Research Papers:

The cornucopia of meaningful leads: Applying deep adversarial autoencoders for new molecule development in oncology

Artur Kadurin _, Alexander Aliper, Andrey Kazennov, Polina Mamoshina, Quentin Vanhaelen, Kuzma Khrabrov and Alex Zhavoronkov

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Oncotarget. 2017; 8:10883-10890. https://doi.org/10.18632/oncotarget.14073

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Artur Kadurin1,2,3,4, Alexander Aliper2, Andrey Kazennov2,7, Polina Mamoshina2,5, Quentin Vanhaelen2, Kuzma Khrabrov1, Alex Zhavoronkov2,6,7

1Search Department, Mail.Ru Group Ltd., Moscow, Russia

2Pharmaceutical Artificial Intelligence Department, Insilico Medicine, Inc., Emerging Technology Centers, Johns Hopkins University at Eastern, Baltimore, Maryland, USA

3Big Data and Text Analysis Laboratory, Kazan Federal University, Kazan, Republic of Tatarstan, Russia

4St. Petersburg Department of V.A. Steklov Institute of Mathematics of the Russian Academy of Sciences, Petersburg, Russia

5Department of Computer Science, University of Oxford, Oxford, UK

6The Biogerontology Research Foundation, Trevissome Park, Truro TR4 8UN, UK

7Moscow Institute of Physics and Technology, Dolgoprudny, Russia

Correspondence to:

Alex Zhavoronkov, email: [email protected]

Keywords: generative adversarian networks, adversarial autoencoder, deep learning, drug discovery, artificial intelligence

Received: June 14, 2016     Accepted: November 24, 2016     Published: December 22, 2016


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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