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1.发明公开公开(公告)号：US20240120022A1
公开(公告)日：2024-04-11
申请号：US18275933
申请日：2022-01-27
Applicant: DeepMind Technologies Limited
Inventor： Andrew W. Senior , Simon Kohl , Jason Yim , Russell James Bates , Catalin-Dumitru Ionescu , Charlie Thomas Curtis Nash , Ali Razavi-Nematollahi , Alexander Pritzel , John Jumper
Abstract: Methods, systems, and apparatus, including computer programs encoded on a computer storage medium, for performing protein design. In one aspect, a method comprises: processing an input characterizing a target protein structure of a target protein using an embedding neural network having a plurality of embedding neural network parameters to generate an embedding of the target protein structure of the target protein; determining a predicted amino acid sequence of the target protein based on the embedding of the target protein structure, comprising: conditioning a generative neural network having a plurality of generative neural network parameters on the embedding of the target protein structure; and generating, by the generative neural network conditioned on the embedding of the target protein structure, a representation of the predicted amino acid sequence of the target protein.
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Patent Agency Ranking
公开(公告)号：US20250103856A1
公开(公告)日：2025-03-27
申请号：US18832817
申请日：2023-01-30
Applicant: DeepMind Technologies Limited
Inventor： Joao Carreira , Andrew Coulter Jaegle , Skanda Kumar Koppula , Daniel Zoran , Adrià Recasens Continente , Catalin-Dumitru Ionescu , Olivier Jean Hénaff , Evan Gerard Shelhamer , Relja Arandjelovic , Matthew Botvinick , Oriol Vinyals , Karen Simonyan , Andrew Zisserman
IPC: G06N3/045
Abstract: Methods, systems, and apparatus, including computer programs encoded on a computer storage medium, for using a neural network to generate a network output that characterizes an entity. In one aspect, a method includes: obtaining a representation of the entity as a set of data element embeddings, obtaining a set of latent embeddings, and processing: (i) the set of data element embeddings, and (ii) the set of latent embeddings, using the neural network to generate the network output. The neural network includes a sequence of neural network blocks including: (i) one or more local cross-attention blocks, and (ii) an output block. Each local cross-attention block partitions the set of latent embeddings and the set of data element embeddings into proper subsets, and updates each proper subset of the set of latent embeddings using attention over only the corresponding proper subset of the set of data element embeddings.
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公开(公告)号：US20200104645A1
公开(公告)日：2020-04-02
申请号：US16586262
申请日：2019-09-27
Applicant: DeepMind Technologies Limited
Inventor： Catalin-Dumitru Ionescu , Tejas Dattatraya Kulkarni
Abstract: A reinforcement learning neural network system in which internal representations and policies are grounded in visual entities derived from image pixels comprises a visual entity identifying neural network subsystem configured to process image data to determine a set of spatial maps representing respective discrete visual entities. A reinforcement learning neural network subsystem processes data from the set of spatial maps and environmental reward data to provide action data for selecting actions to perform a task.
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4.发明公开公开(公告)号：US20240232580A1
公开(公告)日：2024-07-11
申请号：US18284595
申请日：2022-05-27
Applicant: DEEPMIND TECHNOLOGIES LIMITED
Inventor： Andrew Coulter Jaegle , Jean-Baptiste Alayrac , Sebastian Borgeaud Dit Avocat , Catalin-Dumitru Ionescu , Carl Doersch , Fengning Ding , Oriol Vinyals , Olivier Jean Hénaff , Skanda Kumar Koppula , Daniel Zoran , Andrew Brock , Evan Gerard Shelhamer , Andrew Zisserman , Joao Carreira
IPC: G06N3/0455
CPC classification number: G06N3/0455
Abstract: Methods, systems, and apparatus, including computer programs encoded on a computer storage medium, for generating a network output using a neural network. In one aspect, a method comprises: obtaining: (i) a network input to a neural network, and (ii) a set of query embeddings; processing the network input using the neural network to generate a network output that comprises a respective dimension corresponding to each query embedding in the set of query embeddings, comprising: processing the network input using an encoder block of the neural network to generate a representation of the network input as a set of latent embeddings; and processing: (i) the set of latent embeddings, and (ii) the set of query embeddings, using a cross-attention block that generates each dimension of the network output by cross-attention of a corresponding query embedding over the set of latent embeddings.
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公开(公告)号：US20240087686A1
公开(公告)日：2024-03-14
申请号：US18273594
申请日：2022-01-27
Applicant: DeepMind Technologies Limited
Inventor： Alexander Pritzel , Catalin-Dumitru Ionescu , Simon Kohl
Abstract: Methods, systems, and apparatus, including computer programs encoded on a computer storage medium, for unmasking a masked representation of a protein using a protein reconstruction neural network. In one aspect, a method comprises: receiving the masked representation of the protein; and processing the masked representation of the protein using the protein reconstruction neural network to generate a respective predicted embedding corresponding to one or more masked embeddings that are included in the masked representation of the protein, wherein a predicted embedding corresponding to a masked embedding in a representation of the amino acid sequence of the protein defines a prediction for an identity of an amino acid at a corresponding position in the amino acid sequence, wherein a predicted embedding corresponding to a masked embedding in a representation of the structure of the protein defines a prediction for a corresponding structural feature of the protein.
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公开(公告)号：US10748039B2
公开(公告)日：2020-08-18
申请号：US16586262
申请日：2019-09-27
Applicant: DeepMind Technologies Limited
Inventor： Catalin-Dumitru Ionescu , Tejas Dattatraya Kulkarni
Abstract: A reinforcement learning neural network system in which internal representations and policies are grounded in visual entities derived from image pixels comprises a visual entity identifying neural network subsystem configured to process image data to determine a set of spatial maps representing respective discrete visual entities. A reinforcement learning neural network subsystem processes data from the set of spatial maps and environmental reward data to provide action data for selecting actions to perform a task.
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