druGAN: An Advanced Generative Adversarial Autoencoder Model for de Novo Generation of New Molecules with Desired Molecular Properties in Silico.

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 Pharm

journal_title

Molecular pharmaceutics

authors

Kadurin A,Nikolenko S,Khrabrov K,Aliper A,Zhavoronkov A

doi

10.1021/acs.molpharmaceut.7b00346

subject

Has Abstract

pub_date

2017-09-05 00:00:00

pages

3098-3104

issue

9

eissn

1543-8384

issn

1543-8392

journal_volume

14

pub_type

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