利索-全球专利检索

  1. 登录
  2. 注册

搜索结果

11 条记录 (耗时 0.013 秒)

    SYSTEMS AND METHODS FOR SCREENING COMPOUNDS IN SILICO
    6.
    发明申请
    SYSTEMS AND METHODS FOR SCREENING COMPOUNDS IN SILICO 有权

    公开(公告)号：US20210104331A1

    公开(公告)日：2021-04-08

    申请号：US17038473

    申请日：2020-09-30

    申请人： Atomwise Inc.

    发明人： Venkatesh Mysore ,  Jon Sorenson ,  Greg Friedland ,  Tushita Gupta ,  Izhar Wallach

    IPC分类号： G16H50/70 ,  G16H70/40 ,  G06N20/00 ,  G06T15/10 ,  G16B5/20 ,  G16B40/00

    摘要： Systems and methods for reducing a number of test objects in a test object dataset are provided. A target model with a first computational complexity is applied to a subset of test objects from the test object dataset and a target object, thereby obtaining a subset of target results. A predictive model with a second computational complexity is trained using the subset of test objects and the subset of target results. The predictive model is applied to the plurality of test objects, thereby obtaining a plurality of predictive results. A portion of the test objects are eliminated from the plurality of test objects based at least in part on the plurality of predictive results. The method determines whether one or more predefined reduction criteria are satisfied. When the predefined reduction criteria are not satisfied, an additional subset of test objects and target results are obtained, and the method is repeated.

    Systems and methods for correcting error in a first classifier by evaluating classifier output in parallel
    7.
    发明授权
    Systems and methods for correcting error in a first classifier by evaluating classifier output in parallel 有权

    公开(公告)号：US10546237B2

    公开(公告)日：2020-01-28

    申请号：US15473980

    申请日：2017-03-30

    申请人： Atomwise Inc.

    发明人： Abraham Samuel Heifets ,  Izhar Wallach ,  Kong Thong Nguyen

    IPC分类号： G06N5/04 ,  G06N3/08 ,  G06N3/04 ,  G16C20/30 ,  G16C20/70

    摘要： Systems and methods for classifying a test object are provided. For each respective target object in a plurality of target objects, a first procedure is performed comprising (a) posing the test object against the respective target thereby obtaining an interaction between the test and target, and (b) scoring the interaction with a first classifier. Each such score across the plurality of targets forms a test vector that is inputted into a second classifier thereby obtaining an indication of a target object. The second classifier is trained on training vectors, each being the output from instances of the first classifier after inputting a corresponding training object in a plurality of training objects in accordance with the first procedure. Each object in one subset of the training objects is uniquely associated with one of the targets. Another subset of the training objects is not associated with the targets.

    Systems and methods for applying a convolutional network to spatial data
    8.
    发明授权
    Systems and methods for applying a convolutional network to spatial data 有权

    公开(公告)号：US10482355B2

    公开(公告)日：2019-11-19

    申请号：US16011373

    申请日：2018-06-18

    申请人： Atomwise Inc.

    发明人： Abraham Samuel Heifets ,  Izhar Wallach ,  Michael Dzamba

    IPC分类号： G06K9/66 ,  G06N3/08 ,  G06T1/20 ,  G06T1/60 ,  G06K9/52 ,  G06T7/60 ,  G06K9/62 ,  G16B15/00 ,  G16B20/00 ,  G16B40/00 ,  G06T15/08 ,  G06K9/46 ,  G06N20/00

    摘要： Systems and methods for test object classification are provided in which the test object is docked with a target object in a plurality of different poses to form voxel maps. The maps are vectorized and fed into a convolutional neural network comprising an input layer, a plurality of individually weighted convolutional layers, and an output scorer. The convolutional layers include initial and final layers. Responsive to vectorized input, the input layer feeds values into the initial convolutional layer. Each respective convolutional layer, other than the final convolutional layer, feeds intermediate values as a function of the weights and input values of the respective layer into another of the convolutional layers. The final convolutional layer feeds values into one or more fully connected layers as a function of the final layer weights and input values. The one or more full connected layers feed values into the scorer which scores each input vector to thereby classify the test object.

    Systems and methods for applying a convolutional network to spatial data
    10.
    发明授权
    Systems and methods for applying a convolutional network to spatial data 有权

    公开(公告)号：US11080570B2

    公开(公告)日：2021-08-03

    申请号：US16675887

    申请日：2019-11-06

    申请人： Atomwise Inc.

    发明人： Abraham Samuel Heifets ,  Izhar Wallach ,  Michael Dzamba

    IPC分类号： G06K9/66 ,  G06N3/08 ,  G06T1/20 ,  G06T1/60 ,  G06K9/52 ,  G06T7/60 ,  G06K9/62 ,  G16B15/00 ,  G16B20/00 ,  G16B40/00 ,  G06K9/46 ,  G06T15/08 ,  G06N20/00

    摘要： Systems and methods for test object classification are provided in which the test object is docked with a target object in a plurality of different poses to form voxel maps. The maps are vectorized and fed into a convolutional neural network comprising an input layer, a plurality of individually weighted convolutional layers, and an output scorer. The convolutional layers include initial and final layers. Responsive to vectorized input, the input layer feeds values into the initial convolutional layer. Each respective convolutional layer, other than the final convolutional layer, feeds intermediate values as a function of the weights and input values of the respective layer into another of the convolutional layers. The final convolutional layer feeds values into one or more fully connected layers as a function of the final layer weights and input values. The one or more full connected layers feed values into the scorer which scores each input vector to thereby classify the test object.