September 24th, Switzerland

Free Registration, Part of MipTec 2014, Congress Center Basel


MipTec is the largest drug discovery conference in Europe with over 3,000 delegates from pharmaceutical companies


Stem Sells, Translational Medicine, Protein Therapeutics, Medicinal Chemistry and others


New in silico approaches to evaluating the efficacy of and repurposing drugs for aging and age-related diseases


The forum will attract many young scientists in aging research providing the possibility to recruit scarse talent with unique skills and interests


Unlike other aging conferences, the forum is geared towards the pharmaceutical industry addressing the need for new markers and new drugs within the traditional regulatory frameworks

Friday, December 23, 2016

Aging.AI 2.0 launches to provide a biomarker of aging with less parameters

Aging.AI 2.0 is now available for testing. Please use your recent blood test to guess your age. 

The system is available at http://www.Aging.AI

Citation (full text):
Putin, Evgeny, Polina Mamoshina, Alexander Aliper, Mikhail Korzinkin, Alexey Moskalev, Alexey Kolosov, Alexander Ostrovskiy, Charles Cantor, Jan Vijg, and Alex Zhavoronkov. "Deep biomarkers of human aging: Application of deep neural networks to biomarker development." Aging 8, no. 5 (2016): 1-021.

Wednesday, August 24, 2016

Aging And Drug Discovery Event

Date:Wednesday, 21 & Thursday, 22 September 2016
Room:Shanghai 3
Chairs:Alexander Zhavoronkov, Biogerontology Research Foundation
Brian Kennedy, Buck Institute for Research on Aging
Bhupinder Bhullar, BasePort

Forum Description
Aging lies at the core of every age-related disease and affects every person on the planet. It represents an unbearable toll on the economies of the developed countries resulting in over two trillion dollars in direct medical costs, costs of caring and lost productivity in the US and EU alone. Yet, pharmaceutical companies fail to see the practical applications of aging research as many early experiments with drugs that held promise in slowing the aging processes resulted in commercial failures and write offs. Unlike other events that are geared towards academia or have a broad range of topics, this forum intends to focus on drug discovery and pharmaceuticals that may have a role in postponing the aging processes, preventing the age-related diseases and evaluating the effectiveness of various small molecules with geroprotective properties.
The event will showcase the research projects in aging research to the leaders of the pharmaceutical industry. The symposium will comprise of the four sessions relevant to the drug discovery companies. Each session will be chaired by the top expert in the field.

Wednesday, August 3, 2016

Hormone That Reverses Cell Aging in Humans Identified

Danazol, a synthetic male hormone, reverses cell aging, potentially leading to new treatments to counter diseases caused by cell deterioration.

The human body has hidden secrets scientists are just now discovering. Scientists have known that the human body can heal itself naturally, and now they realize that it can ultimately regenerate dying cells. During a clinical trial, the discovery of a new hormone found in males has shown some promising results in countering the effects of aging. The discovery does not promise a prolonged life-span, but rather a way to help people lead healthier lifestyles.
In later experiments, researchers from the United States and Brazil used a synthetic male hormone, known as a danazol steroid, to arouse the production levels of telomerase, a well- known enzyme. Telomerase is best known for keeping cells young by preventing DNA telomerase cells from shrinking. The process stops the generation of telomerase, and attaches itself to the end of the body’s chromosome.
One of the biggest challenges associated with aging is the rapid decrease of telomerase DNA protection. Every time a cell in the body splits or multiplies, the telomeres increase in length. Eventually, the cell will fail to reproduce itself any longer, and die or naturally age. When telomerase is present, it keeps the telomeres in place, and even aids in the process of cell division.
Finding Can Combat the Negative Impact of Telomerase Degeneration
In past studies, evidence presented shows how aging cells can be stopped by increasing telomerase, which is produced naturally by human cells, and is continually multiplying. This process is similar to blood-forming cells. A lack of telomerase can increase the risk of cancer and have a negative impact on the internal organs. Most recent studies show that prescription steroids are responsible for generating telomerase on demand, confirming what scientists had previously witnessed in the laboratory.
Over a two-year period, a person will lose from 100 to 120 telomere base pairs (DNA building blocks) each year. However, people with telomerase deficiencies could lose from 200 to 600 base pairs over the same course of time. When participants were given the new treatment, the length of telomere cells stopped shrinking, and increased by an average of 386 base pairs. Hemoglobin mass increased, which meant patients no longer needed to rely on blood transfusions.
New Discovery Opens the Door for Future Research
While scientists are optimistic at the possible new treatments and medical breakthroughs, every success comes with a price. The use of sex hormones has notable side effects, such as digestive system problems, fatigue, and mood swings.

Wednesday, July 27, 2016

Despise Growing Old? This Embryonic Gene May Help Fight Ageing

To battle ageing, the human body holds a reservoir of non-specialised cells that can regenerate organs. 

The fountain of youth may reside in an embryonic stem cell gene named Nanog, suggests new research that may lead to treatments for conditions due to reduced bone strength, Alzheimer's and other age-related disorders. 

In a series of experiments at the University at Buffalo in New York, the gene kicked into action dormant cellular processes that are key to preventing weak bones, clogged arteries and other telltale signs of growing old. 

