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Learning-to-rank for ligand-based virtual screening

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Masahito Ohue
Masahito Ohue

AHeDD2019/IPAB2019 Joint Symposium Tonomachi King Skyfront Area, Kawasaki City, Japan Nov 27-29, 2019

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Learning-to-rank for ligand-based virtual screening Masahito Ohue, Ph.D. Tokyo Institute of Technology, Japan 1 ohue@c.titech.ac.jp AHeDD2019/IPAB2019 Joint Symposium Nov 29, 2019 @Tonomachi King Skyfront, Kawasaki, Japan
Virtual screening 2 ⋮ ⋮
Virtual screening with machine learning 3 𝒄1 𝒄2 𝒄3 𝑓 𝒄2 ≻ 𝒄3 ≻ 𝒄1 𝑓 𝑓 𝑓( ) > 𝑓( ) > 𝑓( )
Learning-to-rank ≻ ≻ ≻ 𝑓 𝑓𝑓 4
SVM vs. RankSVM 5 𝑥1 ≻ 𝑥2 ≻ 𝑥3 ≻ 𝑥4
RankSVM optimization 𝑥1 ≻ 𝑥2 ≻ 𝑥3 ≻ 𝑥4 𝐰 𝛿(1) 𝛿(2) 𝐰1 𝐰2 >𝛿min (1) 𝛿min 2 𝐰1 𝐰2 6
Two proposed method for learning-to-rank-based LBVS 7 Suzuki SD, Ohue M, Akiyama Y. Artificial Life and Robotics, 23(2): 205-212, 2018. Ohue M, Suzuki SD, Akiyama Y. J Mol Graph Model, 92:192-200, 2019. “Ignore the meaningless order” “Integrate different experimental data” without wet data other wet data
Ignoring meaningless order 8 Ohue M, Suzuki SD, Akiyama Y. J Mol Graph Model, 92:192-200, 2019.
Integrating different experimental data 9 𝐩1 𝐩2 𝐜1 𝐜2 𝐜3 𝐜4 𝐩1 𝐩2 𝐩3 𝐩1 𝐩2 𝐩3 𝐩1 𝐩2 𝐩3 𝐩1 𝐩2 𝐩3 𝐜1 𝐜2 𝐜3 𝐜4 𝐜5 𝐜6 𝐜1 𝐜2 𝐜3 𝐜4 𝐜5 𝐜6 𝐩3 𝐜5 𝐜6 𝐜1 𝐜2 𝐜3 𝐜4 𝐜5 𝐜6 𝐜1 𝐜2 𝐜3 𝐜4 𝐜5 𝐜6 𝑘 𝐜, 𝐩 , 𝐜′ , 𝐩′ = 𝑘com 𝐜, 𝐜′ × 𝑘pro 𝐩, 𝐩′ Jacob L & Vert JP, Bioinformatics, 24:2149, 2008
Evaluation 10
PKRank prediction on BindingDB data 𝑘com 𝑘pro ( ): significantly improvement (paired t-test, P < 0.05) 11 (Zhang Wei, et al. J Cheminform, 7, 2015) Suzuki SD, Ohue M, Akiyama Y. Artificial Life and Robotics, 23(2): 205-212, 2018.
Summary 12 ≻ ≻ ≻ 𝑓 𝑓𝑓 ⋮ ⋮ Ohue M, Suzuki SD, Akiyama Y. J Mol Graph Model, 92:192-200, 2019. Suzuki SD, Ohue M, Akiyama Y. Artificial Life and Robotics, 23(2): 205-212, 2018.

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Learning-to-rank for ligand-based virtual screening

  • 1. Learning-to-rank for ligand-based virtual screening Masahito Ohue, Ph.D. Tokyo Institute of Technology, Japan 1 ohue@c.titech.ac.jp AHeDD2019/IPAB2019 Joint Symposium Nov 29, 2019 @Tonomachi King Skyfront, Kawasaki, Japan
  • 3. Virtual screening with machine learning 3 𝒄1 𝒄2 𝒄3 𝑓 𝒄2 ≻ 𝒄3 ≻ 𝒄1 𝑓 𝑓 𝑓( ) > 𝑓( ) > 𝑓( )
  • 5. SVM vs. RankSVM 5 𝑥1 ≻ 𝑥2 ≻ 𝑥3 ≻ 𝑥4
  • 6. RankSVM optimization 𝑥1 ≻ 𝑥2 ≻ 𝑥3 ≻ 𝑥4 𝐰 𝛿(1) 𝛿(2) 𝐰1 𝐰2 >𝛿min (1) 𝛿min 2 𝐰1 𝐰2 6
  • 7. Two proposed method for learning-to-rank-based LBVS 7 Suzuki SD, Ohue M, Akiyama Y. Artificial Life and Robotics, 23(2): 205-212, 2018. Ohue M, Suzuki SD, Akiyama Y. J Mol Graph Model, 92:192-200, 2019. “Ignore the meaningless order” “Integrate different experimental data” without wet data other wet data
  • 8. Ignoring meaningless order 8 Ohue M, Suzuki SD, Akiyama Y. J Mol Graph Model, 92:192-200, 2019.
  • 9. Integrating different experimental data 9 𝐩1 𝐩2 𝐜1 𝐜2 𝐜3 𝐜4 𝐩1 𝐩2 𝐩3 𝐩1 𝐩2 𝐩3 𝐩1 𝐩2 𝐩3 𝐩1 𝐩2 𝐩3 𝐜1 𝐜2 𝐜3 𝐜4 𝐜5 𝐜6 𝐜1 𝐜2 𝐜3 𝐜4 𝐜5 𝐜6 𝐩3 𝐜5 𝐜6 𝐜1 𝐜2 𝐜3 𝐜4 𝐜5 𝐜6 𝐜1 𝐜2 𝐜3 𝐜4 𝐜5 𝐜6 𝑘 𝐜, 𝐩 , 𝐜′ , 𝐩′ = 𝑘com 𝐜, 𝐜′ × 𝑘pro 𝐩, 𝐩′ Jacob L & Vert JP, Bioinformatics, 24:2149, 2008
  • 11. PKRank prediction on BindingDB data 𝑘com 𝑘pro ( ): significantly improvement (paired t-test, P < 0.05) 11 (Zhang Wei, et al. J Cheminform, 7, 2015) Suzuki SD, Ohue M, Akiyama Y. Artificial Life and Robotics, 23(2): 205-212, 2018.
  • 12. Summary 12 ≻ ≻ ≻ 𝑓 𝑓𝑓 ⋮ ⋮ Ohue M, Suzuki SD, Akiyama Y. J Mol Graph Model, 92:192-200, 2019. Suzuki SD, Ohue M, Akiyama Y. Artificial Life and Robotics, 23(2): 205-212, 2018.