- Patent Title: Generation of protein sequences using machine learning techniques
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Application No.: US17612918Application Date: 2020-05-19
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Publication No.: US11587645B2Publication Date: 2023-02-21
- Inventor: Tileli Amimeur , Randal Robert Ketchem , Jeremy Martin Shaver , Rutilio H. Clark , John Alex Taylor
- Applicant: Just-Evotec Biologies, Inc.
- Applicant Address: US WA Seattle
- Assignee: Just-Evotec Biologies, Inc.
- Current Assignee: Just-Evotec Biologies, Inc.
- Current Assignee Address: US WA Seattle
- Agency: Schwegman Lundberg & Woessner, P.A.
- International Application: PCT/US2020/033646 WO 20200519
- International Announcement: WO2020/236839 WO 20201126
- Main IPC: G16B40/20
- IPC: G16B40/20 ; G16B20/30 ; G06N3/12 ; G06N3/123

Abstract:
Amino acid sequences of antibodies can be generated using a generative adversarial network that includes a first generating component that generates amino acid sequences of antibody light chains and a second generating component generates amino acid sequences of antibody heavy chains. Amino acid sequences of antibodies can be produced by combining the respective amino acid sequences produced by the first generating component and the second generating component. The training of the first generating component and the second generating component can proceed at different rates. Additionally, the antibody amino acids produced by combining amino acid sequences from the first generating component and the second generating component may be evaluated according to complentarity-determining regions of the antibody amino acid sequences. Training datasets may be produced using amino acid sequences that correspond to antibodies have particular binding affinities with respect to molecules, such as binding affinity with major histocompatibility complex (MHC) molecules.
Public/Granted literature
- US20220230710A1 GENERATION OF PROTEIN SEQUENCES USING MACHINE LEARNING TECHNIQUES Public/Granted day:2022-07-21
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