SG11201802759YA - Systems and methods for applying a convolutional network to spatial data - Google Patents

Systems and methods for applying a convolutional network to spatial data

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Publication number
SG11201802759YA
SG11201802759YA SG11201802759YA SG11201802759YA SG11201802759YA SG 11201802759Y A SG11201802759Y A SG 11201802759YA SG 11201802759Y A SG11201802759Y A SG 11201802759YA SG 11201802759Y A SG11201802759Y A SG 11201802759YA SG 11201802759Y A SG11201802759Y A SG 11201802759YA
Authority
SG
Singapore
Prior art keywords
applying
systems
methods
spatial data
convolutional network
Prior art date
Application number
SG11201802759YA
Other languages
English (en)
Inventor
Abraham Heifets
Izhar Wallach
Michael Dzamba
Original Assignee
Atomwise Inc
Priority date (The priority date is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the date listed.)
Filing date
Publication date
Priority claimed from US15/050,983 external-priority patent/US9373059B1/en
Application filed by Atomwise Inc filed Critical Atomwise Inc
Publication of SG11201802759YA publication Critical patent/SG11201802759YA/en

Links

Classifications

    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06N—COMPUTING ARRANGEMENTS BASED ON SPECIFIC COMPUTATIONAL MODELS
    • G06N3/00—Computing arrangements based on biological models
    • G06N3/02—Neural networks
    • G06N3/08—Learning methods
    • G06N3/086—Learning methods using evolutionary algorithms, e.g. genetic algorithms or genetic programming
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06N—COMPUTING ARRANGEMENTS BASED ON SPECIFIC COMPUTATIONAL MODELS
    • G06N20/00—Machine learning
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06N—COMPUTING ARRANGEMENTS BASED ON SPECIFIC COMPUTATIONAL MODELS
    • G06N3/00—Computing arrangements based on biological models
    • G06N3/02—Neural networks
    • G06N3/04—Architecture, e.g. interconnection topology
    • G06N3/045—Combinations of networks
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06V—IMAGE OR VIDEO RECOGNITION OR UNDERSTANDING
    • G06V10/00—Arrangements for image or video recognition or understanding
    • G06V10/40—Extraction of image or video features
    • G06V10/44—Local feature extraction by analysis of parts of the pattern, e.g. by detecting edges, contours, loops, corners, strokes or intersections; Connectivity analysis, e.g. of connected components
    • G06V10/443—Local feature extraction by analysis of parts of the pattern, e.g. by detecting edges, contours, loops, corners, strokes or intersections; Connectivity analysis, e.g. of connected components by matching or filtering
    • G06V10/449—Biologically inspired filters, e.g. difference of Gaussians [DoG] or Gabor filters
    • G06V10/451—Biologically inspired filters, e.g. difference of Gaussians [DoG] or Gabor filters with interaction between the filter responses, e.g. cortical complex cells
    • G06V10/454—Integrating the filters into a hierarchical structure, e.g. convolutional neural networks [CNN]
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06V—IMAGE OR VIDEO RECOGNITION OR UNDERSTANDING
    • G06V10/00—Arrangements for image or video recognition or understanding
    • G06V10/70—Arrangements for image or video recognition or understanding using pattern recognition or machine learning
    • G06V10/764—Arrangements for image or video recognition or understanding using pattern recognition or machine learning using classification, e.g. of video objects
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06V—IMAGE OR VIDEO RECOGNITION OR UNDERSTANDING
    • G06V10/00—Arrangements for image or video recognition or understanding
    • G06V10/70—Arrangements for image or video recognition or understanding using pattern recognition or machine learning
    • G06V10/82—Arrangements for image or video recognition or understanding using pattern recognition or machine learning using neural networks
    • G—PHYSICS
    • G16—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR SPECIFIC APPLICATION FIELDS
    • G16C—COMPUTATIONAL CHEMISTRY; CHEMOINFORMATICS; COMPUTATIONAL MATERIALS SCIENCE
    • G16C20/00—Chemoinformatics, i.e. ICT specially adapted for the handling of physicochemical or structural data of chemical particles, elements, compounds or mixtures
    • G16C20/50—Molecular design, e.g. of drugs
    • G—PHYSICS
    • G16—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR SPECIFIC APPLICATION FIELDS
    • G16C—COMPUTATIONAL CHEMISTRY; CHEMOINFORMATICS; COMPUTATIONAL MATERIALS SCIENCE
    • G16C20/00—Chemoinformatics, i.e. ICT specially adapted for the handling of physicochemical or structural data of chemical particles, elements, compounds or mixtures
    • G16C20/70—Machine learning, data mining or chemometrics

