Artificial intelligence to generate new cancer drugs on demand

Summary:

• Clinical trial failure rates for small molecules in oncology exceed 94% for molecules previously tested in animals and the costs to bring a new drug to market exceed $2.5 billion
• There are around 2,000 drugs approved for therapeutic use by the regulators with very few providing complete cures
• Advances in deep learning demonstrated superhuman accuracy in many areas and are expected to transform industries, where large amounts of training data is available
• Generative Adversarial Networks (GANs), a new technology introduced in 2014 represent the "cutting edge" in artificial intelligence, where new images, videos and voice can be produced by the deep neural networks on demand 
• Here for the first time we demonstrate the application of Generative Adversarial Autoencoders (AAEs), a new type of GAN, for generation of molecular fingerprints of molecules that kill cancer cells at specific concentrations
• This work is the proof of concept, which opens the door for the cornucopia of meaningful molecular leads created according to the given criteria 
• The study was published in Oncotarget and the open-access manuscript is available in the Advance Open Publications section
• Authors speculate that in 2017 the conservative pharmaceutical industry will experience a transformation similar to the automotive industry with deep learned drug discovery pipelines integrated into the many business processes
• The extension of this work will be presented at the "4th Annual R&D Data Intelligence Leaders Forum" in Basel, Switzerland, Jan 24-26th, 2017

Thursday, 22nd of December Baltimore, MD - Scientists at the Pharmaceutical Artificial Intelligence (pharma.AI) group of Insilico Medicine, Inc, today announced the publication of a seminal paper demonstrating the application of generative adversarial autoencoders (AAEs) to generating new molecular fingerprints on demand. The study was published in Oncotarget on 22nd of December, 2016. The study represents the proof of concept for applying Generative Adversarial Networks (GANs) to drug discovery. The authors significantly extended this model to generate new leads according to multiple requested characteristics and plan to launch a comprehensive GAN-based drug discovery engine producing promising therapeutic treatments to significantly accelerate pharmaceutical R&D and improve the success rates in clinical trials.

Since 2010 deep learning systems demonstrated unprecedented results in image, voice and text recognition, in many cases surpassing human accuracy and enabling autonomous driving, automated creation of pleasant art and even composition of pleasant music. 

GAN is a fresh direction in deep learning invented by Ian Goodfellow in 2014. In recent years GANs produced extraordinary results in generating meaningful images according to the desired descriptions. Similar principles can be applied to drug discovery and biomarker development. This paper represents a proof of concept of an artificially-intelligent drug discovery engine, where AAEs are used to generate new molecular fingerprints with the desired molecular properties. 

"At Insilico Medicine we want to be the supplier of meaningful, high-value drug leads in many disease areas with high probability of passing the Phase I/II clinical trials. While this publication is a proof of concept and only generates the molecular fingerprints with the very basic molecular properties, internally we can now generate entire molecular structures according to a large number of parameters. These structures can be fed into our multi-modal drug discovery pipeline, which predicts therapeutic class, efficacy, side effects and many other parameters. Imagine an intelligent system, which one can instruct to produce a set of molecules with specified properties that kill certain cancer cells at a specified dose in a specific subset of the patient population, then predict the age-adjusted and specific biomarker-adjusted efficacy, predict the adverse effects and evaluate the probability of passing the human clinical trials. This is our big vision", said Alex Zhavoronkov, PhD, CEO of Insilico Medicine, Inc. 

Previously, Insilico Medicine demonstrated the predictive power of its discovery systems in the nutraceutical industry. In 2017 Life Extension will launch a range of natural products developed using Insilico Medicine's discovery pipelines. Earlier this year the pharmaceutical artificial intelligence division of Insilico Medicine published several seminal proof of concept papers demonstrating the applications of deep learning to drug discovery, biomarker development and aging research. Recently the authors published a tool in Nature Communications, which is used for dimensionality reduction in transcriptomic data for training deep neural networks (DNNs). The paper published in Molecular Pharmaceutics demonstrating the applications 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. Another paper demonstrating the ability to predict the chronological age of the patient using a simple blood test, published in Aging, became the second most popular paper in the journal's history. 

"I am very happy to work alongside the Pharma.AI scientists at Insilico Medicine on getting the GANs to generate meaningful leads in cancer and, most importantly, age-related diseases and aging itself. This is humaniкty's most pressing cause and everyone in machine learning and data science should be contributing. The pipelines these guys are developing will play a transformative role in the pharmaceutical industry and in extending human longevity and we will continue our collaboration and invite other scientists to follow this path", said Artur Kadurin, the head of the segmentation group at Mail.Ru, one of the largest IT companies in Eastern Europe and the first author on the paper. 

"Generative AAE is a radically new way to discover drugs according to the required parameters. At Pharma.AI we have a comprehensive drug discovery pipeline with reasonably accurate predictors of efficacy and adverse effects that work on the structural data and transcriptional response data and utilize the advanced signaling pathway activation analysis and deep learning. We use this pipeline to uncover the prospective uses of molecules, where these types of data are available. But the generative models allow us to generate completely new molecular structures that can be run through our pipelines and then tested in vitro and in vivo. And while it is too early to make ostentatious claims before our predictions are validated in vivo, it is clear that generative adversarial networks coupled with the more traditional deep learning tools and biomarkers are likely to transform the way drugs are discovered", said Alex Aliper, president, European R&D at the Pharma.AI group of Insilico Medicine. 

