The document discusses a novel approach to exploring chemical space using continuous encodings of molecules through variational autoencoders (VAEs). It highlights the integration of data-driven techniques and gradient-based optimization methods for drug and material design, leveraging large databases of drug-like molecules. The approach shows promise in generating new molecules with desirable properties while optimizing various objective functions in molecular design.

1. , SDS AI Lab.
2019. 6. 23.
PR-173

2. 1. Research Background

3. 1. Research Background
‘Chemical space’
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• 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
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• 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
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Keywords: Chemical Design, Data-Driven, Continuous, VAE

8. 2. Methods

9. 2. Methods
Model training
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- Database (structure, property)
- Molecular descriptor
- Model structure & hyperparameter

10. 2. Methods
Database
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- 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
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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
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• 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
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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

14. 3. Experimental Results

15. 3. Experimental Results 12/19
1)
2)
3)

16. 3. Experimental Results
1) Mapping molecules to the latent space
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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
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“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.
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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
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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.
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• 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.