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Kadurin A, Nikolenko S, Khrabrov K, Aliper A, Zhavoronkov A. druGAN: An Advanced Generative Adversarial Autoencoder Model for de Novo Generation of New Molecules with Desired Molecular Properties in Silico. Mol Pharm 2017; 14:3098-3104. [PMID: 28703000 DOI: 10.1021/acs.molpharmaceut.7b00346] [Citation(s) in RCA: 226] [Impact Index Per Article: 32.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/20/2022]
Abstract
Deep generative adversarial networks (GANs) are the emerging technology in drug discovery and biomarker development. In our recent work, we demonstrated a proof-of-concept of implementing deep generative adversarial autoencoder (AAE) to identify new molecular fingerprints with predefined anticancer properties. Another popular generative model is the variational autoencoder (VAE), which is based on deep neural architectures. In this work, we developed an advanced AAE model for molecular feature extraction problems, and demonstrated its advantages compared to VAE in terms of (a) adjustability in generating molecular fingerprints; (b) capacity of processing very large molecular data sets; and (c) efficiency in unsupervised pretraining for regression model. Our results suggest that the proposed AAE model significantly enhances the capacity and efficiency of development of the new molecules with specific anticancer properties using the deep generative models.
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Affiliation(s)
- Artur Kadurin
- Pharmaceutical Artificial Intelligence Department, Insilico Medicine, Inc., Emerging Technology Centers, Johns Hopkins University at Eastern , Baltimore, Maryland 21218, United States.,Steklov Mathematical Institute at St. Petersburg , St. Petersburg 191023, Russia.,Kazan Federal University , Kazan, Republic of Tatarstan 420008, Russia
| | - Sergey Nikolenko
- National Research University Higher School of Economics , St. Petersburg 190008, Russia.,Steklov Mathematical Institute at St. Petersburg , St. Petersburg 191023, Russia.,Kazan Federal University , Kazan, Republic of Tatarstan 420008, Russia
| | - Kuzma Khrabrov
- Search Department, Mail.Ru Group Ltd. , Moscow 125167, Russia
| | - Alex Aliper
- Pharmaceutical Artificial Intelligence Department, Insilico Medicine, Inc., Emerging Technology Centers, Johns Hopkins University at Eastern , Baltimore, Maryland 21218, United States
| | - Alex Zhavoronkov
- Pharmaceutical Artificial Intelligence Department, Insilico Medicine, Inc., Emerging Technology Centers, Johns Hopkins University at Eastern , Baltimore, Maryland 21218, United States.,The Biogerontology Research Foundation , Trevissome Park, Truro TR4 8UN, U.K.,Moscow Institute of Physics and Technology , Dolgoprudny 141701, Russia
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Kadurin A, Aliper A, Kazennov A, Mamoshina P, Vanhaelen Q, Khrabrov K, Zhavoronkov A. The cornucopia of meaningful leads: Applying deep adversarial autoencoders for new molecule development in oncology. Oncotarget 2017; 8:10883-10890. [PMID: 28029644 PMCID: PMC5355231 DOI: 10.18632/oncotarget.14073] [Citation(s) in RCA: 147] [Impact Index Per Article: 21.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/14/2016] [Accepted: 11/24/2016] [Indexed: 12/19/2022] Open
Abstract
Recent advances in deep learning and specifically in generative adversarial networks have demonstrated surprising results in generating new images and videos upon request even using natural language as input. In this paper we present the first application of generative adversarial autoencoders (AAE) for generating novel molecular fingerprints with a defined set of parameters. We developed a 7-layer AAE architecture with the latent middle layer serving as a discriminator. As an input and output the AAE uses a vector of binary fingerprints and concentration of the molecule. In the latent layer we also introduced a neuron responsible for growth inhibition percentage, which when negative indicates the reduction in the number of tumor cells after the treatment. To train the AAE we used the NCI-60 cell line assay data for 6252 compounds profiled on MCF-7 cell line. The output of the AAE was used to screen 72 million compounds in PubChem and select candidate molecules with potential anti-cancer properties. This approach is a proof of concept of an artificially-intelligent drug discovery engine, where AAEs are used to generate new molecular fingerprints with the desired molecular properties.
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Affiliation(s)
- Artur Kadurin
- Search Department, Mail.Ru Group Ltd., Moscow, Russia.,Pharmaceutical Artificial Intelligence Department, Insilico Medicine, Inc., Emerging Technology Centers, Johns Hopkins University at Eastern, Baltimore, Maryland, USA.,Big Data and Text Analysis Laboratory, Kazan Federal University, Kazan, Republic of Tatarstan, Russia.,St. Petersburg Department of V.A. Steklov Institute of Mathematics of the Russian Academy of Sciences, Petersburg, Russia
| | - Alexander Aliper
- Pharmaceutical Artificial Intelligence Department, Insilico Medicine, Inc., Emerging Technology Centers, Johns Hopkins University at Eastern, Baltimore, Maryland, USA
| | - Andrey Kazennov
- Pharmaceutical Artificial Intelligence Department, Insilico Medicine, Inc., Emerging Technology Centers, Johns Hopkins University at Eastern, Baltimore, Maryland, USA.,Moscow Institute of Physics and Technology, Dolgoprudny, Russia
| | - Polina Mamoshina
- Pharmaceutical Artificial Intelligence Department, Insilico Medicine, Inc., Emerging Technology Centers, Johns Hopkins University at Eastern, Baltimore, Maryland, USA.,Department of Computer Science, University of Oxford, Oxford, UK
| | - Quentin Vanhaelen
- Pharmaceutical Artificial Intelligence Department, Insilico Medicine, Inc., Emerging Technology Centers, Johns Hopkins University at Eastern, Baltimore, Maryland, USA
| | | | - Alex Zhavoronkov
- Pharmaceutical Artificial Intelligence Department, Insilico Medicine, Inc., Emerging Technology Centers, Johns Hopkins University at Eastern, Baltimore, Maryland, USA.,The Biogerontology Research Foundation, Trevissome Park, Truro TR4 8UN, UK.,Moscow Institute of Physics and Technology, Dolgoprudny, Russia
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