Recent advances in deep learning and specifically in generative adversarial networks have demonstrated surprising results in generating new images and videos upon request, even when using natural language as input. In this study the group developed a 7-layer AAE architecture with the latent middle layer serving as a discriminator. As an input and output AAE uses a vector of binary fingerprints and concentration of the molecule. In the latent layer the group introduced a neuron responsible for tumor growth inhibition index, which when negative it indicates the reduction in the number of tumour cells after the treatment. To train AAE, the authors used the NCI-60 cell line assay data for 6252 compounds profiled on MCF-7 cell line. The output of the AAE was used to screen 72 million compounds in PubChem and select candidate molecules with potential anti-cancer properties. 

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About Insilico Medicine, Inc

Insilico Medicine, Inc. is a bioinformatics company located at the Emerging Technology Centers at the Johns Hopkins University Eastern campus in Baltimore with Research and Development ("R&D") resources in Belgium, UK and Russia hiring talent 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 pursues internal drug discovery programs in cancer, Parkinson's Disease, Alzheimer's Disease, sarcopenia, and geroprotector discovery. Through its Pharma.AI division, the company provides advanced machine learning services to biotechnology, pharmaceutical, and skin care companies. Brief company video: https://www.youtube.com/watch?v=l62jlwgL3v8

GeroScope -- a computer method to beat aging

Scientists give robots the 'menial task' of searching for the key to eternal life

Russian scientists from MIPT, in collaboration with Insilico Medicine Inc., were commissioned by the Center for Biogerontology and Regenerative Medicine to develop the GeroScope algorithm to identify geroprotectors - substances that extend healthy life. Hundreds of compounds were screened for geroprotective activity using computer simulations, and laboratory experiments were conducted on the ten substances that were identified using this algorithm. A research paper detailing the results of the study has been published in one of the top peer-reviewed journals in aging research, Aging.

Decades of hard work by highly-competent research teams and millions of dollars are spent on the process of developing new drugs. And the screening and development process of geroprotectors, interventions intended to combat aging, a complex multifactorial biological process affecting every cell in the human body, is even more tedious. Computer modeling techniques may significantly reduce the time and cost of development.

"The aging of the population is a global problem. Developing effective approaches for creating geroprotectors and validating them for use in the human body is one of the most important challenges for biomedicine. We have proposed a possible approach that brings us one step closer to solving this problem," said Alexey Moskalev, a corresponding member of the RAS and head of the Laboratory of Genetics of Aging and Longevity.

For several years the group studied cancer-related processes and relied on the Oncofinder, an algorithm designed to study and analyze the activation values of molecular pathways by comparing gene expression in cancerous and normal healthy cells, and also comparing tissue samples of different patients. The researchers applied a similar approach to develop GeroScope, which is able to compare changes in the cells of young and old patients and search for drugs with minimal side effects that compensate for these changes. 

To do this, the scientists analyzed transcriptomic data (information which is read from DNA and transcribed into RNA) in "young" (donors aged between 15 and 30 years) and "old" (donors over the age of 60) samples from many human tissue types. This data was used for advanced computer modeling to identify and re-construct the molecular pathways associated with aging. Molecular pathways are a sequence of reactions that lead to changes in a cell. The most common molecular pathways are involved in metabolism and signal transduction. GeroScope modeled molecular pathways and analyzed cell reactions to various substances. Having chosen 70 compounds from the database of geroprotective drugs, previously published by the research group in a paper titled "Geroprotectors.org: a new, structured and curated database of current therapeutic interventions in aging and age-related disease," the scientists used the new algorithm to identify 10 substances that could have geroprotector properties in accordance with the model.

The GeroScope model was used to analyze the tissues of young and old patients, as well as cell lines. In order to experimentally verify the algorithm, the scientists took stem cell lines of human fibroblasts (connective tissue cells). Two effects were studied: cell "rejuvenation" and survival.

The experiments started with the measurement of the many parameters of viable cells: the size, shape, and complexity of the internal structure of the cell etc. The cells were then mixed with a test substance and a growth medium and held in this state for 6, 12, and 18 days. The scientists then measured the same parameters as at the start of the experiment, as well as the level of associated β?galactosidase, which is considered one of the markers of aging.

The 10 test substances chosen by the computer model demonstrated different results in human cell assays. For example, NDGA has no effect on rejuvenation, but it does decrease short- and long-term survival, Myricetin has a mild rejuvenating effect and EGCG has a strong rejuvenating effect. NAC has a very mild rejuvenating effect, but dramatically increases short- and long-term survival, PD-98059 has a very strong rejuvenating effect and increases both short- and long-term survival*.

The predictions made by the computer model were confirmed in cell cultures of human fibroblasts for several substances: PP-98059, NAC, Myricetin and EGCG. Some of these drugs are already actively sold as dietary supplements individually. Further analysis of the pathway-level effects of many of these compounds provided insights into the possible combinations providing maximal cumulative effects and minimizing the possible adverse effects. 

"For computer modeling this is a very good result. In the pharmaceutical industry, 92% of drugs that are tested on animals fail during clinical trials in humans. The ability to simulate biological effects with such a high level of accuracy in silico is a real breakthrough. PD-98059 and NAC proved to be the strongest geroprotectors. We hope that some of these drugs will soon be tested on people using biologically-relevant biomarkers of aging," said Alex Zhavoronkov Ph.D., head of the Laboratory of Regenerative Medicine at the D. Rogachev Federal Research and Clinical Center for Pediatric Hematology, Oncology, and Immunology, an adjunct professor at MIPT, and head of Insilico Medicine Inc. (Emerging Technology Centers located at the Johns Hopkins University at Eastern Campus).

