![2. Methods
Model structure – Variational autoencoder
11/19
Decoder: gated recurrent unit (GRU)
Josep Arús-Pous et al., Journal of Cheminformatics. 2018
- into three layers of gated recurrent unit (GRU) networks with hidden dimension of 488.
- Property prediction : fully connected layers [1000, 1000]
• Structural parameters](https://image.slidesharecdn.com/pr173automatic-chemical-designjoo-190623162815/85/PR173-Automatic-Chemical-Design-Using-a-Data-Driven-Continuous-Representation-of-Molecules-13-320.jpg)
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PR173 : Automatic Chemical Design Using a Data-Driven Continuous Representation of Molecules
- 1. , SDS AI Lab. 2019. 6. 23. PR-173
- 3. 1. Research Background ‘Chemical space’ 2/19 • The goal of drug and material design is to identify novel molecules that have certain desirable properties. https://phys.org/news/2018-08-software-framework-drug-discovery-ieee.html
- 4. 1. Research Background 3/19 Virtual screening Genetic algorithm Evolutionary algorithms for de novo drug design – A survey (2015)Combination of Virtual Screening Protocol by in Silico toward the Discovery of Novel 4-Hydroxyphenylpyruvate Dioxygenase Inhibitors (2018)
- 5. 1. Research Background Main idea – continuous representation of molecules 4/19 • Hand-specified mutation rules are unnecessary • We can enable the use of gradient-based optimization to make larger jumps in chemical space. • A data-driven representation can leverage large sets of unlabeled chemical compounds to automatically build an even larger implicit library.
- 6. 1. Research Background Main idea – Variational autoencoder for manifold learning 5/19 https://www.slideshare.net/NaverEngineering/ss-96581209
- 7. • A new for exploring chemical space based on continuous encodings of molecules. 1. Research Background Objective 6/19 Keywords: Chemical Design, Data-Driven, Continuous, VAE
- 8. 2. Methods
- 9. 2. Methods Model training 7/19 - Database (structure, property) - Molecular descriptor - Model structure & hyperparameter
- 10. 2. Methods Database 8/19 - 250,000 drug-like commercially available molecules from ZINC DB https://zinc.docking.org/subsets/drug-like ZINC DB QM9 DB - set of molecules with fewer than 9 heavy atoms - 108,000 molecules was used. http://quantum-machine.org/datasets/
- 11. 2. Methods Molecular descriptor 9/19 https://www.researchgate.net/publication/235919348_manual_for_chemopy/ Yibo Li et al., Journal of Cheminformatics. 2018 SMILES (Simplified molecular-input line-entry system) Molecular fingerprintMolecular graph https://en.wikipedia.org/wiki/Simplified_molecular-input_line-entry_system
- 12. 2. Methods Model structure – Variational autoencoder 10/19 • Structural parameters - Max length: 120 (for ZINC) 34 (for QM9) - Different characters: 35 (for ZINC), 22 (for QM9) - three 1D convolutional layers of filter sizes 9, 9, 10 and 9, 9, 11 convolution kernels - Latent space 196 (for ZINC), 156 (for QM9) Encoder: 1d CNN Yibo Li et al., Journal of Cheminformatics. 2018
- 13. 2. Methods Model structure – Variational autoencoder 11/19 Decoder: gated recurrent unit (GRU) Josep Arús-Pous et al., Journal of Cheminformatics. 2018 - into three layers of gated recurrent unit (GRU) networks with hidden dimension of 488. - Property prediction : fully connected layers [1000, 1000] • Structural parameters
- 15. 3. Experimental Results 12/19 1) 2) 3)
- 16. 3. Experimental Results 1) Mapping molecules to the latent space 13/19 5000 latent points -> 1000 attempts Gaussian noise added to the encoding
- 17. 3. Experimental Results 14/19 Interpolation! ? Distance in the latent space 1) Mapping molecules to the latent space
- 18. 3. Experimental Results A continuous latent space allows interpolation of molecules 15/19 “Interpolating linearly between two points might pass by an area of low probability, to keep the sampling on the areas of high probability we utilize spherical interpolation (slerp).”
- 19. 3. Experimental Results A distribution of chemical properties in training sets against molecules generated. 16/19 Figure 2 in SI • More similar to the original data set. • VAE generates molecules are new as the combinatorial space is extremely large
- 20. 3. Experimental Results The mapping of property values to the latent space representation of molecules 17/19 Encoding -> draw Sampling -> prediction -> draw performance of property prediction model
- 21. 3. Experimental Results 18/19 Gradient-based optimization (Gaussian interpolation) Optimization of Molecules via Properties • Molecule generation from continuous latent space
- 22. 4. Conclusion
- 23. 4. Conclusions • We propose a new family of methods for exploring chemical space based on continuous encodings of molecules. 19/19 • The results and its application to optimizing objective functions of molecular properties, have already and will continue to influence new avenues for molecular design. Thank you.