title: The cornucopia of meaningful leads: Applying deep adversarial autoencoders for new molecule development in oncology.
abstract: 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.
authors.affiliation: Search Department, Mail.Ru Group Ltd., Moscow, Russia.|Pharmaceutical Artificial Intelligence Department, Insilico Medicine, Inc., Emerging Technology Centers, Johns Hopkins University at Eastern, Baltimore, Maryland, USA.|Big Data and Text Analysis Laboratory, Kazan Federal University, Kazan, Republic of Tatarstan, Russia.|St. Petersburg Department of V.A. Steklov Institute of Mathematics of the Russian Academy of Sciences, Petersburg, Russia.
authors.forename: Artur
authors.initials: A
authors.lastname: Kadurin
authors.affiliation: Pharmaceutical Artificial Intelligence Department, Insilico Medicine, Inc., Emerging Technology Centers, Johns Hopkins University at Eastern, Baltimore, Maryland, USA.
authors.forename: Alexander
authors.initials: A
authors.lastname: Aliper
authors.affiliation: Pharmaceutical Artificial Intelligence Department, Insilico Medicine, Inc., Emerging Technology Centers, Johns Hopkins University at Eastern, Baltimore, Maryland, USA.|Moscow Institute of Physics and Technology, Dolgoprudny, Russia.
authors.forename: Andrey
authors.initials: A
authors.lastname: Kazennov
authors.affiliation: Pharmaceutical Artificial Intelligence Department, Insilico Medicine, Inc., Emerging Technology Centers, Johns Hopkins University at Eastern, Baltimore, Maryland, USA.|Department of Computer Science, University of Oxford, Oxford, UK.
authors.forename: Polina
authors.initials: P
authors.lastname: Mamoshina
authors.affiliation: Pharmaceutical Artificial Intelligence Department, Insilico Medicine, Inc., Emerging Technology Centers, Johns Hopkins University at Eastern, Baltimore, Maryland, USA.
authors.forename: Quentin
authors.initials: Q
authors.lastname: Vanhaelen
authors.affiliation: Search Department, Mail.Ru Group Ltd., Moscow, Russia.
authors.forename: Kuzma
authors.initials: K
authors.lastname: Khrabrov
authors.affiliation: Pharmaceutical Artificial Intelligence Department, Insilico Medicine, Inc., Emerging Technology Centers, Johns Hopkins University at Eastern, Baltimore, Maryland, USA.|The Biogerontology Research Foundation, Trevissome Park, Truro TR4 8UN, UK.|Moscow Institute of Physics and Technology, Dolgoprudny, Russia.
authors.forename: Alex
authors.initials: A
authors.lastname: Zhavoronkov
mesh_terms: D000970:Antineoplastic Agents / Q000494:pharmacology / Q000627:therapeutic use; D045744:Cell Line, Tumor; D004354:Drug Screening Assays, Antitumor / Q000379:methods*; D004358:Drug Therapy / Q000379:methods; D057166:High-Throughput Screening Assays / Q000379:methods*; D006801:Humans; D020014:K562 Cells; D061986:MCF-7 Cells; D000069550:Machine Learning; D016571:Neural Networks, Computer; D015203:Reproducibility of Results
chemical_list: D000970:Antineoplastic Agents
keywords: adversarial autoencoder; artificial intelligence; deep learning; drug discovery; generative adversarian networks