Earlier this year Alexey Moskalev and Alex Zhavoronkov collaborated on applying the deep learning techniques to develop cost-effective biomarkers of aging on one of the most abundant data types from simple blood tests, Putin E, et al, "Deep biomarkers of human aging: Application of deep neural networks to biomarker development. These and other biomarkers developed using deep learning techniques commonly referred to as artificial intelligence will be applied to validating the effects of geroprotectors in humans. 

The GeroScope algorithm developed for geroprotector screening has thus been successfully validated using series of experiments on human cells. A high correlation was demonstrated between the predictions made by the algorithm and experimental data. GeroScope will later be used to search for unknown substances with geroprotective effects as well as for compounds that may be used to treat a variety of the age-related conditions.

Partnership of LongeVC and Insilico Medicine to screen for projects maximizing longevity dividend

• At the "Ageing Societies 2016," The Economist conference in London, LongeVC and Insilico Medicine entered into a research collaboration
• LongeVC is a new venture fund investing in companies developing drugs and other interventions to cure or prevent age-related diseases with a mission to extend healthy human longevity
• Over 90% of small molecules that pass pre-clinical validation fail in clinical trials
• Insilico Medicine is one of the leaders in applying artificial intelligence and advanced pathway activation analysis techniques to predict the efficacy and safety of small molecules and the outcomes of clinical trials
• LongeVC will utilize Insilico expertise to identify projects with high probability of passing the clinical trials and maximizing the longevity dividend

 Insilico Medicine today announced a research collaboration with LongeVC, a venture fund dedicated to investing in companies targeting aging and age-related diseases. In the scope of this collaboration, Insilico Medicine's signaling pathway-based analytical methods and deep learned predictors will be used to evaluate the efficacy, adverse effects, and population-specificity of small molecules, antibodies, gene therapy, and other interventions.

The parties entered into a research collaboration at the "Ageing Societies 2016" conference organized by The Economist, one of the most reputable journals in the field (for more information, see: http://www.economist.com/events-conferences/emea/ageing-societies). 

"The percentage of the elderly population in developed countries is rapidly increasing and presents a major growth opportunity for businesses focused on increasing productive longevity, as well as treating and preventing age-related diseases. But identifying the winning technologies requires complex screening methods, so we partnered with one of the leaders in biomarker development and aging research to perform quality control and to assess the effectiveness of anti-aging interventions. We are aiming to identify projects that make the most impact and have a reasonably high probability of success. At the end of the day, everything we own, we rent for the length of our lifetime, and investing in longevity is one of the ways to maximize the value of our assets," said Garri Zmudze, managing partner at LongeVC. 

Insilico Medicine developed a portfolio of deep learned and parametric biomarkers of human aging utilizing simple blood biochemistry tests (for more information, see: http://www.Aging.AI), blood-derived and tissue-specific gene expression data, and imaging data. These biomarkers are essential for assessing the relevance of experiments performed on animals.

"When evaluating interventions related to aging and age-related diseases, it is important to have biologically-relevant biomarkers of aging that work in humans. The ability to backtest some of the interventions on retrospective data is very appealing. LongeVC is the first fund focused on longevity research, and we welcome this collaboration with Insilico Medicine, one of the most established players in bioinformatics and longevity science," said Fred Khodorov, CFP®, senior pharmaceutical and healthcare consultant, and advisor to LongeVC. 

Insilico Medicine's in silico Pathway Activation Network Decomposition Analysis ("iPANDA") technique, recently published in Nature Communications, doi:10.1038/ncomms13427 (for more information, see: http://www.nature.com/articles/ncomms13427), will be applied to projects where transcriptomic or proteomic data is available to analyze pathway-level effects of interventions. DeepPharma™ clinical trials scoring engine will be used to predict the outcomes of the Phase I (safety) and Phase II (efficacy) clinical trials. The scoring engine has already demonstrated proof of concept of using Deep Neural Networks ("DNNs") to predict the pharmacological properties of many drugs. 

Insilico Medicine's HedgePharma system, which predicts the outcomes of Phase II clinical trials, is a by-product of a massive backtesting project, where scientists working on predictive modeling analyzed almost all clinical trials and linked them to stock prices of biotechnology and pharmaceutical companies to understand if some of the clinical trials failed or succeeded when outcome measures were unclear. This system is now used by some venture funds and hedge funds to make investment decisions in biomedicine. 

"We are very happy to collaborate with LongeVC, an emerging venture fund intended to address one of the most pressing challenges of our time - aging, by investing in companies developing therapies that may extend human productive longevity. It is a pleasure to see that more and more smart investors are moving into the field of longevity science. I hope that other venture funds, hedge funds, and portfolio managers will utilize our expertise in omics-informed clinical trials risk estimates and our experience in analyzing the longevity dividend in terms of the productivity-adjusted life years ("PALY") of potential investment projects," said Alex Zhavoronkov, PhD, CEO of Insilico Medicine, Inc. 

