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. 2017 Feb 14;8(7):10883-10890.
doi: 10.18632/oncotarget.14073.

The Cornucopia of Meaningful Leads: Applying Deep Adversarial Autoencoders for New Molecule Development in Oncology

Free PMC article

The Cornucopia of Meaningful Leads: Applying Deep Adversarial Autoencoders for New Molecule Development in Oncology

Artur Kadurin et al. Oncotarget. .
Free PMC article


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.

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

Conflict of interest statement


The authors are affiliated with Insilico Medicine, Inc, which is applying deep learning techniques, including generative adversarial networks to drug discovery for internal research, providing services to the pharmaceutical companies and identifying novel geroprotectors. The authors have vested interest in demonstrating and popularizing the successful applications of the deep learning techniques for drug discovery and biomarker development.


Figure 1
Figure 1. Architecture of Adversarial Autoencoder (AAE) used in this study
Encoder consists of two consequent layers L1 and L2 with 128 and 64 neurons, respectively. In turn, decoder consists of layers L'1 and L'2 comprising 64 and 128 neurons. Latent layer consists of 5 neurons one of which is Growth Inhibition percentage (GI) and the other 4 are discriminated with normal distribution.
Figure 2
Figure 2. Mapping generated molecules to chemical space of Pubchem
Pubchem compounds are depicted in green, training set is shown in blue and mapped predictions in red.

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