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Graph Convolutional Network の化合物への応用

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京都大学x理研AIPxエクサウィザーズ 機械学習勉強会1で発表したスライドです

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Graph Convolutional Network の化合物への応用

  1. 1. 2018 7 19 Graph Convolutional Network
  2. 2. 2 © ExaWizards Inc. All rights reserved.
  3. 3. 3 © ExaWizards Inc. All rights reserved. Graphs • • SNS • • • …
  4. 4. 4 © ExaWizards Inc. All rights reserved. Paul, S. M. et al. How to improve R&D productivity: the pharmaceutical industry’s grand challenge. Nat. Rev. Drug Discov. 9, 203–14 (2010). Million $
  5. 5. 5 © ExaWizards Inc. All rights reserved. • • • • • • …
  6. 6. 6 © ExaWizards Inc. All rights reserved. Graph Convolutional Network (GCN) GCN (Kipf et al., 2016; Hamilton et al., 2018., etc) (Altae-Tran H. et al., 2017)
  7. 7. 7.pptx© ExaWizards Inc. All rights reserved. 7 © ExaIntelligence Inc. All rights reserved. 7_v0.1.pptx. . . 7 G r a p h C o n v R e l u B a t c h N o r m D e n s e R e l u G a t h e r t a n h D e n s e & σ ) ( 0-1 x 2 or 3
  8. 8. 8 © ExaWizards Inc. All rights reserved. solubility /9 • ( ) 6 61 n Tested Substances: u All 57,859 u Active 40,286 u Inactive 17,573 train:valid:test = 8:1:1 False positive True positive roc-auc = 0.88
  9. 9. 9 © ExaWizards Inc. All rights reserved. gpt X riY foreS tqt c s n tbU p a r c pSx s od c y p h T il 0LCL L 9 9 9 9 , CL 9 2A A 9 Q. 9 A L 9 L9 R 9 AM ( ( )M 63/ G 9 NAM 9: ( ( )M • s ir r X. 1s Y • t inS T • “t” tvrykS “ t t” Ø X ~ ouS “s j ” tvt jY
  10. 10. 10 © ExaWizards Inc. All rights reserved.
  11. 11. 11 © ExaWizards Inc. All rights reserved. GCN x variational autoencoder • • Graph autoencoder (Kipf and Welling, 2016) Encoder: Z = g(GCN(X, A)) Decoder : A’ = σ(Z • ZT ) <-
  12. 12. 12.pptx© ExaWizards Inc. All rights reserved. 12 © ExaIntelligence Inc. All rights reserved. 12_v0.1.pptx© ExaWizards Inc. All rights reserved. 12 c1csc(CC2CCCC2N2CO2)c1 Cc1csc(C2ON2C2CCCC2)c1 CCCCCNC(=O)c1ccc(C)s1 CC[SH]C(CC)C1ON1C1CCCC1 Cc1ccc(CNC2CCCC2)s1OCc1ccc(C2ON2C(C)C2CC2)s1 Cc1ccc(C(=O)NC2CCCC2)s1 CCC(CC)NC(=O)c1ccc(C)s1
  13. 13. 13.pptx© ExaWizards Inc. All rights reserved. 13 © ExaIntelligence Inc. All rights reserved. 13_v0.1.pptx© ExaWizards Inc. All rights reserved. 13 Source dataset samples logP Tanimoto QED GC VAE ZINC 19,034 2.91 (0.68) 0.64 (0.19) 0.76 (0.07) GC VAE PubChem 11,994 3.22 (1.01) 0.72 (0.14) 0.64 (0.09) GC VAE Emolecules 12,390 3.01 (0.93) 0.87 (0.19) 0.66 (0.10) GC VAE QM9 373 2.21 (0.31) 0.72 (0.14) 0.53 (0.02) 1. Our autoencoder that was trained on 250000 molecules from ZINC encodes approximately 7.5 million molecules. 2. We collected the set of all molecules generated from 400 decoding attempts from the latent space points encoded from the same 1000 seed molecules. Experiments’ Explanation from Molecular Autoencoder paper 2017 Our Model Gomez- Bombadelli et al., 2018
  14. 14. 14 © ExaWizards Inc. All rights reserved. GCN • • •

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