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About LongeVC

LongeVC is a next generation venture fund focusing on high-impact investments in biotechnology and healthcare companies developing therapeutic interventions or enabling technologies that have a chance to result in complete cures of major age-related diseases and producing maximum longevity dividends. The company is founded by a team of experts in information technology, pharmaceutical industry, telecommunications, and finance. 

Company website: http://www.longevc.com. Contact: hq@longevc.com. 

About Insilico Medicine, Inc.

Insilico Medicine, Inc. is a bioinformatics company located at the Emerging Technology Centers at the Johns Hopkins University Eastern campus in Baltimore with Research and Development ("R&D") resources in Belgium, Russia, and Poland hiring talent 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 pursues internal drug discovery programs in cancer, Parkinson's Disease, Alzheimer's Disease, sarcopenia, and geroprotector discovery. Through its Pharma.AI division, the company provides advanced machine learning services to biotechnology, pharmaceutical, and skin care companies. Brief company video: https://www.youtube.com/watch?v=l62jlwgL3v8.

iPANDA: A novel approach for precision medicine and drug discovery on gene expression data

Summary:

• Insilico Medicine collaborating with a large group of scientists from top-tier academic institutions, pharmaceutical and biotechnology companies developed and published a novel tool for deriving new insights from gene expression repositories;
• The method is called "in silico Pathway Activation Network Decomposition Analysis (iPANDA) as a method for biomarker development". The proof of concept was demonstrated the application of iPANDA as a biomarker developmental tool that can be used in personalized medicine applications;
• The consortium uses the iPANDA method for biologically-relevant dimensionality reduction when training the deep neural networks to predict the various pharmacological properties of molecules and developing biomarkers using highly-variable, sparse and highly-dimensional gene expression data;
• The paper is published in Nature Communications on the 16th of November, 2016, with aToday Insilico Medicine announced the publication of its in silico Pathway Activation Network Decomposition Analysis (iPANDA), a novel approach for analyzing signaling and metabolic pathway perturbation states using gene expression data, in Nature Communications. iPANDA is a scalable robust method for rapid biomarker development using gene expression data. In the present work Insilico Medicine team together with collaborators from the Johns Hopkins University, Albert Einstein College of Medicine, Boston University, Novartis, Nestle and BioTime demonstrate that iPANDA provides significant noise reduction in transcriptomic data and identifies highly robust sets of biologically relevant pathway signatures for breast cancer patients according to their sensitivity to neoadjuvant therapy.

"In this paper we describe one of our most sensitive and biologically-relevant approaches for integrating and analyzing gene expression data and present its application for stratifying responders and non-responders to targeted chemotherapy. However, the applications of this method stretch beyond precision medicine and we use it primarily for artificial intelligence and aging research. My team and many of our collaborators also use it to develop targetable tissue-specific signatures of senescence, cross-species gene expression analysis ", said Ivan Ozerov, PhD, head of senolytics group at Insilico Medicine and the lead author on the paper. 

"iPANDA is a universal tool, which allows us to perform in-depth analysis of the effects of external perturbations on the activation of signaling pathways and how it affects the downstream targets. A systematic use of such approach will contribute to obtain a better understanding of how genes involved in various cancers, disease and age-related diseases are dynamically controlled by sets of highly complex networks of signaling pathways. Information gained using in silico approaches are helpful to design more efficient therapies", said Ksenia Lezhnina, head of the "Nutriomi" personalized omics-informed nutrition group at Insilico Medicine and the co-author of the paper. 

The iPANDA method combines precalculated gene coexpression data with gene importance factors based on the degree of differential gene expression and pathway topology decomposition for obtaining pathway activation scores. While modern pathway-based methods often fail to provide stable pathway signatures of a specific phenotype or disease condition, iPANDA produces highly consistent sets of biologically relevant biomarkers acquired on multiple transcriptomic data sets. 

"Deep learning is turning into a lego game, where the various techniques are combined as blocks of sophisticated architectures. However, ensuring biological relevance of the outputs and learned or generated features is difficult. When we started working on this method in 2014, we came up with the criteria for pathway activation scoring quality metrics that included the ability to find highly discriminative pathway markers, biological relevance, cumulative effect, batch effect minimization, consistent performance at the platform- , species-, tissue- and experiment-level, and most importantly, the ability to reduce the dimensionality of gene expression data for the deep neural networks. iPANDA was conceived with all these criteria in mind and we use it primarily for dimensionality reduction in applications utilizing deep learning", said Alex Zhavoronkov, PhD, CEO of Insilico Medicine.

Signalling pathway activation scoring using iPANDA will likely help reduce the dimensionality of expression data without losing biological relevance and may be used as an input to rapidly developing deep learning methods especially for drug discovery applications. Using iPANDA values as an input data seems to be a particularly high-potential approach to obtaining reproducible results when analysing transcriptomic data from multiple sources. Therefore, while there is no single preferential approach for interpreting gene expression results, the iPANDA method of transcriptomic data analysis on the signaling pathway level may not only be useful for discrimination between various biological or clinical conditions, but may aid in identifying functional categories or pathways that may be relevant as possible therapeutic targets.

"Gene expression profiles generated on microarray equipment is one of the most abundant biological data types with hundreds of thousands of published experiments. A method which allows scientists to make better use of this data, perform quality control and integrate it with RNASeq and protein expression data is extremely important", said Charles Cantor, PhD, the co-founder and former CSO of Sequenom, former CSO of the Human Genome Project from the DOE and advisor to Insilico Medicine, a co-author of the publication. 

