06N3/092 , G06N3/0455 , G06N3/0475 , G06N3/084
Abstract: The proposed model is a Variational Autoencoder having a learnable prior that is parametrized with a Tensor Train (VAE-TTLP). The VAE-TTLP can be used to generate new objects, such as molecules, that have specific properties and that can have specific biological activity (when a molecule). The VAE-TTLP can be trained in a way with the Tensor Train so that the provided data may omit one or more properties of the object, and still result in an object with a desired property.
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4.发明申请公开(公告)号：US20210057050A1
公开(公告)日：2021-02-25
申请号：US17000109
申请日：2020-08-21
Applicant: INSILICO MEDICINE IP LIMITED
Inventor： Aleksandrs Zavoronkovs , Yan Ivanenkov , Daniil Polykovskiy , Aleksandr Aliper
IPC: G16C20/50 , G16C20/70 , C07D487/04 , C07D231/56 , C07D403/10 , C07D471/10 , C12Q1/48 , G06N20/00 , G16H10/20
Abstract: A computer-implemented method can include: receiving input of a biological target; receiving a generative model (e.g., tensorial reinforcement learning (GENTRL) model or other model) trained with reference compounds, wherein the reference compounds include: general compounds, compounds that modulate the biological target, and compounds that modulate biomolecules other than the biological target; generating structures of generated compounds with the generative model; prioritizing structures of generated compounds based on at least one criteria; processing prioritized chemical structures of the generated compounds through a Sammon mapping protocol to obtain hit structures; and providing chemical structures of the hit structures. One or more non-transitory computer readable media are provided that store instructions that in response to being executed by one or more processors, cause a computer system to perform operations, the operations comprising performing the computer methods described herein for providing chemical structure of hit structures generated by the generative model.
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公开(公告)号：US12040057B2
公开(公告)日：2024-07-16
申请号：US16831747
申请日：2020-03-26
Applicant: INSILICO MEDICINE IP LIMITED
Inventor： Aleksandrs Zavoronkovs , Daniil Polykovskiy , Maksim Kuznetsov , Andrey Filimonov
Abstract: A scaffold-oriented line notation can include: a scaffold sequence of atom identifiers of a scaffold, the scaffold sequence includes at least one decoration marker or any number of decoration markers, each decoration marker being adjacent to an atom identifier of a linking atom of the scaffold; a decoration separator following a last atom identifier or a last decoration marker of the scaffold sequence; at least one decoration having at least one atom identifier in a line notation that defines a chemical structure of the chemical moiety of the decoration that is attached to the linking atom of the scaffold of the molecule; in the scaffold sequence, an order of the at least one decoration marker defines an order of the at least one decoration; in the at least one decoration, the first decoration follows the first decoration separator.
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Patent Agency Ranking
公开(公告)号：US20230331723A1
公开(公告)日：2023-10-19
申请号：US18336818
申请日：2023-06-16
Applicant: Insilico Medicine IP Limited
Inventor： Daniil Polykovskiy , Artur Kadurin , Aleksandr M. Aliper , Alexander Zhebrak , Aleksandrs Zavoronkovs
IPC: C07D471/04 , G16C20/40 , G16C20/70 , G06N3/04 , G06N3/08 , G16C20/30 , G06F18/21 , G06V10/764 , G06V10/82
CPC classification number: C07D471/04 , G16C20/40 , G16C20/70 , G06N3/04 , G06N3/08 , G16C20/30 , G06F18/2178 , G06V10/764 , G06V10/82
Abstract: A method is provided for generating new objects having given properties, such as a specific bioactivity (e.g., binding with a specific protein). In some aspects, the method can include: (a) receiving objects (e.g., physical structures) and their properties (e.g., chemical properties, bioactivity properties, etc.) from a dataset; (b) providing the objects and their properties to a machine learning platform, wherein the machine learning platform outputs a trained model; and (c) the machine learning platform takes the trained model and a set of properties and outputs new objects with desired properties. The new objects are different from the received objects. In some aspects, the objects are molecular structures, such as potential active agents, such as small molecule drugs, biological agents, nucleic acids, proteins, antibodies, or other active agents with a desired or defined bioactivity (e.g., binding a specific protein, preferentially over other proteins).
