druGAN: An Advanced Generative Adversarial Autoencoder Model for de Novo Generation of New Molecules with Desired Molecular Properties in Silico
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...
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| Published in: | Molecular pharmaceutics Vol. 14; no. 9; p. 3098 |
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| Main Authors: | , , , , |
| Format: | Journal Article |
| Language: | English |
| Published: |
United States
05.09.2017
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| Subjects: | |
| ISSN: | 1543-8392, 1543-8392 |
| Online Access: | Get more information |
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| 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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| AbstractList | 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. 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.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. |
| Author | Zhavoronkov, Alex Khrabrov, Kuzma Kadurin, Artur Aliper, Alex Nikolenko, Sergey |
| Author_xml | – sequence: 1 givenname: Artur surname: Kadurin fullname: Kadurin, Artur organization: Kazan Federal University , Kazan, Republic of Tatarstan 420008, Russia – sequence: 2 givenname: Sergey surname: Nikolenko fullname: Nikolenko, Sergey organization: Kazan Federal University , Kazan, Republic of Tatarstan 420008, Russia – sequence: 3 givenname: Kuzma surname: Khrabrov fullname: Khrabrov, Kuzma organization: Search Department, Mail.Ru Group Ltd. , Moscow 125167, Russia – sequence: 4 givenname: Alex surname: Aliper fullname: Aliper, Alex organization: Pharmaceutical Artificial Intelligence Department, Insilico Medicine, Inc., Emerging Technology Centers, Johns Hopkins University at Eastern , Baltimore, Maryland 21218, United States – sequence: 5 givenname: Alex orcidid: 0000-0001-7067-8966 surname: Zhavoronkov fullname: Zhavoronkov, Alex organization: Moscow Institute of Physics and Technology , Dolgoprudny 141701, Russia |
| BackLink | https://www.ncbi.nlm.nih.gov/pubmed/28703000$$D View this record in MEDLINE/PubMed |
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| Title | druGAN: An Advanced Generative Adversarial Autoencoder Model for de Novo Generation of New Molecules with Desired Molecular Properties in Silico |
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