"The iPANDA algorithm is a very sensitive pathway perturbation analysis method, which allows for granular comparison of the differentiation states of human cell and tissues using highly sparse gene expression data. It also serves as a biologically-relevant dimensionality reduction and feature selection tool for training the deep neural networks", said Michael West, Ph.D., CEO of BioTime, Inc

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About Insilico Medicine, Inc

Insilico Medicine, Inc. is a bioinformatics company headquartered at the Emerging Technology Centers (ETC) located at the Johns Hopkins University Eastern campus in Baltimore with R&D resources in Belgium, Russia and Poland. A pioneer in applying deep learning techniques to biomarker development and drug discovery, the company is constantly hiring highly-efficient and motivated talent through the bioinformatics and deep learning hackathons and competitions including DeepHack, GeneHack, SkinHack, AgeNet, Innovate Hamburg and others. The company is pursuing internal drug discovery programs in cancer, Parkinson's, Alzheimer's, sarcopenia and geroprotector discovery. Through its Pharma.AI division the company provides advanced machine learning services to biotechnology, pharmaceutical and skin care companies. Brief company video: https://www.youtube.com/watch?v=l62jlwgL3v8

Insilico Medicine launches a deep learned biomarker of aging, Aging.AI 2.0 for testing

Today, Insilico Medicine, Inc., a company applying latest advances in deep learning to biomarker development, drug discovery and aging research, launched Aging.AI 2.0, the blood biochemistry predictor of human age. Capitalizing on the success of Aging.AI 1.0 platform, using just 41 blood biochemistry biomarkers launched in January 2016 and tested by thousands of people, the Aging.AI 2.0 allows users to use just 33 parameters from their recent blood test to guess their chronological age. The system is available for beta testing via http://www.Aging.AI .

The Aging.AI 2.0 has slightly higher mean absolute error than the previous version; however, it covers more population groups and works slightly better on the long tail of the older population. The research study behind the Aging.AI system was published in a leading peer-reviewed journal in the field of aging: Putin, et al, "Deep biomarkers of human aging: Application of deep neural networks to biomarker development." Aging 8, no. 5 (2016): 1-021 and recent studies demonstrated that these markers are population-specific. At the recent "3rd International Aging Research for Drug Discovery" conference in Basel, Switzerland, Dr. Mun Yew Wong, the CEO of Asia Genomics presented the first insights into a study demonstrating that certain population groups in Asia are guessed younger in older age by the deep neural networks trained on Eastern European population and the mean absolute error is higher. 

"Deep Learning with no doubt has a huge potential in healthcare, but unfortunately, very few groups are applying it to aging research. Aging is one of the most complex and multifactorial processes killing millions every year and causing more suffering than any other known disease. We are developing deep integrated biomarkers of aging that incorporate blood biochemistry, transcriptomics and even imaging data to be able to track the effectiveness of the various interventions we are developing", said Alex Zhavoronkov, PhD, CEO of Insilico Medicine.

Insilico Medicine also sees the applications of deep learned biomarkers of aging in multiple applications including clinical trials enrollment, clinical practice and regular health checkups. 

"Old-school physicians were trained to guess the age of the patient the moment he or she walked into the office and if the patient looked significantly older than the chronological age, more extensive testing was advised. This may be the case with Aging.AI, since what we are really looking for when training the DNNs to guess the age of reasonably healthy people is the biomarker of health status. And even though the system may guess your age with significant error when you first use it, what we want to study is the differential changes for each individual patient so people could monitor their health and adjust their lifestyle", said Alex Aliper, president of European operations at Insilico Medicine. 

Aging.AI 2.0 was trained on more samples from North America, and Central Europe and may demonstrate lower error rates on across population groups than Aging.AI 1.0. Insilico Medicine is constantly looking for collaborators with large data sets to develop better biomarkers of aging and disease. Please contact Insilico Medicine for collaboration opportunities. 

"Our research team is primarily focused on developing transcriptomic blood-derived and tissue-specific deep learned biomarkers of aging and disease trained on a large number of gene expression datasets and blood biochemistry data is not our primary focus. However, encouraged by the success of the first version of Aging.AI, we decided to improve our algorithm and change the interface of the web-version to made it more user-friendly. The system is easy to use I hope that this would stimulate more people to participate in aging research and to pay more attention to their own health. We are working on integrating the blood biochemistry data with gene expression data in order to build a comprehensive, biologically-relevant biomarker of aging" - said Polina Mamoshina, a Research Scientist of Pharmaceutical Artificial Intelligence division of Insilico Medicine.

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About Insilico Medicine

Insilico Medicine, Inc. is a bioinformatics company located at the Emerging Technology Centers at the Johns Hopkins University Eastern campus in Baltimore with R&D resources in Belgium, Russia and Poland hiring talent through hackathons and competitions. It 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 pursues internal drug discovery programs in cancer, Parkinson's, Alzheimer's, sarcopenia and geroprotector discovery. Through its Pharma.AI division the company provides advanced machine learning services to biotechnology, pharmaceutical and skin care companies. Brief company video: https://www.youtube.com/watch?v=l62jlwgL3v8

Insilico Medicine to present on applications of DL to drug discovery and repurposing at Boston SPDR

 Insilico Medicine today announced that it will present its recent advances in applying deep learning techniques to drug discovery and repurposing at the Strategic Partnerships in Drug Repurposing conference in Boston taking place at the Wyndham Boston Beacon Hill 27-28th of October. The CEO of Insilico Medicine, Alex Zhavoronkov, Ph.D. will give a talk titled "Deep Learning for Drug Repurposing". 

