News Release

Insilico publishes a review of deep aging clocks and announces the issuance of key patent

Insilico Medicine announced the publication of a comprehensive review of the deep biomarkers of aging and the publication of a granted patent

Peer-Reviewed Publication

InSilico Medicine

Insilico Medicine Grants a Patent on Deep Transcriptomic Aging Clock 

image: Insilico Medicine grants a patent on Deep Transcriptomic Aging Clock  view more 

Credit: Insilico Medicine

December 5th 2019 - Insilico Medicine today announced the publication of a paper titled "Deep biomarkers of aging and longevity: from research to applications" in Aging and the issuance of a key patent "Deep transcriptomic markers of human biological aging and methods of determining a biological aging clock" (US20190034581). Insilico Medicine utilizes next-generation computational approaches to accelerate the three areas of drug discovery and development: disease target identification, generation of novel molecules, and prediction of clinical trial outcomes. Age is a universal feature for every living being and allows the deep neural networks to be trained to predict age or use age to generate using multiple data types. This allows for novel methods for target identification, data quality control, and generation of synthetic biological data.

"The fields of artificial intelligence, drug discovery, and aging research are rapidly converging. We are using the deep neural networks trained on age for a variety of applications such as target identification or patient stratification geared to accelerate pharmaceutical R&D. We are very happy to see the first patent on the deep aging clocks granted. At Insilico we filed for patents for a broad range of inventions in generative chemistry and in generative biology," said Alex Zhavoronkov, PhD.


About Insilico Medicine

Insilico Medicine is an artificial intelligence company with offices in six countries and regions striving to accelerate three areas of drug discovery and development: disease target identification, generation of novel molecules (generative chemistry) and synthetic biological data (generative biology), and prediction of clinical trial outcomes. The Company was the first to apply the generative adversarial networks (GANs) and reinforcement learning (RL) to generate new molecular structures with the specified parameters in 2015. In addition to collaborating with large pharmaceutical companies, Insilico Medicine is also pursuing internal drug discovery programs in different disease areas. Recently, Insilico Medicine published a proof-of-concept study in generative chemistry in Nature Biotechnology, and secured $37 million in series B funding. Website

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