Landscapes

  • Engineering & Computer Science (AREA)
  • Theoretical Computer Science (AREA)
  • Physics & Mathematics (AREA)
  • Evolutionary Computation (AREA)
  • Health & Medical Sciences (AREA)
  • Software Systems (AREA)
  • Artificial Intelligence (AREA)
  • Computing Systems (AREA)
  • General Physics & Mathematics (AREA)
  • General Health & Medical Sciences (AREA)
  • Computer Vision & Pattern Recognition (AREA)
  • Life Sciences & Earth Sciences (AREA)
  • Medical Informatics (AREA)
  • Multimedia (AREA)
  • Data Mining & Analysis (AREA)
  • Bioinformatics & Cheminformatics (AREA)
  • Biomedical Technology (AREA)
  • General Engineering & Computer Science (AREA)
  • Molecular Biology (AREA)
  • Mathematical Physics (AREA)
  • Databases & Information Systems (AREA)
  • Bioinformatics & Computational Biology (AREA)
  • Chemical & Material Sciences (AREA)
  • Computational Linguistics (AREA)
  • Biophysics (AREA)
  • Physiology (AREA)
  • Biodiversity & Conservation Biology (AREA)
  • Evolutionary Biology (AREA)
  • Crystallography & Structural Chemistry (AREA)
  • Medicinal Chemistry (AREA)
  • Pharmacology & Pharmacy (AREA)
  • Spectroscopy & Molecular Physics (AREA)
  • Investigating Or Analysing Biological Materials (AREA)
SG11201802759YA 2015-10-04 2016-10-04 Systems and methods for applying a convolutional network to spatial data SG11201802759YA (en)

Applications Claiming Priority (4)

Application Number Priority Date Filing Date Title
US201562236962P 2015-10-04 2015-10-04
US15/050,983 US9373059B1 (en) 2014-05-05 2016-02-23 Systems and methods for applying a convolutional network to spatial data
US15/187,018 US10002312B2 (en) 2014-05-05 2016-06-20 Systems and methods for applying a convolutional network to spatial data
PCT/US2016/055369 WO2017062382A1 (en) 2015-10-04 2016-10-04 Systems and methods for applying a convolutional network to spatial data

Publications (1)

Publication Number Publication Date
SG11201802759YA true SG11201802759YA (en) 2018-05-30

Family

ID=58488391

Family Applications (1)

Application Number Title Priority Date Filing Date
SG11201802759YA SG11201802759YA (en) 2015-10-04 2016-10-04 Systems and methods for applying a convolutional network to spatial data

Country Status (8)

Country Link
EP (2) EP3680820B1 (hu)
JP (1) JP6975140B2 (hu)
CN (1) CN108140131B (hu)
DK (1) DK3356999T3 (hu)
ES (1) ES2772687T3 (hu)
HU (1) HUE047807T2 (hu)
SG (1) SG11201802759YA (hu)
WO (1) WO2017062382A1 (hu)

