WO2022119256A1 - Dispositif de prédiction de réponse à un médicament à l'aide d'un réseau hiérarchique à base d'auto-attention et procédé associé - Google Patents

Dispositif de prédiction de réponse à un médicament à l'aide d'un réseau hiérarchique à base d'auto-attention et procédé associé Download PDF

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WO2022119256A1
WO2022119256A1 PCT/KR2021/017761 KR2021017761W WO2022119256A1 WO 2022119256 A1 WO2022119256 A1 WO 2022119256A1 KR 2021017761 W KR2021017761 W KR 2021017761W WO 2022119256 A1 WO2022119256 A1 WO 2022119256A1
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drug
pathway
level
network
gene
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PCT/KR2021/017761
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Korean (ko)
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남호정
진일중
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광주과학기술원
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    • 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
    • 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
    • G—PHYSICS
    • G16—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR SPECIFIC APPLICATION FIELDS
    • G16B—BIOINFORMATICS, i.e. INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR GENETIC OR PROTEIN-RELATED DATA PROCESSING IN COMPUTATIONAL MOLECULAR BIOLOGY
    • G16B15/00—ICT specially adapted for analysing two-dimensional or three-dimensional molecular structures, e.g. structural or functional relations or structure alignment
    • G16B15/30—Drug targeting using structural data; Docking or binding prediction
    • G—PHYSICS
    • G16—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR SPECIFIC APPLICATION FIELDS
    • G16B—BIOINFORMATICS, i.e. INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR GENETIC OR PROTEIN-RELATED DATA PROCESSING IN COMPUTATIONAL MOLECULAR BIOLOGY
    • G16B40/00—ICT specially adapted for biostatistics; ICT specially adapted for bioinformatics-related machine learning or data mining, e.g. knowledge discovery or pattern finding
    • G16B40/20—Supervised data analysis
    • 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
    • G16C20/30—Prediction of properties of chemical compounds, compositions or mixtures
    • 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
    • G16C20/60—In silico combinatorial chemistry
    • 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
    • G16C20/90—Programming languages; Computing architectures; Database systems; Data warehousing