"We are very happy to be invited to present our research on deep-learned predictors of therapeutic use and adverse effects of the molecules trained on transcriptional response data and large data sets of molecular fingerprints. Many approved drugs and drugs that are currently in the pipelines of major pharmaceutical companies may be even more effective in conditions unrelated to the primary indications. We developed rather sophisticated pipelines to identify these alternative indications and can be used in precision medicine and even personalized drug discovery applications", said Alex Zhavoronkov, PhD, CEO of Insilico Medicine, Inc. 

Advances in artificial intelligence are quickly propagating into areas, where large data sets are available for training and the pharmaceutical industry in no exception. Earlier this year Insilico Medicine published several seminal papers describing proofs of concept of application of deep learning techniques to drug discovery (Deep Learning Applications for Predicting Pharmacological Properties of Drugs and Drug Repurposing Using Transcriptomic Data in Molecular Pharmaceutics), to biomarker development (Deep biomarkers of human aging: Application of deep neural networks to biomarker development in Aging, and to predicting the differentiation state of cells and tissues by developing a resource called Embryonic.AI in collaboration with Biotime. These concepts have been significantly expanded and applied to massive public and private data sets. The company presented a study on issues with population-specificity of deep blood biochemistry biomarkers as well as new machine learning techniques for geroprotector discovery at its annual International Aging Research for Drug Discovery Forum in Basel, Switzerland in September. 

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About Strategic Partnerships in Drug Repurposing

The Strategic Partnerships for Drug Repurposing Forum is organized by a conference conglomerate ExL Events. The conference is focused on the latest advances in drug repurposing, repositioning and rescue. Delegates learn about the different resources available to them, including public-private partnerships, foundations, patient advocacy groups, universities and other funding partners.The conference helps identify therapeutic areas or disease states that need drugs, and enable them to adopt and customize a plan for their own business models. 

The conference program is available at: http://exlevents.com/strategic-partnerships-drug-repurposing-forum/

About Insilico Medicine, Inc

Insilico Medicine, Inc. is a bioinformatics company located at the Emerging Technology Centers at the Johns Hopkins University Eastern campus in Baltimore with R&D resources in Belgium, Russia and Poland hiring talent through hackathons and competitions. It 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 pursues internal drug discovery programs in cancer, Parkinson's, Alzheimer's, sarcopenia and geroprotector discovery. Through its Pharma.AI division the company provides advanced machine learning services to biotechnology, pharmaceutical and skin care companies. Brief company video: https://www.youtube.com/watch?v=l62jlwgL3v8

Insilico Medicine to present on recent advances in AI at BioData World in Cambridge

 Insilico Medicine, Inc to present on the recent advances in applying the deep learning techniques to biomarker development and drug discovery at the BioData World Congress in Cambridge, UK. Jane Schastnaya, a research scientist at Insilico Medicine, will give a presentation on October 27th in the "Genomics personalized medicine and biomarkers" track. 

The presentation titled "Actionable biomarkers of aging and disease trained on biochemistry and transcriptomics data" will cover the applications of deep learning techniques to large repositories of biomedical data and creating multi-modal biomarkers and predictors of therapeutic use of the drugs for high-throughput screening, deep-learned predictor of human age trained on human blood biochemistry and transcriptomics data. It will also cover the new method for deriving the importance of the features in deep networks soon to be published in a peer-reviewed journal. 

"BioData World is one of the world's top conferences on bioinformatics bridging academia and industry, and I am very thankful to the organizers and our company for the opportunity to present our research at such a high-profile event. Insilico Medicine is clearly one of the leaders in the field with several "industry firsts" and events like BioData World allow us to find new challenges and sources of data that rapidly result in valuable research results. I will be presenting our work on applying deep learning techniques to estimating the chronological age of the patient using very simple blood tests as well as the comprehensive gene expression data. These biomarkers of aging are likely to be indicative of the overall health status and can be combined with the biomarkers of age-related diseases and used to improve clinical trials enrollment practices", said Jane Schastnaya, a research scientist at Insilico Medicine.

Sr. research scientist, pharmaceutical artificial intelligence (http://www.Pharma.AI) division will help with the Q&A and facilitate the discussion on applying the deep learning techniques to biomarker development and combining deep reinforcement learning and generative adversarial networks for drug discovery and establish collaborations with the top industry and academic thought leaders attending the conference. 

"Many of the advances that brought fame and recognition to the deep learning approaches were made using the imaging and text data and new discoveries are published every day. However, the adoption of these advances for drug discovery and biomarker development has been very slow, because these require domain expertise in many areas, vast amounts of data and biologically-relevant validation. At Insilico we have a very strong ecosystem which allows us to solve complex problems ranging from biomarker development to precision drug discovery to aging research. We are very happy to take part in the BioData World conference to meet some of the world's top thought leaders and present our work", said Poly Mamoshina, sr. research scientist at Insilico Medicine. 

