Advances in deep learning for biomarker development to be presented at the PMWC in Silicon Valley

Thursday, Jan. 18th, 2018, Baltimore, MD - Insilico Medicine, a Baltimore-based company specializing in the application of artificial intelligence for drug discovery, biomarker development and aging research, is pleased to announce the lecture of its founder and CEO, Dr. Alex Zhavoronkov, at the Precision Medicine World Conference (PMWC) 2018, January 23, 2018, Silicon Valley.

The AI panel will focus on the latest advances in artificial intelligence for biomarker development and drug discovery. The session will cover different approaches and AI platforms, and how they impact the pharmaceutical industry and specifically, the drug discovery and development processes. 

"PMWC Silicon Valley is one of the top conferences in biomedicine and we are very happy to be back this year after receiving the Most Promising Company award at the conference in 2015, when deep learning was still an exotic term. This year's conference is heavily focused on AI and the organizers managed to gather pretty much everyone in the field in the Computer History Museum", said Alex Zhavoronkov, PhD, the founder and CEO of Insilico Medicine, Inc.

The Precision Medicine World Conference is an independent and established conference series known as prominent medicine conference gathering recognized leaders, top global researchers, medical professionals and innovators across healthcare and biotechnology to close the gap between different sectors and to catalyze the cross-functional collaborations. The program of PMWC Silicon Valley embaraces innovative technologies, growing initiatives, and clinical case studies converting the advances of precision medicine into direct improvements in health care. The conference agenda highlights more than 70 sessions with 350+ talks covering all facets of precision medicine. The event will transpire January 22-24, 2018. 

Insilico Medicine is regularly publishing research papers in peer reviewed journals. It was the first company applied 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. The latest paper published in the Journals of Gerontologyexhibited the new artificial intelligence algorithm determining the biological age with high precision and having a potential to reveal whether lifestyle changes and medicinal products can increase people's chances of living a long and healthy life. 

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For further information, images or interviews, please contact:

Contact: Qingsong Zhu, PhD 
a href="mailto:zhu@pharma.ai">zhu@pharma.ai

Website: http://www.Insilico.com

Official Conference Website: http://www.pmwcintl.com/2018sv/

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 utilizes advances in genomics, big data analysis, and deep learning for in silico drug discovery and drug repurposing for aging and age-related diseases. 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 and Aging.AI and operates Chemistry.AI intended to capture the tacit knowledge of medicinal chemists. 

Through a partnership with the BitFury Group, the company is working on a range of AI solutions for blockchain to help return the power over life data back to the individual. The company raised venture capital and partnered with Juvenescence Limited, a holding company focused on longevity biotechnology. The company aspires to become the "Bell Labs" for artificial intelligence and associated technologies for healthcare and longevity biotechnology and commercialize its research by forming subsidiaries around the specific technologies and licensing the intellectual property, molecules and data to the biotechnology and pharmaceutical companies. 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. Brief company video: https://www.youtube.com/watch?v=l62jlwgL3v8

Insilico to present the latest advances in AI for drug discovery at Advanced Pharma Analytics Summit

Tuesday, January 16th, 2018, Baltimore, MD - Insilico Medicine, a Baltimore-based next-generation artificial intelligence company specializing in the application of deep learning for drug discovery, announces the presentation of Polina Mamoshina, Senior Research Scientist involved in multiple deep learning projects at the Pharmaceutical Artificial Intelligence division of Insilico Medicine, at 5th annual Advanced Pharma Analytics Europe Summit, January 31, 2018.

The presentation will cover the recent advances in the applications of generative adversarial networks (GANs) for new molecules development in drug discovery. The fundamental principle of GANs is adversarial training based on game theory results: competition between the Generative and Discriminative networks leads to joint evolution and almost perfect results. Insilico Medicine was the first company to integrate the advances of GAN architecture into a comprehensive drug discovery pipeline with the goal to enable the deep neural networks to produce perfect molecules for the specific set of diseases including different cancers, neurodegenerative diseases such as Alzheimer's disease, virus infections, and more. 

"Generative adversarial networks (GANs) revolutionised deep learning and found its applications in several areas, including realistic image synthesis, text-to-image synthesis or even animating movie, etc Vast amounts of data collected within pharmaceutical industry can be utilised to build similar models. Nowadays, the target itself is not a competitive advantage, in most cases the value is in a molecule. At Insilico Medicine, we build GANs that work on multiple representations of the chemical structures to expand and navigate through the chemical space. Automated discovery of novel chemotypes with certain properties against specific targets offered by GANs shows a great promise to significantly facilitate early drug design stages", said Alex Aliper, President of EMEA in Insilico Medicine, Inc.

"It is a privilege for me to speak alongside the leaders in the industry at the 5th annual Advanced Pharma Analytics event. As usual, it brings together thought experts to discuss new technologies on drug discovery. Technologies based on AI have a great potential to change drug discovery process dramatically mainly by speeding it up introducing a limited amount of highly promising molecules instead of thousands with unknown activities and possibly increase the drug space, finding new drugs, a new mechanism of action, new chemistry", said Polina Mamoshina, Senior Research Scientist in Insilico Medicine, Inc.

The 5th annual Advanced Pharma Analytics Europe Summit is dedicated to supporting the growing biopharmaceutical community and provides opportunities to showcase advanced pipelines to maximize successful outcomes with cutting-edge data science and analytics. The meeting will transpire 30-31 of January 2018. 

Insilico Medicine is the first company applied 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.

The latest paper published in the Journals of Gerontology exhibited the new artificial intelligence algorithm determining the biological age with high precision and having a potential to reveal whether lifestyle changes and medicinal products can increase people's chances of living a long and healthy life. 

