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公开(公告)号：US20240071571A1
公开(公告)日：2024-02-29
申请号：US18471641
申请日：2023-09-21
Applicant: NEC Laboratories America, Inc.
Inventor： Renqiang Min , Hans Peter Graf , Ziqi Chen
Abstract: A system for binding peptide search for immunotherapy is presented. The system includes employing a deep neural network to predict a peptide presentation given Major Histocompatibility Complex allele sequences and peptide sequences, training a Variational Autoencoder (VAE) to reconstruct peptides by converting the peptide sequences into continuous embedding vectors, running a Monte Carlo Tree Search to generate a first set of positive peptide vaccine candidates, running a Bayesian Optimization search with the trained VAE and a Backpropagation search with the trained VAE to generate a second set of positive peptide vaccine candidates, using a sampling from a Position Weight Matrix (sPWM) to generate a third set of positive peptide vaccine candidates, screening and merging the first, second, and third sets of positive peptide vaccine candidates, and outputting qualified peptides for immunotherapy from the screened and merged sets of positive peptide vaccine candidates.
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2.发明公开公开(公告)号：US20230253068A1
公开(公告)日：2023-08-10
申请号：US18151686
申请日：2023-01-09
Applicant: NEC Laboratories America, Inc.
Inventor： Renqiang Min , Hans Peter Graf , Ziqi Chen
Abstract: A method for implementing deep reinforcement learning with T-cell receptor (TCR) mutation policies to generate binding TCRs recognizing target peptides for immunotherapy is presented. The method includes extracting peptides to identify a virus or tumor cells, collecting a library of TCRs from target patients, predicting, by a deep neural network, interaction scores between the extracted peptides and the TCRs from the target patients, developing a deep reinforcement learning (DRL) framework with TCR mutation policies to generate TCRs with maximum binding scores, defining reward functions based on a reconstruction-based score and a density estimation-based score, randomly sampling batches of TCRs and following a policy network to mutate the TCRs, outputting mutated TCRs, and ranking the outputted TCRs to utilize top-ranked TCR candidates to target the virus or the tumor cells for immunotherapy.
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3.发明公开公开(公告)号：US20240185948A1
公开(公告)日：2024-06-06
申请号：US18414645
申请日：2024-01-17
Applicant: NEC Laboratories America, Inc.
Inventor： Renqiang Min , Hans Peter Graf , Ziqi Chen
Abstract: A method for implementing deep reinforcement learning with T-cell receptor (TCR) mutation policies to generate binding TCRs recognizing target peptides for immunotherapy is presented. The method includes extracting peptides to identify a virus or tumor cells, collecting a library of TCRs from target patients, predicting, by a deep neural network, interaction scores between the extracted peptides and the TCRs from the target patients, developing a deep reinforcement learning (DRL) framework with TCR mutation policies to generate TCRs with maximum binding scores, defining reward functions based on a reconstruction-based score and a density estimation-based score, randomly sampling batches of TCRs and following a policy network to mutate the TCRs, outputting mutated TCRs, and ranking the outputted TCRs to utilize top-ranked TCR candidates to target the virus or the tumor cells for immunotherapy.
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4.发明公开公开(公告)号：US20240177798A1
公开(公告)日：2024-05-30
申请号：US18414670
申请日：2024-01-17
Applicant: NEC Laboratories America, Inc.
Inventor： Renqiang Min , Hans Peter Graf , Ziqi Chen
Abstract: A method for implementing deep reinforcement learning with T-cell receptor (TCR) mutation policies to generate binding TCRs recognizing target peptides for immunotherapy is presented. The method includes extracting peptides to identify a virus or tumor cells, collecting a library of TCRs from target patients, predicting, by a deep neural network, interaction scores between the extracted peptides and the TCRs from the target patients, developing a deep reinforcement learning (DRL) framework with TCR mutation policies to generate TCRs with maximum binding scores, defining reward functions based on a reconstruction-based score and a density estimation-based score, randomly sampling batches of TCRs and following a policy network to mutate the TCRs, outputting mutated TCRs, and ranking the outputted TCRs to utilize top-ranked TCR candidates to target the virus or the tumor cells for immunotherapy.
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公开(公告)号：US20240071572A1
公开(公告)日：2024-02-29
申请号：US18471667
申请日：2023-09-21
Applicant: NEC Laboratories America, Inc.