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7.发明授权公开(公告)号：US12293809B2
公开(公告)日：2025-05-06
申请号：US17000109
申请日：2020-08-21
Applicant: INSILICO MEDICINE IP LIMITED
Inventor： Aleksandrs Zavoronkovs , Yan Ivanenkov , Daniil Polykovskiy , Aleksandr Aliper
IPC: G16C20/50 , C07D231/56 , C07D245/04 , C07D401/06 , C07D401/12 , C07D403/06 , C07D403/10 , C07D403/12 , C07D409/12 , C07D471/04 , C07D471/10 , C07D487/04 , C07D487/10 , C07D491/20 , C07D513/10 , G06N20/00 , G16C20/40 , G16C20/70 , G16H10/20
Abstract: A computer-implemented method can include: receiving input of a biological target; receiving a generative model (e.g., tensorial reinforcement learning (GENTRL) model or other model) trained with reference compounds, wherein the reference compounds include: general compounds, compounds that modulate the biological target, and compounds that modulate biomolecules other than the biological target; generating structures of generated compounds with the generative model; prioritizing structures of generated compounds based on at least one criteria; processing prioritized chemical structures of the generated compounds through a Sammon mapping protocol to obtain hit structures; and providing chemical structures of the hit structures. One or more non-transitory computer readable media are provided that store instructions that in response to being executed by one or more processors, cause a computer system to perform operations, the operations comprising performing the computer methods described herein for providing chemical structure of hit structures generated by the generative model.
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公开(公告)号：US12282851B2
公开(公告)日：2025-04-22
申请号：US17842247
申请日：2022-06-16
Applicant: Insilico Medicine IP Limited
Inventor： Aleksandr Aliper , Aleksandrs Zavoronkovs , Alexander Zhebrak , Artur Kadurin , Daniil Polykovskiy , Rim Shayakhmetov
Abstract: A method for generating an object includes: providing a dataset having object data and condition data; processing the object data to obtain latent object data and latent object-condition data; processing the condition data to obtain latent condition data and latent condition-object data; processing the latent object data and the latent object-condition data to obtain generated object data; processing the latent condition data and latent condition-object data to obtain generated condition data; comparing the latent object-condition data to the latent condition-object data to determine a difference; processing the latent object data and latent condition data and one of the latent object-condition data or latent condition-object data to obtain a discriminator value; and selecting a selected object based on the generated object data.
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9.发明授权公开(公告)号：US11893498B2
公开(公告)日：2024-02-06
申请号：US18114566
申请日：2023-02-27
Applicant: INSILICO MEDICINE IP LIMITED
Inventor： Aleksandr Aliper , Aleksandrs Zavoronkovs , Alexander Zhebrak , Daniil Polykovskiy , Maksim Kuznetsov , Yan Ivanenkov , Mark Veselov , Vladimir Aladinskiy , Evgeny Putin , Yuriy Volkov , Arip Asadulaev
IPC: G06N3/092 , G06N3/0455 , G06N3/084 , G06N3/0475
CPC classification number: G06N3/092 , G06N3/0455 , G06N3/0475 , G06N3/084
Abstract: The proposed model is a Variational Autoencoder having a learnable prior that is parametrized with a Tensor Train (VAE-TTLP). The VAE-TTLP can be used to generate new objects, such as molecules, that have specific properties and that can have specific biological activity (when a molecule). The VAE-TTLP can be trained in a way with the Tensor Train so that the provided data may omit one or more properties of the object, and still result in an object with a desired property.
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10.发明公开公开(公告)号：US20230214662A1
公开(公告)日：2023-07-06
申请号：US18114566
申请日：2023-02-27
Applicant: INSILICO MEDICINE IP LIMITED
Inventor： Aleksandr Aliper , Aleksandrs Zavoronkovs , Alexander Zhebrak , Daniil Polykovskiy , Maksim Kuznetsov , Yan Ivanenkov , Mark Veselov , Vladimir Aladinskiy , Evgeny Putin , Yuriy Volkov , Arip Asadulaev
IPC: G06N3/092 , G06N3/0475 , G06N3/084 , G06N3/0455
CPC classification number: G06N3/092 , G06N3/0475 , G06N3/084 , G06N3/0455
Abstract: The proposed model is a Variational Autoencoder having a learnable prior that is parametrized with a Tensor Train (VAE-TTLP). The VAE-TTLP can be used to generate new objects, such as molecules, that have specific properties and that can have specific biological activity (when a molecule). The VAE-TTLP can be trained in a way with the Tensor Train so that the provided data may omit one or more properties of the object, and still result in an object with a desired property.
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