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Insilico Medicine named to the global top 100 AI companies by CB Insights

十二月 20th, 2017

BALIMORE, Dec. 20, 2017 /PRNewswire/ — CB Insights today named Insilico Medicine to the prestigious AI 100, a select group of promising private companies working on groundbreaking artificial intelligence technology. CB Insights CEO and co-founder Anand Sanwal will reveal the full list of the second annual AI 100 companies at the A-ha! conference in San Francisco.

“Last year’s AI 100 enjoyed amazing success in the year since earning this recognition. 55 of them went onto raise additional funding nearing $2B and 5 were acquired. This year’s list was culled from 1000+ applications and looks even more impressive.  These are companies using artificial intelligence in industries from drug discovery and cybersecurity to robotics and legal tech.  I’m happy that CB Insights is able to shine a light on the founders and companies that will revolutionize these industries and look forward to seeing what they do in 2018 and beyond.” said CB Insights CEO Anand Sanwal.

The CB Insights research team selected the AI 100 companies based on criteria, examining company-submitted data and the company’s Mosaic Score.The Mosaic Score, based on CB Insights’ National Science Foundation-funded algorithm, measures the overall health and growth potential of private companies. Through this evidence-based, statistically-driven approach, the Mosaic Score can help predict a company’s momentum, market health and financial viability.

“In 2014 Insilico Medicine made a bet on the deep learning technology and since then established over 250 industry and academic collaborations in both drug discovery and biomarker development and we became an innovation driver for the pharmaceutical industry. We are very happy to be recognized as one of the top 100 global AI companies by CB Insights, one of the top industry analysts,” said Alex Zhavoronkov, PhD, the CEO of Insilico Medicine, Inc

In May 2017 Insilico Medicine was named top 5 AI companies for social impact by Nvidia. It pioneered the application of the generative adversarial networks (GANs) and reinforcement learning to generation of new molecular structures with the specific set of characteristics. It also pioneered using age as the main feature for multi-modal multi-omics data integration, biomarker development, target identification and transfer learning.

“In 2014 we just got our signaling pathway perturbation analysis algorithms to work very well after several years of hard work and got venture capital to develop them further. We even got some freedom and the tools to focus on our primary interest – aging research. So the idea of refocusing the company into deep learning, which was very new back then was met with some internal resistance. But nowadays we probably have one of the most efficient and productive DL teams in the world.  We are hiring through hackathons and competitions in the many countries, where the DL talent is not overpriced and bridge the gap between the DL scientists, biologists and medicinal chemists very quickly. We also perform literature reviews at least twice a week to ensure that we incorporate all of the latest advances in DL into our models. We are happy to see that this hard work is recognized by CB Insights.”, said Alex Aliper, president of Europe, Insilico Medicine, Inc.

Insilico Medicine was the first company to apply deep generative adversarial networks (GANs) to the generation of new molecular structures with specified parameters and published seminal papers in Oncotarget and Molecular Pharmaceutics. Another paper published in Molecular Pharmaceutics in 2016 and demonstrated the proof of concept of the application of deep neural networks for predicting the therapeutic class of the molecule using the transcriptional response data, received the American Chemical Society Editors’ Choice Award. One of the recent papers published in November 2017 described the application of the next-generation AI and blockchain technologies to return the control over personal data back to the individual.

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