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About BioData World Congress

BioData World Congress is the world's leading event for individuals working with Big Data in precision medicine. The BioData World Congress UK partnered with the worlds of leading organizations: EMBL EBI, Genomics England, Sanger Institute and many others. The event will showcase innovation, demonstrate success and break through the obstacles and barriers to ensure that the innovations in genomics and big data enter the clinic with speed and efficiency. It will aim to make the dream of stratified medicine a reality. With hundreds of delegates from all over the world and thought leaders from the top academic, industry and governmental organizations, BioData World Congress is one of the main international events on data-driven personalized medicine. Website: http://www.healthnetworkcommunications.com/conference/biodatA/index.stm

About Insilico Medicine

Insilico Medicine, Inc. is a bioinformatics company located at the Emerging Technology Centers at the Johns Hopkins University Eastern campus in Baltimore with R&D resources in Belgium, Russia and Poland hiring talent through hackathons and competitions. It 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 pursues internal drug discovery programs in cancer, Parkinson's, Alzheimer's, sarcopenia and geroprotector discovery. Through its Pharma.AI division the company provides advanced machine learning services to biotechnology, pharmaceutical and skin care companies. Brief company video: https://www.youtube.com/watch?v=l62jlwgL3v8

Insilico Medicine uses AI to identify geroprotectors predicted to support human longevity

Compounds with potential to extend human life identified using advances in computational biology and artificial intelligence

Summary:

• An international group of expert scientists led by Insilico Medicine published a research paper, "In search for geroprotectors: in silico screening and in vitro validation of signalome-level mimetics of young healthy state" in one of the highest-impact journals in the field, "Aging" 
• The paper presents a novel way to screen for compounds that demonstrate geroprotection by mimicking the young healthy state in tissue samples from elderly subjects using computational approaches and in vitro validation
• The top geroprotector, in terms of performance in both enhancing viability and rejuvenation was PD-98059, a highly selective inhibitor of MEK1 and the MAP kinase cascade. Natural compounds predicted to have excellent geroprotective efficacy and safety in humans include N-acetyl-L-cysteine (NAC), Myricetin and Epigallocatechin gallate (EGCG)
• Scientists used ensembles of deep neural networks to predict the safety of the compounds
• Reference: Aliper, et al (2016) "In search for geroprotectors: in silico screening and in vitro validation of signalome-level mimetics of young healthy state" Aging (Albany NY), DOI: 10.18632/aging.101047 , http://www.aging-us.com/article/MHpmpbTNuNqLnCN9g/text#fulltext

     Insilico Medicine, Inc. today announced the publication of a research paper describing the applications of its human signaling pathway-centric GeroScope platform for scoring human tissue-specific geroprotective properties of compounds implicated in the aging of model organisms. GeroScope utilizes large human gene expression databases to analyze changes in tissue samples from healthy young compared with old human subjects at the level of signaling pathways implicated in aging and longevity. This enables the identification of compounds with known transcriptional response profiles that may be able to mimic the young healthy state in old human tissues. In collaboration with Life Extension Foundation Buyer's Club, Inc., a trailblazer in the dietary supplement industry with a focus on health and wellness, authors analyzed the top scoring naturally-occurring compounds to predict the possible pathway-level synergy of combinations. 

"Life Extension Foundation Buyer's Club is a science-based nutritional supplement company recognized for their cutting-edge products and information. We are happy to collaborate with Life Extension Foundation Buyer's Club and their deep passion for health and wellness. Together we will make a major impact on the identification of natural compounds with critical health, wellness, and longevity properties," said Alex Zhavoronkov, PhD, CEO of Insilico Medicine, Inc. and the CSO of the Biogerontology Research Foundation (UK).

The study initiated with 70 compounds that already had been implicated in extension of life span in animal models (Geroprotectors.Org) for which transcriptional response data in human cell lines was available. The compounds were scored for their ability to mimic the young healthy state in old tissues and the list of compounds was narrowed to just a few candidates that were ordered and tested in human senescent fibroblasts. 

"We are excited to have these results published, since they served as an excellent platform for follow up studies we have underway. Over the past year we made considerable progress in applying deep learning to geroprotector discovery and development of comprehensive biomarkers of aging and reconfirmed some of the findings in this paper using other methods," said Alex Aliper, president of Insilico Medicine and the lead author on the paper. 

To predict the possible side effects of the compounds the scientists applied the available transcriptional response data to ensembles of the deep neural networks trained on tens of thousands of samples. While not without caveats, this approach may serve as the proof of concept for evaluating the possible adverse effects of compounds that have not yet been tested in humans. 

"We at Life Extension Foundation Buyer's Club are focused on identifying natural products with critical health and wellness properties. Our collaboration with Insilico Medicine will enable us to apply artificial intelligence, sophisticated biologically-inspired algorithms and our knowledge of dietary supplements to the discovery and development of unique science-based products. This paper represents an example of merging artificial intelligence with natural product knowledge to produce leading-edge scientific results," said Andrew G. Swick, PhD, senior vice president of scientific affairs, discovery research and product development for Life Extension.

"There has been much progress in biogerontology in the recent years and while working with model organisms, we need new methods for estimating the potential utility for humans. Insilico Medicine is clearly leading the way in this regard. In this paper we demonstrated proof of concept, where geroprotective efficacy is predicted and then validated experimentally in human senescent cells," said Alexey Moskalev, PhD, DSc, professor of the Russian Academy of Sciences, adjunct professor of the George Mason University and advisor to Insilico Medicine. 

