Wednesday, October 24, 2018, Boston - Insilico Medicine, a Rockville-based company developing the end-to-end drug discovery pipeline utilizing the next generation artificial intelligence, will present its latest results in modern and next-generation AI for biology and chemistry at the AI in Healthcare Conference in Boston, 26 of October.
Artificial Intelligence (AI) techniques, such as deep learning (DL), reinforcement learning (RL), and generative adversarial networks (GANs) play a pivotal role in studying the biology of aging on many levels. The presentation will focus on the development of multi-modal predictors of age that use a variety of data types, ranging from blood tests to microbiomic and synthetic data, in order to give the most accurate, true biological age of the patient and identify actionable targets and pathways.
"We are happy to present our work at the AI in Healthcare Conference, which gathers the key industry leaders. The topic of aging biomarkers is rapidly gaining popularity, and we are happy to be at the leading edge of research and one of the innovation drivers in the area", says Alex Zhavoronkov, Ph.D., Founder, and CEO of Insilico Medicine, Inc.
AI in Healthcare brings together all the entities from the healthcare domain to discuss the objectives of Artificial Intelligence and learn first-hand experience from the experts, developing the technology. The Conference is held on 25-26 of October.
For the audience, interested in the applications of AI to medicinal chemistry, Prof. Zhavoronkov will be presenting at another conference in Boston during the same dates, AI Applications Summit, 25-26 of October.
Insilico Medicine is regularly publishing research papers in peer-reviewed journals. The company was first to apply the generative adversarial networks (GANs) to the generation of the new molecular structures with the specified parameters and published a seminal peer-reviewed paper submitted in June 2016. The concept was further extended and augmented with advanced memory and reinforcement learning. 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. The latest special issue in Molecular Pharmaceutics featured several research papers by Insilico Medicine.
For further information, images or interviews, please contact:
Contact: Klug Gehilfer
Official website of the conference: http://www.
About Insilico Medicine, Inc
Insilico Medicine is an artificial intelligence company headquartered in Rockville, with R&D and management resources in Belgium, Russia, UK, Taiwan, and Korea sourced through hackathons and competitions. The company and its scientists are 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.
In 2017, NVIDIA selected Insilico Medicine as one of the Top 5 AI companies in its potential for social impact. In 2018, the company was named one of the global top 100 AI companies by CB Insights. In 2018 it received the Frost & Sullivan 2018 North American Artificial Intelligence for Aging Research and Drug Development Award accompanied with the industry brief. Brief company video: https:/