Head of Segmentation Group, Mail.ru
Artur Kadurin is the head of the segmentation group at Mail.ru, one of the world's largest internet holding companies. He has worked for the company since 2011 in various technical roles in the fields of advertising, search, social media networks, and game development. He is a high-ranked online poker professional and has specialized in machine learning since 2009. He graduated from Kuban State University as a systems programmer and did his graduate work at the Steklov Mathematical Institute and at Insilico Medicine, where he is a consultant. His current research interests are in generative adversarial networks and applications of GANs to healthcare. Over the past several years, he developed an active interest in longevity research and age-related diseases.
Sr. Research Scientist, Pharmaceutical Artificial Intelligence, Insilico Medicine, Inc
Polina Mamoshina is a senior research scientist at Insilico Medicine, Inc., a Baltimore-based bioinformatics and deep learning company focused on reinventing drug discovery and biomarker development and a part of the computational biology team of Oxford University Computer Science Department. Polina graduated from the Department of Genetics of Moscow State University. She was one of the winners of GeneHack, a 48-hour hackathon on bioinformatics at the Moscow Institute of Physics and Technology attended by hundreds of young bioinformaticians from across Russia. Polina is involved in multiple deep learning projects at the Pharmaceutical Artificial Intelligence division of Insilico Medicine, working on the drug discovery engine and developing biochemistry, transcriptome, and cell-free nucleic acid-based biomarkers of aging and disease. She recently co-authored seven academic papers in peer-reviewed journals.
CEO, Insilico Medicine, Inc
Alex Zhavoronkov, PhD, is the CEO or Insilico Medicine, Inc, a company applying latest advances in artificial intelligence to drug discovery, biomarker development and aging research headquartered at the Emerging Technology Centers located at the the Johns Hopkins University at Eastern in Baltimore and the CSO of the Biogerontology Research Foundation, a UK-based registered charity supporting aging research worldwide. He is also the director of the International Aging Research Portfolio (IARP) knowledge management project and head of the Regenerative Medicine Laboratory at the Federal Clinical Research Center for Pediatric Hematology, Oncology and Immunology, one of the largest children's cancer centers in the world performing over 300 bone marrow transplantations annually since 2012.
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. We'll present the first application of generative adversarial autoencoders (AAE) for generating novel molecules with a defined set of parameters. In the first proof of concept experiment, we developed a seven-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. We'll also present the applications of this approach to discovering new anti-infective drugs and present the roadmap for generating drugs for rare diseases and even for individual patients.
Tags: Healthcare and Life Sciences; Deep Learning and AI; Computational Biology