Definitions

  • the present invention relates to drug reactivity prediction, and more particularly, to drug reactivity prediction technology using an artificial neural network.
  • the method of experimenting based on living organisms is called in vivo, and the method through a glass test tube is called in-vitro.
  • GDSC Geneomics of Drug Sensitivity in Cancer
  • the inventors of the present invention have been working hard to solve the problems of drug reactivity prediction using artificial intelligence of the prior art. After much effort, the present invention was completed after much effort to complete a drug responsiveness prediction device and method including an artificial intelligence network that can reflect the importance that varies depending on the expression level of genes for new or existing cancer cells.
  • An object of the present invention is to provide an apparatus and method for predicting drug reactivity including an artificial intelligence network that can reflect the state of cells, such as the amount of gene expression in cells that change frequently based on the self-attention technique. .
  • a gene-level network that outputs pathway activity in a gene-level self-attention-based artificial intelligence network that inputs gene expression levels and drug descriptors for each pathway; a pathway-level network that receives the pathway activity as an input and outputs a pathway feature multiplied by a weight to a pathway-level self-attention-based AI network; a drug network that outputs drug descriptors using a drug deep learning network (DNN); and a reactivity prediction network for outputting a drug response prediction using a drug response deep learning network by inputting the weighted pathway feature and the drug descriptor as inputs.
  • DNN drug deep learning network
  • the gene-level network obtains a gene-level attention score by using a value passed through a gene-level weight network with the gene expression level and drug descriptor as inputs, and the gene-level attention score and the gene expression level It is characterized in that the pathway activity is obtained by a value multiplied by .
  • the pathway level network obtains a pathway level attention score as a value passed through a pathway level weight network with the pathway activity and drug descriptor as inputs, and multiplies the pathway level attention score by the pathway activity. It is characterized in that a path feature multiplied by a weight is obtained.
  • the drug descriptor is characterized in that it is obtained using a Morgan fingerprint.
  • the reactivity prediction network is characterized in that the drug response prediction is output as an IC50 predicted value.
  • the step (a) is to obtain a gene level attention score (Attention Score) using the value passed through the gene level weight network as the input of the gene expression level and the drug descriptor, the gene level attention score and the gene expression It is characterized in that the pathway activity is calculated as a value multiplied by the amount.
  • a gene level attention score (Attention Score) using the value passed through the gene level weight network as the input of the gene expression level and the drug descriptor, the gene level attention score and the gene expression It is characterized in that the pathway activity is calculated as a value multiplied by the amount.
  • the pathway-level network obtains a pathway-level attention score using a value passed through a pathway-level weighting network with the pathway activity and drug descriptor as inputs, and the pathway-level attention score and the pathway A value multiplied by the activity is characterized in that a pathway feature multiplied by the weight is obtained.
  • the drug descriptor in step (c) is characterized in that it is obtained using a Morgan fingerprint.
  • the step (d) is characterized in that the drug response prediction is output as an IC50 predicted value.
  • the network state is changed to reflect this, and it is possible to predict an accurate drug response.
  • FIG. 1 is a schematic structural diagram of a self-attention-based drug reactivity prediction device according to a preferred embodiment of the present invention.
  • FIG. 2 is a schematic structural diagram of a gene level network according to a preferred embodiment of the present invention.
  • FIG. 3 is a schematic structural diagram of a pathway level network according to a preferred embodiment of the present invention.
  • FIG. 4 is a schematic structural diagram of a drug network according to a preferred embodiment of the present invention.
  • FIG. 5 is a schematic structural diagram of a reactive prediction network according to a preferred embodiment of the present invention.
  • FIG. 6 is a graph showing the drug reactivity results predicted by the self-attention-based drug reactivity prediction device according to a preferred embodiment of the present invention.
  • FIG. 7 is a flowchart of a method for predicting self-attention-based drug reactivity according to another preferred embodiment of the present invention.
  • FIG. 1 is a schematic structural diagram of a self-attention-based drug reactivity prediction device according to a preferred embodiment of the present invention.
  • Self-attention-based drug reactivity prediction apparatus 100 is a gene-level network 110 , a pathway-level network 120 , a drug network 130 , and reactivity and a Response Prediction network 140 .
  • the self-attention-based drug reactivity prediction apparatus 100 predicts the reactivity between a drug and cancer cells using an artificial intelligence network.
  • the self-attention-based drug reactivity prediction apparatus 100 may include a processor and a memory, and program codes for driving the processor and learned network data may be stored in the memory.
  • an artificial intelligence neural network based on the Self-Attention technique to obtain the Attention Score, it has the advantage of being able to check the change in importance according to the state of each cell or the amount of gene expression.
  • the gene-level network 110 and the pathway-level network 120 of the present invention use self-attention-based artificial intelligence neural network networks.
  • the gene-level network 110 receives the gene expression level 10 and the drug descriptor 20 of the sample and outputs pathway activity based on self-attention, and the pathway level (Pathway- level) the network 120 receives the pathway activity as an input and outputs the pathway activity multiplied by the weight.
  • the reactivity prediction network 140 receives the pathway activity multiplied by the weight from the pathway level network 120 and the drug network 130 and the drug descriptor multiplied by the weight, and outputs an IC50 value 30 .
  • IC50 refers to the amount of drug that can eliminate half (50%) of cancer cells.
  • the drug descriptor uses the Morgan Fingerprint.
  • this self-attention-based network it is possible to specify a gene or pathway that has a high attention score, that is, a target gene or pathway for a drug, rather than simply how effective a drug is on which cancer cell. That is, unlike the prior art, an interpretable network can be used.
  • FIG. 2 is a schematic structural diagram of a gene level network according to a preferred embodiment of the present invention.
  • the gene level network 110 receives the gene expression level 10 and the drug descriptor 20 of the sample as inputs and outputs the pathway activity 12 based on self-attention.
  • the network output (u ij ) is obtained as an output.
  • the attention score ( ⁇ ij ) can be obtained as follows.
  • This attention score ( ⁇ ij ) is multiplied by the gene expression level (g ij ) again to obtain the pathway activity (12, p i ) for each pathway as follows.
  • FIG. 3 is a schematic structural diagram of a pathway level network according to a preferred embodiment of the present invention.
  • pathway activity 14 With the previously obtained pathway activity 12 as an input, the pathway activity 14 multiplied by a weight based on self-attention is obtained.
  • the pathway activity 12 and the drug descriptor 20 are input to the network, and the network output value u i is obtained as follows.
  • the attention score ( ⁇ i ) can be obtained, which is expressed by the following equation.
  • the gene level network 110 and the pathway level network 120 do not simply pass the input value through the artificial intelligence network, but continuously reflect the importance to the network, so that the network changes depending on the cell or the state of the cell and can be applied. can have an effect.
  • FIG. 4 is a schematic structural diagram of a drug network according to a preferred embodiment of the present invention.
  • the drug network 130 outputs the drug descriptor 22 passing the drug descriptor 20 through a general artificial neural network.
  • a deep neural network (DNN) or the like may be used as the artificial neural network.
  • the drug descriptors 22 and c that have passed through the drug network 130 may be expressed by the following equation.
  • FIG. 5 is a schematic structural diagram of a reactive prediction network according to a preferred embodiment of the present invention.
  • the drug response value (r) can be expressed by the following formula.
  • FIG. 6 is a graph showing the drug reactivity results predicted by the self-attention-based drug reactivity prediction device according to a preferred embodiment of the present invention.
  • 6a shows a comparison of predicted values and observed values for a case where both the cell line and the drug are unknown (Unseen Pair), and b is the cell line when the cell line is unknown. It shows the comparison of the predicted value and the observed value at the time (Unseen Cell Line). It shows that when the IC50 is predicted based on self-interest, an improved result is obtained compared to the prior art.
  • FIG. 7 is a flowchart of a method for predicting self-attention-based drug reactivity according to another preferred embodiment of the present invention.
  • the self-attention-based drug reactivity prediction method according to the present invention may be performed by a controller including one or more processors and a memory.
  • Pathway activity is first calculated by the network learned using a database such as GDSC (S10).
  • the gene expression level of the sample and the drug descriptor are passed through the network as inputs, the attention score is calculated based on the output value, and then it is multiplied by the gene expression level to calculate the pathway activity.
  • pathway activity If the pathway activity is obtained, it is again input to the network together with the drug descriptor to obtain an output value, and by multiplying the attention score by the output value by the pathway activity, a pathway feature that is a weighted pathway activity can be obtained (S20).
  • the drug descriptor is calculated by a method such as Morgan fingerprint, and the output is obtained through an artificial neural network such as DNN (S30). can be (S40).
  • the self-attention-based drug response prediction device and method as described above, it is possible to predict more accurately drug response according to the cell state or gene expression level by constructing a network using the attention score that reflects importance rather than a fixed network weight. It works.
  • the self-attention-based drug reactivity prediction device is a drug reactivity prediction technology using an artificial neural network and can be applied to medicine and medical fields.

