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.
journal_name
Mol Pharmjournal_title
Molecular pharmaceuticsauthors
Kadurin A,Nikolenko S,Khrabrov K,Aliper A,Zhavoronkov Adoi
10.1021/acs.molpharmaceut.7b00346subject
Has Abstractpub_date
2017-09-05 00:00:00pages
3098-3104issue
9eissn
1543-8384issn
1543-8392journal_volume
14pub_type
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