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Journal reference: Aliper, et al (2016) "In search for geroprotectors: in silico screening and in vitro validation of signalome-level mimetics of young healthy state" Aging (Albany NY), DOI: 10.18632/aging.101047 http://www.aging-us.com/article/MHpmpbTNuNqLnCN9g/text#fulltext

About Life Extension 

A trailblazer in the $35 billion U.S. dietary supplement industry for the past 36 years, Life Extension Foundation Buyer's Club's mission is providing cutting-edge information and dietary supplements to facilitate healthy longevity. Life Extension Foundation Buyer's Club, Inc. ("Life Extension") offers a full-range of premium-quality dietary supplements as well as unique, scientifically-supported formulas. The company's products are developed based on scientific studies from peer-reviewed medical journals and are consistently updated as new information occurs. To learn more about Life Extension Foundation Buyer's Club, visit http://www.lifeextension.com/

About Insilico Medicine

Insilico Medicine, Inc. is a bioinformatics company located at the Emerging Technology Centers at the Johns Hopkins University Eastern campus in Baltimore with R&D resources in Belgium, Russia and Poland hiring talent through hackathons and competitions. It 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 pursues internal drug discovery programs in cancer, Parkinson's, Alzheimer's, sarcopenia and geroprotector discovery. Through its Pharma.AI division the company provides advanced machine learning services to biotechnology, pharmaceutical and skin care companies. Brief company video: https://www.youtube.com/watch?v=l62jlwgL3v8

Advances in computational biology and artificial intelligence used to identify compounds with potential to extend human life

Insilico Medicine, Inc. and Life Extension today announced the publication of a research paper describing the applications of its human signaling pathway-centric GeroScope platform for scoring human tissue-specific geroprotective properties of compounds implicated in the aging of model organisms.

GeroScope utilizes large human gene expression databases to analyze changes in tissue samples from healthy young compared with old human subjects at the level of signaling pathways implicated in aging and longevity. This enables the identification of compounds with known transcriptional response profiles that may be able to mimic the young healthy state in old human tissues. In collaboration with Life Extension, a trailblazer in the dietary supplement industry with a focus on health and wellness, authors analyzed the top scoring naturally-occurring compounds to predict the possible pathway-level synergy of combinations.

"Life Extension is a science-based nutritional supplement company recognized for their cutting-edge products and information," said Alex Zhavoronkov, PhD, CEO of Insilico Medicine, Inc. and the CSO of the Biogerontology Research Foundation (UK).

"We are happy to collaborate with Life Extension and their deep passion for health and wellness and together we will make a major impact on the identification of natural compounds with critical health, wellness, and longevity properties," noted Zhavoronkov.

The study initiated with 70 compounds that already had been implicated in extension of life span in animal models (Geroprotectors.Org) for which transcriptional response data in human cell lines was available. The compounds were scored for their ability to mimic the young healthy state in old tissues and the list of compounds was narrowed to just a few candidates that were ordered and tested in human senescent fibroblasts.

"We are excited to have these results published, since they served as an excellent platform for follow up studies we have underway," said Alex Aliper, president of Insilico Medicine and the lead author on the paper.

"Over the past year we made considerable progress in applying deep learning to geroprotector discovery and development of comprehensive biomarkers of aging and reconfirmed some of the findings in this paper using other methods," added Aliper.

To predict the possible side effects of the compounds the scientists applied the available transcriptional response data to ensembles of the deep neural networks trained on tens of thousands of samples. While not without caveats, this approach may serve as the proof of concept for evaluating the possible adverse effects of compounds that have not yet been tested in humans.

"We at Life Extension are focused on identifying natural products with critical health and wellness properties," said Andrew G. Swick, PhD, senior vice president of scientific affairs, discovery research and product development for Life Extension.

"Our collaboration with Insilico Medicine will enable us to apply artificial intelligence, sophisticated biologically-inspired algorithms and our knowledge of dietary supplements to the discovery and development of unique science-based products. This paper represents an example of merging artificial intelligence with natural product knowledge to produce leading-edge scientific results," Swick said.

"There has been much progress in biogerontology in the recent years and while working with model organisms, we need new methods for estimating the potential utility for humans," said Alexey Moskalev, PhD, DSc, professor of the Russian Academy of Sciences, adjunct professor of the George Mason University and advisor to Insilico Medicine.

"Insilico Medicine is clearly leading the way in this regard. In this paper we demonstrated proof of concept, where geroprotective efficacy is predicted and then validated experimentally in human senescent cells," said Moskalev.

About Life Extension

A trailblazer in the $37 billion U.S. dietary supplement industry for the past 36 years, Life Extension's mission is providing cutting-edge information and dietary supplements to facilitate healthy longevity. Life Extension offers a full-range of premium-quality dietary supplements as well as unique, scientifically-supported formulas. The company's products are developed based on scientific studies from peer-reviewed medical journals and are consistently updated as new information occurs. To learn more about Life Extension, visit www.lifeextension.com.

About Insilico Medicine

Insilico Medicine, Inc. is a bioinformatics company located at the Emerging Technology Centers at the Johns Hopkins UniversityEastern campus in Baltimore with R&D resources in Belgium, Russia and Poland hiring talent through hackathons and competitions. It 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 pursues internal drug discovery programs in cancer, Parkinson's, Alzheimer's, sarcopenia and geroprotector discovery. Through its Pharma.AI division the company provides advanced machine learning services to biotechnology, pharmaceutical and skin care companies. Brief company video: https://www.youtube.com/watch?v=l62jlwgL3v8

SOURCE Life Extension

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