Uploaded byMasahito Ohue
Learning-to-rank for ligand-based virtual screening
The document discusses the application of learning-to-rank methods for ligand-based virtual screening in drug discovery. It contrasts svm and rank-svm optimization techniques while proposing two new methods for enhancing ligand-based virtual screening results. Additionally, it highlights the integration of diverse experimental data to improve prediction accuracy and minimize irrelevant ordering.
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Learning-to-rank for ligand-based virtual screening
- 1. Learning-to-rank for ligand-based virtualscreening 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
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- 3. Virtual screening withmachine learning 3 𝒄1 𝒄2 𝒄3 𝑓 𝒄2 ≻ 𝒄3 ≻ 𝒄1 𝑓 𝑓 𝑓( ) > 𝑓( ) > 𝑓( )
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- 5. SVM vs. RankSVM5 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 methodfor 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 order8 Ohue M, Suzuki SD, Akiyama Y. J Mol Graph Model, 92:192-200, 2019.
- 9. Integrating different experimentaldata 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
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- 11. PKRank prediction onBindingDB 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.