公开(公告)号：US11906518B2

公开(公告)日：2024-02-20

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 ,  C40B30/04 ,  G16C20/20 ,  G16C20/50 ,  G16C20/70

CPC classification number: G01N33/574 ,  C12N15/115 ,  C40B30/04 ,  G16B5/20 ,  G16C20/20 ,  G16C20/50 ,  G16C20/70 ,  C12N2310/16 ,  C12N2320/10 ,  G01N2500/10

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.

公开(公告)号：US11834656B2

公开(公告)日：2023-12-05

申请号：US17126842

申请日：2020-12-18

Applicant: X Development LLC

Inventor： Ivan Grubisic

IPC: C12N15/10 ,  C12N15/115 ,  G16B35/20

CPC classification number: C12N15/1089 ,  C12N15/115 ,  G16B35/20 ,  C12N2310/16 ,  C12N2310/3231

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.

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.

公开(公告)号：US20210363528A1

公开(公告)日：2021-11-25

申请号：US16877729

申请日：2020-05-19

Applicant: X Development LLC

Inventor： Ivan Grubisic ,  Ray Nagatani

IPC: C12N15/115 ,  C12N15/10 ,  C40B40/08 ,  G16B5/00

Abstract: The present disclosure relates to a biologics development platform that derives biologics from aptamers found to bind to a target. Particularly, aspects of the present disclosure are directed to generating sequencing data and analysis data for each unique aptamer of an aptamer library that binds to a target within a monoclonal compartment, inferring aptamer sequences derived from the sequencing data and the analysis data, identifying interaction points between the aptamer sequences and epitopes of the target based on structure or sequence motifs of the aptamer sequences, modeling molecular dynamics of interactions between the aptamer sequences and the epitopes to identify characteristics of the interaction points as requirements or restraints for the interactions, and inferring one or more amino acid sequences based on the characteristics of the interaction points derived from the interactions between aptamer sequences and the epitopes.