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公开(公告)号：US20210189385A1
公开(公告)日：2021-06-24
申请号：US17126842
申请日：2020-12-18
Applicant: X Development LLC
Inventor： Ivan Grubisic
IPC: C12N15/10 , C12N15/115 , G16B35/20
Abstract: The present disclosure relates to a closed loop aptamer development system that identifies one or more aptamers observed experimentally and implements machine-learning models to identify other aptamers not observed experimentally. Particularly, aspects of the present disclosure are directed to receiving a query concerning one or more targets, acquiring a library of aptamers that potential satisfy the query, identifying a first set of aptamers from the library of aptamers that substantially or completely satisfy the query, obtaining sequence data for the first set of aptamers, generating, by a prediction model, a third set of aptamers derived from the sequence data for the first set of aptamers, validating the third set of aptamers that substantially or completely satisfy the query, and upon validating the third set of aptamers and in response to the query, providing the third set of aptamers as a result to the query.
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2.发明申请公开(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
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.
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公开(公告)号：US20220341934A1
公开(公告)日：2022-10-27
申请号：US17236538
申请日：2021-04-21
Applicant: X Development LLC
Inventor： Ivan Grubisic , Ray Nagatani
IPC: G01N33/574 , G16B5/20 , C12N15/115 , G01N33/53
Abstract: Methods described herein include receiving data from flowing a plurality of aptamers over a sample of tumor cells randomly affixed to a surface of a microfluidic device. The tumor cells may include one or more unknown tumor subtypes of cells. The plurality of aptamers may include a plurality of aptamer families. Each aptamer family of the plurality of aptamer families may be determined to bind to at least one possible subtype of the tumor cells. The data may include a measure of binding affinity of each aptamer family to the tumor cells. The method may include analyzing the measure of the binding affinity of each aptamer family to the tumor cells. The analyzing may include classifying the binding affinity. The method may also include determining one or more aptamer families that characterize the one or more unknown tumor subtypes of cells based on the classifying.
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4.发明公开公开(公告)号：US20240087682A1
公开(公告)日：2024-03-14
申请号：US17932153
申请日：2022-09-14
Applicant: X Development LLC
Inventor： Jon Deaton , Hayley Weir , Ryan Poplin , Ivan Grubisic
Abstract: A multi-dimensional latent space (defined by an Encoder model) corresponds to projections of sequences of aptamers. An architecture of the Encoder model, a hyperparameter of the Encoder model, or a characteristic of a training data set used to train the Encoder model was selected using an assessment of an encoding-efficiency of the Encoder model that is based on: a predicted extents to which representations in an embedding space are indicative of specific aptamer sequences to which a probability distribution of the embedding space differs from a probability distribution of a source space that represents individual base-pairs; generating projections in the latent space using representations of aptamers and the Encoder model; identifying one or more candidate aptamers for the particular target using the projections and the Decoder model; and outputting an identification of the one or more candidate aptamers.
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5.发明申请公开(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
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.
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Patent Agency Ranking
公开(公告)号：US20220267762A1
公开(公告)日：2022-08-25
申请号：US17662022
申请日：2022-05-04
Applicant: X Development LLC
Inventor： Ivan Grubisic
IPC: C12N15/10 , G16B35/20 , C12N15/115
Abstract: The present disclosure relates to a closed loop aptamer development system that identifies one or more aptamers observed experimentally and implements machine-learning models to identify other aptamers not observed experimentally. Particularly, aspects of the present disclosure are directed to receiving a query concerning one or more targets, acquiring a library of aptamers that potential satisfy the query, identifying a first set of aptamers from the library of aptamers that substantially or completely satisfy the query, obtaining sequence data for the first set of aptamers, generating, by a prediction model, a third set of aptamers derived from the sequence data for the first set of aptamers, validating the third set of aptamers that substantially or completely satisfy the query, and upon validating the third set of aptamers and in response to the query, providing the third set of aptamers as a result to the query.
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公开(公告)号：US11636916B1
公开(公告)日：2023-04-25
申请号：US15976576
申请日：2018-05-10
Applicant: X Development LLC
Inventor： Johan Jessen , Ivan Grubisic
Abstract: A whole cell model may be constructed and used to simulate cell behavior. The whole cell model may have a baseline cell state that can be perturbed by a user in order to understand the behavior and importance of various molecules, processes and/or sub-models within the whole cell model. The simulation data is evaluated according to a variety of heuristics. The simulation data is ranked within each heuristic. The heuristic evaluation of the simulation data is then compared to an input perturbation to determine the relative importance of the heuristics. The output is a visualization of the simulation data according to each heuristic within a dynamic ranked display.
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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
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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公开(公告)号：US20210158890A1
公开(公告)日：2021-05-27
申请号：US16692522
申请日：2019-11-22
Applicant: X Development LLC
Inventor： Ivan Grubisic , David Brookes
Abstract: The present disclosure relates to development of aptamers, and in particular to developing machine-learning models to describe characteristics of a given sequence for an aptamer and based on the characteristics find other sequences for aptamers not observed experimentally, and techniques for separating out sequences for aptamers that are present primarily due to PCR bias and/or binding affinity. Particularly, aspects of the present disclosure are directed to obtaining sequence data for an aptamer sequence that binds to a target, generating a binding affinity latent variable and a PCR bias latent variable based on the sequence data, generating a predicted count of the aptamer sequence based on the binding affinity latent variable and PCR bias latent variable, determining that the binding affinity latent variable is greater than the PCR bias latent variable, and in response to the determining, accepting the predicted count of the aptamer sequence.
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公开(公告)号：US20240086423A1
公开(公告)日：2024-03-14
申请号：US17898236
申请日：2022-08-29
Applicant: X Development LLC
Inventor： Lance Co Ting Keh , Ivan Grubisic , Ryan Poplin , Jon Deaton , Hayley Weir
IPC: G06F16/28 , G06N3/0455 , G06N3/08
CPC classification number: G06F16/285 , G06N3/0455 , G06N3/08
Abstract: Some techniques relate to projecting aptamer representations into an embedding space and clustering the representations. A cluster-specific binding metric can be defined for each cluster based on aptamer-specific binding metrics of aptamers associated with the cluster. A subset of the clusters can be selected based on the cluster-specific binding metrics. Identifications of aptamers assigned to the subset of clusters can then be output.
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