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    EXPERIMENT AND MACHINE-LEARNING TECHNIQUES TO IDENTIFY AND GENERATE HIGH AFFINITY BINDERS
    2.
    发明申请
    EXPERIMENT AND MACHINE-LEARNING TECHNIQUES TO IDENTIFY AND GENERATE HIGH AFFINITY BINDERS 有权

    公开(公告)号：US20220380753A1

    公开(公告)日：2022-12-01

    申请号：US17333272

    申请日：2021-05-28

    Applicant: X Development LLC

    Inventor： Ivan Grubisic ,  Ray Nagatani ,  Lance Co Ting Keh ,  Andrew Weitz ,  Kenneth Jung ,  Ryan Poplin

    IPC: C12N15/10 ,  G16B35/00 ,  G16B15/30

    Abstract: The present disclosure relates to in vitro experiments and in silico computation and machine-learning based techniques to iteratively improve a process for identifying binders that can bind any given molecular target. Particularly, aspects of the present disclosure are directed to obtaining sequence data for aptamers that bind to a target, where the sequence data has a first signal to noise ratio, generating, by a search process, a first set of aptamer sequences derived from the sequence data, obtaining subsequent sequence data for subsequent aptamers that bind to the target, where the subsequent aptamers includes aptamers synthesized from the first set of aptamer sequences, and the subsequent sequence data has a second signal to noise ratio greater than the first signal to noise ratio, generating, by a linear machine-learning model, a second set of aptamer sequences derived from the subsequent sequence data, and outputting the second set of aptamer sequences.

    Deformulation techniques for deducing the composition of a material from a spectrogram
    3.
    发明授权
    Deformulation techniques for deducing the composition of a material from a spectrogram 有权

    公开(公告)号：US11353394B2

    公开(公告)日：2022-06-07

    申请号：US16948760

    申请日：2020-09-30

    Applicant: X Development LLC

    Inventor： Gearoid Murphy ,  Artem Goncharuk ,  Lance Co Ting Keh ,  Diosdado Rey Banatao ,  Sujit Sanjeev

    IPC: G01N21/31 ,  G01J3/28 ,  G16C20/20 ,  G01N33/52

    Abstract: The present disclosure relates to techniques for deformulating the spectra of arbitrary compound formulations such as polymer formulations into their chemical components. Particularly, aspects of the present disclosure are directed to obtaining an initial set of spectra for a plurality of samples comprising pure samples and composite samples, constructing a basis set of spectra for a plurality of pure samples based on the initial set of spectra, and providing or outputting the basis set of spectrum. The basis set of spectra is constructed in an iterative process that attempts to decompose, using a decomposition algorithm or model, the spectrum from the initial set of spectra in order to differentiate the pure samples from the composite samples. The basis set of spectra may then be used to deduce the composition of a material from a spectrogram.

    END-TO-END APTAMER DEVELOPMENT SYSTEM
    6.
    发明申请
    END-TO-END APTAMER DEVELOPMENT SYSTEM 有权

    公开(公告)号：US20230101523A1

    公开(公告)日：2023-03-30

    申请号：US17936181

    申请日：2022-09-28

    Applicant: X Development LLC

    Inventor： Ryan Poplin ,  Lance Co Ting Keh ,  Ivan Grubisic ,  Ray Nagatani

    IPC: G16B30/10 ,  C12N15/115 ,  G06N3/08 ,  G16B35/20 ,  G06N20/20 ,  G16B40/20

    Abstract: The present disclosure relates to in vitro experiments and in silico computation and machine-learning based techniques to iteratively improve a process for identifying binders that can bind a target. Particularly, aspects of the present disclosure are directed to obtaining initial sequence data, identifying, by a first machine-learning model having model parameters learned from the initial sequence data, a first set of aptamer sequences, obtaining, using an in vitro binding selection process, subsequent sequence data including sequences from the first set of aptamer sequences, identifying, by a second machine-learning model having model parameters learned from the subsequent sequence data, a second set of aptamer sequences, determining, using one or more in vitro assays, analytical data for aptamers synthesized from the second set of aptamer sequences, and identifying a final set of aptamer sequences from the second set of aptamer sequences based on the analytical data associated with each aptamer.