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Abstract

La présente invention concerne un dispositif et un procédé de prédiction de sensibilité à un médicament de type" in silico " utilisant un réseau d'intelligence artificielle hiérarchique. Selon la présente invention, par prédiction de la sensibilité à un médicament à l'aide d'un réseau d'intelligence artificielle à base d'auto-attention, des changements d'état cellulaire tels que des changements du niveau d'expression génique peuvent être réfléchis vers le réseau, et ainsi, il est possible de prédire avec plus de précision une réponse à un médicament.
PCT/KR2021/017761 2020-12-01 2021-11-29 Dispositif de prédiction de réponse à un médicament à l'aide d'un réseau hiérarchique à base d'auto-attention et procédé associé WO2022119256A1 (fr)

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Citations (2)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
KR20180022537A (ko) * 2016-08-23 2018-03-06 주식회사 스탠다임 기계 학습 앙상블 모델을 이용한 조합 약물의 효과 예측 방법
KR101964694B1 (ko) * 2017-03-28 2019-08-07 가천대학교 산학협력단 약물의 유사도 판단장치, 방법, 및 컴퓨터-판독가능매체

Patent Citations (2)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
KR20180022537A (ko) * 2016-08-23 2018-03-06 주식회사 스탠다임 기계 학습 앙상블 모델을 이용한 조합 약물의 효과 예측 방법
KR101964694B1 (ko) * 2017-03-28 2019-08-07 가천대학교 산학협력단 약물의 유사도 판단장치, 방법, 및 컴퓨터-판독가능매체

Non-Patent Citations (3)

* Cited by examiner, † Cited by third party
Title
ALI OSKOOEI, JANNIS BORN, MATTEO MANICA, VIGNESHWARI SUBRAMANIAN, JULIO SÁEZ-RODRÍGUEZ, MARÍA RODRÍGUEZ MARTÍNEZ: "PaccMann: Prediction of anticancer compound sensitivity with multi-modal attention-based neural networks", ARXIV.ORG, CORNELL UNIVERSITY LIBRARY, 201 OLIN LIBRARY CORNELL UNIVERSITY ITHACA, NY 14853, 14 July 2019 (2019-07-14), 201 Olin Library Cornell University Ithaca, NY 14853 , XP081435937 *
WANG XUEWEI, SUN ZHIFU, ZIMMERMANN MICHAEL T., BUGRIM ANDREJ, KOCHER JEAN-PIERRE: "Predict drug sensitivity of cancer cells with pathway activity inference", BMC MEDICAL GENOMICS, BIOMED CENTRAL LTD, LONDON UK, vol. 12, no. S1, 1 January 2019 (2019-01-01), London UK , pages 15, XP055937356, ISSN: 1755-8794, DOI: 10.1186/s12920-018-0449-4 *
YANG MI, SIMM JAAK, LAM CHI CHUNG, ZAKERI POOYA, VAN WESTEN GERARD J. P., MOREAU YVES, SAEZ-RODRIGUEZ JULIO: "Linking drug target and pathway activation for effective therapy using multi-task learning", SCIENTIFIC REPORTS, NATURE PUBLISHING GROUP, US, vol. 8, no. 1, 1 December 2018 (2018-12-01), US , pages 8322, XP055937354, ISSN: 2045-2322, DOI: 10.1038/s41598-018-25947-y *

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