Inventor： Renqiang Min , Hans Peter Graf , Ziqi Chen
Abstract: A system for binding peptide search for immunotherapy is presented. The system includes employing a deep neural network to predict a peptide presentation given Major Histocompatibility Complex allele sequences and peptide sequences, training a Variational Autoencoder (VAE) to reconstruct peptides by converting the peptide sequences into continuous embedding vectors, running a Monte Carlo Tree Search to generate a first set of positive peptide vaccine candidates, running a Bayesian Optimization search with the trained VAE and a Backpropagation search with the trained VAE to generate a second set of positive peptide vaccine candidates, using a sampling from a Position Weight Matrix (sPWM) to generate a third set of positive peptide vaccine candidates, screening and merging the first, second, and third sets of positive peptide vaccine candidates, and outputting qualified peptides for immunotherapy from the screened and merged sets of positive peptide vaccine candidates.
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Patent Agency Ranking
公开(公告)号：US20240087672A1
公开(公告)日：2024-03-14
申请号：US18471591
申请日：2023-09-21
Applicant: NEC Laboratories America, Inc.
Inventor： Renqiang Min , Hans Peter Graf , Ziqi Chen
Abstract: A method for generating binding peptides presented by any given Major Histocompatibility Complex (MHC) protein is presented. The method includes, given a peptide and an MHC protein pair, enabling a Reinforcement Learning (RL) agent to interact with and exploit a peptide mutation environment by repeatedly mutating the peptide and observing an observation score of the peptide, learning to form a mutation policy, via a mutation policy network, to iteratively mutate amino acids of the peptide to obtain desired presentation scores, and generating, based on the desired presentation scores, qualified peptides and binding motifs of MHC Class I proteins.
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公开(公告)号：US20240071563A1
公开(公告)日：2024-02-29
申请号：US18471597
申请日：2023-09-21
Applicant: NEC Laboratories America, Inc.
Inventor： Renqiang Min , Hans Peter Graf , Ziqi Chen
Abstract: A method for generating binding peptides presented by any given Major Histocompatibility Complex (MHC) protein is presented. The method includes, given a peptide and an MHC protein pair, enabling a Reinforcement Learning (RL) agent to interact with and exploit a peptide mutation environment by repeatedly mutating the peptide and observing an observation score of the peptide, learning to form a mutation policy, via a mutation policy network, to iteratively mutate amino acids of the peptide to obtain desired presentation scores, and generating, based on the desired presentation scores, qualified peptides and binding motifs of MHC Class I proteins.
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公开(公告)号：US20230085160A1
公开(公告)日：2023-03-16
申请号：US17899004
申请日：2022-08-30
Applicant: NEC Laboratories America, Inc.
Inventor： Renqiang Min , Hans Peter Graf , Ziqi Chen
Abstract: A method for generating binding peptides presented by any given Major Histocompatibility Complex (MHC) protein is presented. The method includes, given a peptide and an MHC protein pair, enabling a Reinforcement Learning (RL) agent to interact with and exploit a peptide mutation environment by repeatedly mutating the peptide and observing an observation score of the peptide, learning to form a mutation policy, via a mutation policy network, to iteratively mutate amino acids of the peptide to obtain desired presentation scores, and generating, based on the desired presentation scores, qualified peptides and binding motifs of MHC Class I proteins.
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9.发明公开公开(公告)号：US20240177799A1
公开(公告)日：2024-05-30
申请号：US18414687
申请日：2024-01-17
Applicant: NEC Laboratories America, Inc.
Inventor： Renqiang Min , Hans Peter Graf , Ziqi Chen
Abstract: A method for implementing deep reinforcement learning with T-cell receptor (TCR) mutation policies to generate binding TCRs recognizing target peptides for immunotherapy is presented. The method includes extracting peptides to identify a virus or tumor cells, collecting a library of TCRs from target patients, predicting, by a deep neural network, interaction scores between the extracted peptides and the TCRs from the target patients, developing a deep reinforcement learning (DRL) framework with TCR mutation policies to generate TCRs with maximum binding scores, defining reward functions based on a reconstruction-based score and a density estimation-based score, randomly sampling batches of TCRs and following a policy network to mutate the TCRs, outputting mutated TCRs, and ranking the outputted TCRs to utilize top-ranked TCR candidates to target the virus or the tumor cells for immunotherapy.
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公开(公告)号：US20240087673A1
公开(公告)日：2024-03-14
申请号：US18471610
申请日：2023-09-21
Applicant: NEC Laboratories America, Inc.
Inventor： Renqiang Min , Hans Peter Graf , Ziqi Chen
Abstract: A method for generating binding peptides presented by any given Major Histocompatibility Complex (MHC) protein is presented. The method includes, given a peptide and an MHC protein pair, enabling a Reinforcement Learning (RL) agent to interact with and exploit a peptide mutation environment by repeatedly mutating the peptide and observing an observation score of the peptide, learning to form a mutation policy, via a mutation policy network, to iteratively mutate amino acids of the peptide to obtain desired presentation scores, and generating, based on the desired presentation scores, qualified peptides and binding motifs of MHC Class I proteins.
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