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KR102601604B1 (ko) * 2017-08-04 2023-11-13 삼성전자주식회사 뉴럴 네트워크의 파라미터들을 양자화하는 방법 및 장치
TWI822789B (zh) * 2018-06-01 2023-11-21 美商格瑞爾有限責任公司 用於資料分類之卷積神經網路系統及方法
CN109033505A (zh) * 2018-06-06 2018-12-18 东北大学 一种基于深度学习的超快冷温度控制方法
CN108880568A (zh) * 2018-07-06 2018-11-23 中国石油大学(华东) 一种基于卷积神经网络的串行抵消列表译码参数优化方法
CN112585685A (zh) * 2018-09-21 2021-03-30 渊慧科技有限公司 确定蛋白结构的机器学习
US11455439B2 (en) * 2018-11-28 2022-09-27 Robert Bosch Gmbh Neural network force field computational algorithms for molecular dynamics computer simulations
CN109800231B (zh) * 2019-01-17 2020-12-08 浙江大学 一种基于Flink的实时轨迹co-movement运动模式检测方法
US11918336B2 (en) * 2019-02-19 2024-03-05 King Abdullah University Of Science And Technology Reduced feature generation for signal classification based on position weight matrix
CN109959911A (zh) * 2019-03-25 2019-07-02 清华大学 基于激光雷达的多目标自主定位方法及装置
JP7387760B2 (ja) * 2019-05-02 2023-11-28 ボード オブ リージェンツ，ザ ユニバーシティ オブ テキサス システム 合成タンパク質の安定性を高めるためのシステムおよび方法
CN110265092B (zh) * 2019-05-10 2021-08-24 江苏理工学院 基于人工智能的抗体-抗原分子对接评价方法和系统
CN110163369B (zh) * 2019-05-21 2021-05-11 北京迈格威科技有限公司 图像识别与神经网络模型的训练方法、装置和系统
CN110600125B (zh) * 2019-09-18 2022-05-24 山东浪潮科学研究院有限公司 一种基于人工智能的体态分析助手系统及传输方法
CN110675488B (zh) * 2019-09-24 2023-02-28 电子科技大学 基于深度学习的创意三维体素模型的建模系统的构建方法
CN110995277B (zh) * 2019-12-06 2021-06-01 浙江大学 一种多层神经网络辅助的罚对偶分解信道译码方法
CN112086145B (zh) * 2020-09-02 2024-04-16 腾讯科技（深圳）有限公司 一种化合物活性预测方法、装置、电子设备和存储介质
CN112488309B (zh) * 2020-12-21 2023-10-20 清华大学深圳国际研究生院 基于临界阻尼动量的深度神经网络的训练方法及系统
CN112668536B (zh) * 2021-01-06 2023-08-25 北京理工大学 一种基于机载光电视频的轻量化旋转目标检测识别方法
CN114822717A (zh) * 2021-01-28 2022-07-29 腾讯科技（深圳）有限公司 基于人工智能的药物分子处理方法、装置、设备及存储介质
CN115083537A (zh) * 2021-03-10 2022-09-20 腾讯科技（深圳）有限公司 分子骨架跃迁的处理方法、装置、介质及电子设备
CN113066539A (zh) * 2021-03-22 2021-07-02 上海商汤智能科技有限公司 预测方法及相关装置、设备
CN113721293B (zh) * 2021-08-30 2022-07-01 电子科技大学 一种基于深度学习的多波地震信号人工智能匹配方法
WO2023055949A1 (en) * 2021-10-01 2023-04-06 Atomwise Inc. Characterization of interactions between compounds and polymers using negative pose data and model conditioning
CN114708931B (zh) * 2022-04-22 2023-01-24 中国海洋大学 结合机器学习和构象计算提高药-靶活性预测精度的方法

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CN102930152B (zh) * 2012-10-26 2016-08-03 中国科学院上海药物研究所 一种模拟配体分子与靶标受体反应并计算预测该反应的热力学与动力学参数的方法和系统
CN102930181B (zh) * 2012-11-07 2015-05-27 四川大学 基于分子描述符的蛋白质-配体亲和力预测方法
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Also Published As

Publication number Publication date
CN108140131B (zh) 2021-09-14
WO2017062382A1 (en) 2017-04-13
EP3356999A4 (en) 2019-07-03
ES2772687T3 (es) 2020-07-08
JP6975140B2 (ja) 2021-12-01
JP2019501433A (ja) 2019-01-17
CN108140131A (zh) 2018-06-08
EP3356999A1 (en) 2018-08-08
EP3680820A1 (en) 2020-07-15
HUE047807T2 (hu) 2020-05-28
DK3356999T3 (da) 2020-02-03
EP3680820B1 (en) 2021-12-01
EP3356999B1 (en) 2019-11-27

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