    EXPERIMENT AND MACHINE-LEARNING TECHNIQUES TO IDENTIFY AND GENERATE HIGH AFFINITY BINDERS
    7.
    发明申请
    EXPERIMENT AND MACHINE-LEARNING TECHNIQUES TO IDENTIFY AND GENERATE HIGH AFFINITY BINDERS 有权

    公开(公告)号：US20220383981A1

    公开(公告)日：2022-12-01

    申请号：US17333287

    申请日：2021-05-28

    Applicant: X Development LLC

    Inventor： Ivan Grubisic ,  Ray Nagatani ,  Lance Co Ting Keh ,  Andrew Weitz ,  Kenneth Jung ,  Ryan Poplin

    IPC: G16B35/10 ,  G16B40/00 ,  G16B5/20 ,  G06N20/20

    Abstract: The present disclosure relates to in vitro experiments and in silico computation and machine-learning based techniques to iteratively improve a process for identifying binders that can bind any given molecular target. Particularly, aspects of the present disclosure are directed to obtaining initial sequence data for aptamers that bind to a target, measuring a first signal to noise ratio within the initial sequence data, provisioning, based on the first signal to noise ratio, a first machine-learning system, generating, by the first machine-learning system, a first set of aptamer sequences, obtaining subsequent sequence data for aptamers that bind to the target, measuring a second signal to noise ratio within the subsequent sequence data, provisioning, based on the second signal to noise ratio, a second machine-learning system, generating, by the second machine-learning system, a second set of aptamer sequences, and outputting the second set of aptamer sequences.

    Deformulation techniques for deducing the composition of a material from a spectrogram
    9.
    发明授权
    Deformulation techniques for deducing the composition of a material from a spectrogram 有权

    公开(公告)号：US11630057B2

    公开(公告)日：2023-04-18

    申请号：US17658765

    申请日：2022-04-11

    Applicant: X Development LLC

    Inventor： Gearoid Murphy ,  Artem Goncharuk ,  Lance Co Ting Keh ,  Diosdado Rey Banatao ,  Sujit Sanjeev

    IPC: G01N21/31 ,  G16C20/20 ,  G01J3/28 ,  G01N33/52

    Abstract: The present disclosure relates to techniques for deformulating the spectra of arbitrary compound formulations such as polymer formulations into their chemical components. Particularly, aspects of the present disclosure are directed to obtaining an initial set of spectra for a plurality of samples comprising pure samples and composite samples, constructing a basis set of spectra for a plurality of pure samples based on the initial set of spectra, and providing or outputting the basis set of spectrum. The basis set of spectra is constructed in an iterative process that attempts to decompose, using a decomposition algorithm or model, the spectrum from the initial set of spectra in order to differentiate the pure samples from the composite samples. The basis set of spectra may then be used to deduce the composition of a material from a spectrogram.

    GENERATIVE TNA SEQUENCE DESIGN WITH EXPERIMENT-IN-THE-LOOP TRAINING
    10.
    发明申请
    GENERATIVE TNA SEQUENCE DESIGN WITH EXPERIMENT-IN-THE-LOOP TRAINING 有权

    公开(公告)号：US20230081439A1

    公开(公告)日：2023-03-16

    申请号：US17471903

    申请日：2021-09-10

    Applicant: X Development LLC

    Inventor： Ryan Poplin ,  Ivan Grubisic ,  Lance Co Ting Keh ,  Ray Nagatani

    IPC: G16B40/20 ,  G16B20/30 ,  G16B30/00 ,  G16H20/00 ,  G06K9/62

    Abstract: A latent space is defined to represent sequences using training data and a machine-learning model. The training data identifies sequences of molecules and binding-approximation metrics that characterizes whether the molecules bind to a particular target and/or that approximate an extent to which the molecule is more likely to bind to the particular target than some other molecules. Supplemental training data is accessed that identifies other sequences of other molecules and binding affinity scores quantifying binding strengths between the molecules and the particular target. Projections of representations of the other sequences in the supplemental training data are projected in the latent space using the binding affinity scores. An area or position of interest within the latent space is identified based on the projections. A particular sequence represented within or at the area or position of interest or at the position of interest is identified for downstream processing.

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