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.

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

Small-molecule drug discovery can be viewed as a challenging multidimensional problem in which various characteristics of compounds - including efficacy, pharmacokinetics and safety - need to be optimized in parallel to provide drug candidates. Recent advances in areas such as microfluidics-assisted chemical synthesis and biological testing, as well as artificial intelligence systems that improve a design hypothesis through feedback analysis, are now providing a basis for the introduction of greater automation into aspects of this process. This could potentially accelerate time frames for compound discovery and optimization and enable more effective searches of chemical space. However, such approaches also raise considerable conceptual, technical and organizational challenges, as well as scepticism about the current hype around them. This article aims to identify the approaches and technologies that could be implemented robustly by medicinal chemists in the near future and to critically analyse the opportunities and challenges for their more widespread application.

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Automating drug discovery

Artificial intelligence (AI) tools are increasingly being applied in drug discovery. While some protagonists point to vast opportunities potentially offered by such tools, others remain sceptical, waiting for a clear impact to be shown in drug discovery projects. The reality is probably somewhere in-between these extremes, yet it is clear that AI is providing new challenges not only for the scientists involved but also for the biopharma industry and its established processes for discovering and developing new medicines. This article presents the views of a diverse group of international experts on the 'grand challenges' in small-molecule drug discovery with AI and the approaches to address them.

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Rethinking drug design in the artificial intelligence era

Ruthenium(II) [Ru(II)] polypyridyl complexes have been the focus of intense investigations since work began exploring their supramolecular interactions with DNA. In recent years, there have been considerable efforts to translate this solution-based research into a biological environment with the intention of developing new classes of probes, luminescent imaging agents, therapeutics and theranostics. In only 10 years the field has expanded with diverse applications for these complexes as imaging agents and promising candidates for therapeutics. In light of these efforts this review exclusively focuses on the developments of these complexes in biological systems, both in cells and in vivo, and hopes to communicate to readers the diversity of applications within which these complexes have found use, as well as new insights gained along the way and challenges that researchers in this field still face.

The development of ruthenium(II) polypyridyl complexes and conjugates for in vitro cellular and in vivo applications

// Artur Kadurin 1, 2, 3, 4 , Alexander Aliper 2 , Andrey Kazennov 2, 7 , Polina Mamoshina 2, 5 , Quentin Vanhaelen 2 , Kuzma Khrabrov 1 , Alex Zhavoronkov 2, 6, 7 1 Search Department, Mail.Ru Group Ltd., Moscow, Russia 2 Pharmaceutical Artificial Intelligence Department, Insilico Medicine, Inc., Emerging Technology Centers, Johns Hopkins University at Eastern, Baltimore, Maryland, USA 3 Big Data and Text Analysis Laboratory, Kazan Federal University, Kazan, Republic of Tatarstan, Russia 4 St. Petersburg Department of V.A. Steklov Institute of Mathematics of the Russian Academy of Sciences, Petersburg, Russia 5 Department of Computer Science, University of Oxford, Oxford, UK 6 The Biogerontology Research Foundation, Trevissome Park, Truro TR4 8UN, UK 7 Moscow Institute of Physics and Technology, Dolgoprudny, Russia Correspondence to: Alex Zhavoronkov, email: alex@insilicomedicine.com Keywords: generative adversarian networks, adversarial autoencoder, deep learning, drug discovery, artificial intelligence Received: June 14, 2016      Accepted: November 24, 2016      Published: December 22, 2016 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.

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

Why and how have drug discovery strategies in pharma changed? What are the new mindsets?

The present invention relates to novel aryloazol-2-yl-cyanoethylamino derivatives of formula (I): 
wherein R 3 , R 4 , R 5 , R 6 , R 7 , P, Q, V, W, X, Y, Z and a are as defined in the description, compositions thereof, processes for their preparation and their uses as pesticides.

Aryloazol-2-yl cyanoethylamino compounds, method of making and method of using thereof

Compound high-quality criteria: a new vision to guide the development of drugs, current situation

A method of controlling parasites in or on an animal comprising administering to the animal a parasiticidally effective, substantially non-emetic 1-arylpyrazole.

Control of arthropods in animals

Several optically pure cis-tricyclo[9.3.1.0 3,8 ]pentadecanones have been prepared via anionic oxy-Cope rearrangement of exo-norbornanol precursors that have been obtained by convergent coupling of two functionalized reaction partners. The sigmatropic rearrangement is shown to proceed with exceptionally good stereochemical transmission because of universal adherence to the same endochair transition state. The atropisomeric aspects of this pivotal transformation are addressed. Also investigated was the proclivity of the products to undergo transannular hemiketal formation following osmylation of the bridgehead double bond. As matters turn out, the operation or nonoperation of this intramolecular addition to the C-9 carbonyl group is amenable to control merely by adjusting properly the stereochemistry of a single pendant substituent

Huber Scot Kevin

Papers

Why and how have drug discovery strategies in pharma changed? What are the new mindsets?

Aryloazol-2-yl cyanoethylamino compounds, method of making and method of using thereof

Compound high-quality criteria: a new vision to guide the development of drugs, current situation

Control of arthropods in animals

A means for stereocontrolled introduction of the C-2 oxygen substituent in functionalized cis-tricyclo[9.3.1.03,8]pentadecanones related to taxol