Journal
MOLECULAR PHARMACEUTICS
Volume 14, Issue 9, Pages 3098-3104Publisher
AMER CHEMICAL SOC
DOI: 10.1021/acs.molpharmaceut.7b00346
Keywords
adversarial autoencoder; deep learning; drug discovery; variational autoencoder; generative adversarial network
Funding
- Government of the Russian Federation [14.Z50.31.0030]
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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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