Designing natural product-like virtual libraries using deep molecule generative models

doi:10.5246/jcps.2018.07.046

Journal of Chinese Pharmaceutical Sciences ›› 2018, Vol. 27 ›› Issue (7): 451-459.DOI: 10.5246/jcps.2018.07.046

• Original articles • Next Articles

Designing natural product-like virtual libraries using deep molecule generative models

Yibo Li, Xin Zhou, Zhenming Liu^*, Liangren Zhang^*

State Key Laboratory of Natural and Biomimetic Drugs, School of Pharmaceutical Sciences, Peking University Health Science Center, Beijing 100191, China

Received:2018-05-06 Revised:2018-06-10 Online:2018-07-25 Published:2018-06-25
Contact: Tel.: +86-010-82802567; +86-010-82805514, E-mail: zmliu@bjmu.edu.cn; liangren@bjmu.edu.cn
Supported by:
The National Natural Science Foundation of China (Grant No. 81573273, 81673279, 21572010 and 21772005) as well as National Major Scientific and Technological Special Project for “Significant New Drugs Development” (Grant No. 2018ZX09735001-003).

Abstract

Abstract:

Natural products (NPs) have long been recognized as a valuable resource for drug discovery, and bringing NP-related features to virtual libraries is believed to be an effective way to increase the coverage of druggable chemical space. Here, deep learning-based molecule generative model, which is a recent technique in de novo molecule design, was applied to generate virtual libraries with NP-like properties. Results demonstrated that the model was effective in generating molecules that highly resemble NPs. Moreover, the model was also found to be capable of generating NP-like molecules that were also easy to synthesize, significantly increasing the practical value of the compound library.

Key words: Natural product, Deep learning, Generative model, Virtual library design

CLC Number:

R916

Supporting:

Yibo Li, Xin Zhou, Zhenming Liu, Liangren Zhang. Designing natural product-like virtual libraries using deep molecule generative models[J]. Journal of Chinese Pharmaceutical Sciences, 2018, 27(7): 451-459.

References

[1] Newman, D.J.; Cragg, G.M. Natural Products as Sources of New Drugs from 1981 to 2014. J. Nat. Prod. 2016, 79, 629-661.

[2] Drewry, D.H.; Macarron, R. Enhancements of screening collections to address areas of unmet medical need: an industry perspective. Curr. Opin. Chem. Biol. 2010, 14, 289-298.

[3] Harvey, A.L.; Edradaebel, R.; Quinn, R.J. The re-emergence of natural products for drug discovery in the genomics era. Nat. Rev. Drug Discov. 2015, 14, 111-129.

[4] Ertl, P.; Roggo, S.; Schuffenhauer, A. Natural Product-Likeness Score and Its Application for Prioritization of Compound Libraries. J. Chem. Inf. Model. 2008, 48, 68-74.

[5] Yu, M.J. Natural Product-Like Virtual Libraries: Recursive Atom-Based Enumeration. J. Chem. Inf. Model. 2011, 51, 541-557.

[6] Tian, S.; Wang, J.; Li, Y.; Li, D.; Xu, L.; Hou, T. The application of in silico drug-likeness predictions in pharmaceutical research. Adv. Drug Deliv. Rev. 2015, 86, 2-10.

[7] Shang, J.; Sun, H.; Liu, H.; Chen, F.; Tian, S.; Pan, P.; Li, D.; Kong, D.; Hou, T. Comparative analyses of structural features and scaffold diversity for purchasable compound libraries. J. Cheminf. 2017, 9, 25.

[8] Shen, M.; Tian, S.; Li, Y.; Li, Q.; Xu, X.; Wang, J.; Hou, T. Drug-likeness analysis of traditional Chinese medicines: 1. property distributions of drug-like compounds, non-drug-like compounds and natural compounds from traditional Chinese medicines. J. Cheminf. 2012, 4, 1-13.

