Thursday, July 5th, Rockville, MD - Insilico Medicine, a Rockville-based next-generation artificial intelligence company specializing in the application of deep learning for target identification, drug discovery and aging research announces the publication of a new research paper "Machine learning on human muscle transcriptomic data for biomarker discovery and tissue-specific drug target identification" in Frontiers in Genetics journal.

Sarcopenia (from Greek "flesh poverty"), is one of the major age-related processes and involves the loss of skeletal muscle and its function. Age-associated muscle wasting remains an important clinical challenge that impacts hundreds of millions of older adults. It is associated with serious negative health outcomes such as falls, impaired standing balance, physical disability, and mortality. The many insights into sarcopenia from aging research suggest that understanding the molecular mechanisms of muscle aging can reveal novel potentially rejuvenating treatments.

To address this challenge researchers from Insilico Medicine developed a novel deep-learning based model that predicts a biological age of a muscle and can be used to estimate the relevant importance of the genetic and epigenetic factors driving this process within many age groups. The paper explaining one of the simple models for applying the age predictors developed using several machine learning techniques was published in Frontiers in Genetics.

"We are working on multiple biomarkers using deep learning and including blood biochemistry, transcriptomics, and even imaging data to be able to track the effectiveness of the various interventions we are developing. We believe that the most effective anti-aging therapy should be tissue-specific, so we focused on the development of tissue-specific biomarkers of aging. This work is an example of a marker of skeletal muscle tissue. Internally, we work with six tissues, including the liver, skin, and lungs." said Polina Mamoshina, senior deep learning scientist at Insilico Medicine.

In this study, the scientists applied a state of the art signaling pathway analysis algorithm, iPANDA, to compare transcriptomic signatures of 'old' and 'young' tissues and utilized several machine learning methods to predict the age of samples based on their transcriptomic signatures. Ultimately, the trained age predictors were used to identify tissue-specific aging clocks.

This combined data-driven approach demonstrates that age prediction models can become a powerful tool for identifying prospective targets for geroprotectors.

"Sarcopenia is one of the lowest-hanging fruits and major commercial opportunities out of all of the diseases of aging we are focusing on. This paper presents one of the many way machine learning can be used to identify biological targets in muscle wasting. While this approach does not establish causality or evaluate the most important targets for a specific age group, it may be appealing to the pharmaceutical companies looking at developing broad-spectrum interventions for sarcopenia. I am very happy to see that Polina's work done in 2015 finally saw the light", said Alex Zhavoronkov, PhD, founder, and CEO of Insilico Medicine, Inc.

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For its work in the field of artificial intelligence for drug discovery and development, Insilico Medicine received the Frost & Sullivan 2018 North American Artificial Intelligence for Aging Research and Drug Development Technology Innovation Award. The company plans to use GAN-RL systems to target age-related diseases and aging itself.

"Technology leadership in artificial intelligence for drug discovery and biomarker development, academic excellence, extensive collaborations with pharmaceutical and consumer companies, novel methods of attracting top talent, and increasing global reach have allowed Insilico Medicine to build a credible and sustainable business model in the nascent longevity biotechnology industry," noted Neelotpal Goswami. "In recognition of its pioneering research and ability to introduce novel products and solutions for age management, Frost & Sullivan is pleased to present it with the 2018 Technology Innovation Award."

Insilico Medicine is regularly publishing research papers in peer-reviewed journals. The company was first who applied deep generative adversarial networks (GANs) to the generation of new molecular structures with specified parameters and published seminal proof of concept papers in the field. The paper published in Molecular Pharmaceutics in 2016 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. A recent paper 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. One of the latest papers published in the Journals of Gerontology demonstrated the application of the deep neural networks to assessing the biological age of the patients.

For further information, images or interviews, please contact:

Contact: Qingsong Zhu, PhD

zhu@pharma.ai

Website: http://www. Insilico. com

About Insilico Medicine, Inc

Insilico Medicine, Inc. is an artificial intelligence company headquartered at the Emerging Technology Centers at the Johns Hopkins University Eastern campus in Baltimore, with R&D and management resources in Belgium, Russia, UK, Taiwan and Korea sourced through hackathons and competitions. The company and its scientists is dedicated to extending human productive longevity and transforming every step of the drug discovery and drug development process through excellence in biomarker discovery, drug development, digital medicine and aging research.

Insilico pioneered the applications of the generative adversarial networks (GANs) and reinforcement learning for generation of novel molecular structures for the diseases with a known target and with no known targets. In addition to working collaborations with the large pharmaceutical companies, the company is pursuing internal drug discovery programs in cancer, dermatological diseases, fibrosis, Parkinson's Disease, Alzheimer's Disease, ALS, diabetes, sarcopenia, and aging. Through a partnership with LifeExtension.com the company launched a range of nutraceutical products compounded using the advanced bioinformatics techniques and deep learning approaches. It also provides a range of consumer-facing applications including Young.AI.