###

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 utilizes advances in genomics, big data analysis, and deep learning for in silico drug discovery and drug repurposing for aging and age-related duiseases. 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 and Aging.AI and operates Chemistry.AI intended to capture the tacit knowledge of medicinal chemists. 

Through a partnership with the BitFury Group, the company is working on a range of AI solutions for blockchain to help return the power over life data back to the individual. The company raised venture capital and partnered with Juvenescence Limited, a holding company focused on longevity biotechnology. The company aspires to become the "Bell Labs" for artificial intelligence and associated technologies for healthcare and longevity biotechnology and commercialize its research by forming subsidiaries around the specific technologies and licensing the intellectual property, molecules and data to the biotechnology and pharmaceutical companies. 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 the one of the global top 100 AI companies by CB Insights. Brief company video: https://www.youtube.com/watch?v=l62jlwgL3v8

For further information, images or interviews, please contact:

Contact: Qingsong Zhu, PhD
zhu@pharma.ai
Website: http://www.Insilico.com

Official Summit Website: http://advancedpharma-analytics.com

Population-specific deep biomarkers of aging

Thursday, Jan. 11th, Baltimore, MD - Today, Insilico Medicine, Inc., a Baltimore-based company specializing in the application of artificial intelligence for drug discovery, biomarker development and aging research, announced a publication of a research paper titled "Population-specific biomarkers of human aging: a big data study using South Korean, Canadian and Eastern-European patient populations" in The Journal of Gerontology. 

In the paper, the authors present a novel deep-learning based hematological human aging clock, a biomarker that predicts the biological age of individual patients. This big data study uses a large dataset of fully anonymized Canadian, South Korean and Eastern European blood test records to train an aging clock. The developed model predicts the age better than models tailored to the specific populations highlighting the differences of subregion-specific patterns of aging. In addition, the developed clocks were shown to be a better predictor of all-cause mortality than chronological age. The paper includes co-authors from Gachon University Gil Medical Center, University of Copenhagen, University of Alberta, and the Biogerontology Research Foundation. 

"If we are to develop actionable biomarkers of aging, we need a comprehensive and robust approach. Such an approach can only be developed using a large number of samples from multiple populations. We are working on multiple biomarkers using deep learning and incorporating blood biochemistry, transcriptomics, and even imaging data to be able to track the effectiveness of the various interventions we are developing". said Polina Mamoshina, a senior research scientist at Insilico Medicine.

"The pursuit of biological aging clocks is a major focus point of the aging field and is a key step in the development of interventions in human aging. This paper represents the evolution of the first easily adaptable clock that can be applied at a population level regardless of population biases. The clock is very cost-effective, without the requirement of next-generation sequencing or other specialized equipment. It is therefore extraordinarily suited for testing aging-interventions in multiple settings across the globe." said Morten Scheibye-Knudsen, MD, Head of the Biology of Aging Laboratory, Center for Healthy Aging, and associate professor, University of Copenhagen.

"Development of effective biomarkers of age is one of the most pressing goals in geroscience today, as it lays the foundation for efficient preclinical and clinical evaluation of potential healthspan-extending interventions. Humans live a long time, and testing the effect of gerontological interventions in humans using lifespan gains as the main criterion for success would be wildly impractical, necessitating long and costly longitudinal studies. By developing accurate biomarkers of aging, the efficacy of potential healthspan-extending interventions could instead be tested according to changes in study participants' biomarkers of age. While significant attention is paid to the development of highly accurate biomarkers of aging, less attention is paid to developing actionable biomarkers of aging that can be tested inexpensively using the tools at hand to the majority of researchers and clinicians. We developed the deep-learning based, blood biochemistry aging clock presented in this paper in the hopes of making progress toward the goal of more actionable biomarkers of aging" said Franco Cortese, co-author of the paper and Deputy Director of the Biogerontology Research Foundation.

"This work demonstrates the synergy between artificial intelligence and aging research. Every living being has age as a feature and it is possible to engage in multi-national collaborations using the very simple data types to assess the population specificity of age predictors. Our group is using advanced AI for multiple clinical applications and has a working collaboration with IBM Watson, but working with Insilico Medicine is a pleasure", said Lee Uhn, PhD, Chief of Artificial Intelligence at the Gachon University Gil Medical Center. 

"Age is one of the features possessed by every living creature. In 2015 we made a very neat discovery - when we train the deep neural networks to predict the age of the person, the DNNs capture the most biologically-relevant features and can be re-trained on diseases and can be used to integrate the multiple data types and also extract the most important features within each data type and across the data types. In this paper we show one of the proofs of concept on a very simple and abundant data type that we can now assess the population-specificity of the predictors, the importance of ethnicity and population group in age prediction and the differences in the most important features contributing to the accuracy of these predictors", said Alex Zhavoronkov, PhD, CEO of Insilico Medicine, Inc. 

This work may help improve clinical trial enrollment practices, assess the population specificity of a variety of the biomarkers and pave the way for the development of more complex multi-modal biomarkers of aging and disease. 

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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 located at the Emerging Technology Centers at the Johns Hopkins University Eastern campus in Baltimore, with R&D offices and resources in 6 countries sourced through hackathons and competitions. The company utilizes advances in genomics, big-data analysis, and deep learning for in silico drug discovery and drug repurposing for aging and age-related diseases. The company is pursuing internal drug discovery programs in cancer, Parkinson's Disease, Alzheimer's Disease, ALS, diabetes, sarcopenia, and aging. Through its Pharma.AI division, Insilico provides advanced machine learning services to biotechnology, pharmaceutical, and skin care companies, foundations and national governments globally. In 2017, NVIDIA selected Insilico Medicine as one of the Top 5 AI companies in its potential for social impact and CB Insights named Insilico Medicine to the prestigious top 100 AI companies.  http://www.insilico.com