[9] Tian, S.; Li, Y.; Wang, J.; Xu, X.; Xu, L.; Wang, X.; Chen, L.; Hou, T. Drug-likeness analysis of traditional Chinese medicines: 2. Characterization of scaffold architectures for drug-like compounds, non-drug-like compounds, and natural compounds from traditional Chinese medicines. J. Cheminf. 2013, 5, 1-14.

[10] Tian, S.; Wang, J.; Li, Y.; Xu, X.; Hou, T. Drug-likeness Analysis of Traditional Chinese Medicines: Prediction of Drug-likeness Using Machine Learning Approaches. Mol. Pharm. 2012, 9, 2875-2886.

[11] Lavecchia, A.; Di Giovanni, C. Virtual screening strategies in drug discovery: a critical review. Curr. Med. Chem. 2013, 20, 2839-2860.

[12] Segler, M.H.S.; Kogej, T.; Tyrchan, C.; Waller, M.P. Generating Focused Molecule Libraries for Drug Discovery with Recurrent Neural Networks. ACS Cent. Sci. 2018, 4, 120-131.

[13] Olivecrona, M.; Blaschke, T.; Engkvist, O.; Chen, H. Molecular de-novo design through deep reinforcement learning. J. Cheminf. 2017, 9, 48.

[14] Gómez-Bombarelli, R.; Duvenaud, D.; Hernández-Lobato, J.M.; Aguilera-Iparraguirre, J.; Hirzel, T.D.; Adams, R.P.; Aspuru-Guzik, A. Automatic chemical design using a data-driven continuous representation of molecules. arXiv:1610.02415v1 2016.

[15] Simonovsky, M.; Komodakis, N. GraphVAE: Towards Generation of Small Graphs Using Variational Autoencoders. arXiv:1610.02920 2017.

[16] Li, Y.; Vinyals, O.; Dyer, C.; Pascanu, R.; Battaglia, P. In Learning Deep Generative Models of Graphs, 6th International Conference on Learning Representations, 2017.

[17] Li, Y.; Zhang, L.; Liu, Z. Multi-Objective De Novo Drug Design with Conditional Graph Generative Model. arXiv:1801.07299 2018.

[18] Gaulton, A.; Bellis, L. J.; Bento, A.P.; Chambers, J.; Davies, M.; Hersey, A.; Light, Y.; McGlinchey, S.; Michalovich, D.; Al-Lazikani, B. ChEMBL: a large-scale bioactivity database for drug discovery. Nucleic Acids Res. 2011, 40, D1100-D1107.

[19] RDKit: open source cheminformatics. http://www.rdkit.org/.

[20] Chen, T.; Li, M.; Li, Y.; Lin, M.; Wang, N.; Wang, M.; Xiao, T.; Xu, B.; Zhang, C.; Zhang, Z. MXNet: A Flexible and Efficient Machine Learning Library for Heterogeneous Distributed Systems. arXiv:1512.01274 2015.

[21] Blaschke, T.; Olivecrona, M.; Engkvist, O.; Bajorath, J.; Chen, H. Application of generative autoencoder in de novo molecular design. Mol. Inform. 2017, 37, 1-2.

[22] Brown, N.; Mckay, B.; Gilardoni, F.; Gasteiger, J. A graph-based genetic algorithm and its application to the multiobjective evolution of median molecules. J. Chem. Inf. Comput. Sci. 2004, 35, 1079-1087.

[23] Ertl, P.; Schuffenhauer, A. Estimation of synthetic accessibility score of drug-like molecules based on molecular complexity and fragment contributions. J. Cheminf. 2009, 1, 8.
[24] Zhou, X.; Li, Y.; Lv, C.; Liu, Z.; Zhang, L. Cheminformatics analysis of natural products and indication distribution prediction. J. Chin. Pharm. Sci. 2017, 26, 635-641.

Designing natural product-like virtual libraries using deep molecule generative models

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