"Our research into Nanog is helping us to better understand the process of ageing and ultimately how to reverse it," said the study's lead author Stelios Andreadis, Professor at the University at Buffalo School of Engineering and Applied Sciences. 

The findings, published in the journal Stem Cells, also showed promise in counteracting premature ageing disorders such as Hutchinson-Gilford progeria syndrome. 

To battle ageing, the human body holds a reservoir of non-specialised cells that can regenerate organs. 

These cells are called adult stem cells, and they are located in every tissue of the body and respond rapidly when there is a need. 

But as people age, fewer adult stem cells perform their job well, a scenario which leads to age-related disorders. 

Reversing the effects of ageing on adult stem cells, essentially rebooting them, can help overcome this proble .. 

Read more at:

Tuesday, June 7, 2016

New Method Seeks To Diminish Risk, Maximize Investment In Cancer 'megafunds'


Recognizing the high research and development costs for drugs to combat cancer, a team of researchers has devised a method to maximize investment into these undertakings by spotting which efforts are the most scientifically viable.
The work centers on "cancer megafunds," or Special Purpose Vehicles (SPVs), in which a collection of investors back a range of research projects, all designed to develop pharmaceuticals to battle cancer. By pooling resources and sponsoring multiple ventures, financial supporters aim to share the high costs of drug development, which include research and clinical trials that can run more than a decade.
However, SPVs mask risks to investors. Among these, as with many "portfolios," are "toxic assets" or "lemons" that threaten the fund by directing resources toward scientifically unsound initiatives.
The challenge, then, is to spot these lemons before too much money has been spent on them -- or too little directed toward more worthwhile studies. In other words, what's the optimal financial strategy to increase the likelihood that an investment is paying off scientifically?
This was the aim of the method, reported in the journal Oncotarget and developed by New York University's Bud Mishra, along with his colleagues and students: Xianjin Yang of Saudi Arabia's King Abdullah University of Science and Technology, Edouard Debonneuil of the University of Lyon, and Alex Zhavoronkov, CEO of InSilico Medicine.
The paper may be downloaded here:
The team analyzed their proposed financial model by mathematical analysis, followed by a series of simulations designed to replicate early-stage investment, which is the most risky portion of this process and when funding is scarce. It used one semester (approximately 15 weeks) as a unit of time and six years as the duration of the drug-development enterprise.
"Ultimately, such an unfortunate outcome could lead the financial markets to completely lose their appetite for megafunds," observes Mishra. "The principles studied here could be helpful: they will strongly improve the yields and risks associated with securitization, but also limit the possibility of hiding defects of the 'lemon' projects."
"The principles introduced in this paper go beyond cancer megafunds and may be applied more broadly, helping finance biomedical research to address a wide range of diseases, including rare diseases, as well as extend into aging and longevity and providing pension funds with new instruments to hedge longevity risk," notes Zhavoronkov.

Keywords: Cancer; SPV; Drug Development; Longevity

Sunday, May 29, 2016

Deep Learning Applied To Drug Discovery And Repurposing

Artificial Neural Nets predict pharmacological properties of small molecules based on transcriptional response signatures in cell lines

Deep learning, frequently referred to as artificial intelligence, a branch of machine learning utilizing multiple layers of neurons to model high-level abstractions in data, has outperformed humans in tasks including image, text and voice recognition, autonomous driving and others, and is now being applied to drug discovery and biomarker development.
In a study published in Molecular Pharmaceutics, a prestigious journal published by the American Chemical Society, scientists from Insilico Medicine in collaboration with Datalytic Solutions and Mind Research Network trained deep neural networks to predict the therapeutic use of large number of multiple drugs using gene expression data obtained from high-throughput experiments on human cell lines.
Deep neural networks outperformed other machine learning techniques and did not result in significant drop in performance as the number of classes increased.
This is the first known application of deep learning to drug discovery using transcriptional response data.
May 26, 2016, Baltimore, MD
-In a recently accepted manuscript titled "Deep learning applications for predicting pharmacological properties of drugs and drug repurposing using transcriptomic data", scientists from Insilico Medicine, Inc located at the Emerging Technology Centers at Johns Hopkins University in collaboration with Datalytic Solutions and Mind Research Network presented a novel approach applying deep neural networks (DNNs) to predict pharmacologic properties of many drugs. In this study, scientists trained deep neural networks to predict the therapeutic use of a large number of drugs using gene expression data obtained from high-throughput experiments on human cell lines. Authors used a sophisticated approach of measuring the differential signaling pathway activation score for a large number of pathways to reduce the dimensionality of the data while retaining biological relevance and used these scores to train the deep neural networks.
"The world of artificial intelligence is rapidly evolving and affecting every aspect of our daily life. And soon this progress will be felt in the pharmaceutical industry. We set up the Pharma.AI division to help pharmaceutical companies significantly accelerate their R&D and increase the number of approved drugs, but in the process we came up with over 800 strong hypotheses in oncology, cardiovascular, metabolic and CNS space and started basic validation. We are cautious about making strong statements, but if this approach works, it will uberize the pharmaceutical industry and generate unprecedented number of QALY", said Alex Zhavoronkov, PhD, CEO of Insilico Medicine, Inc.
Despite the commercial orientation of the companies, the authors agreed not to file for intellectual property on these methods and to publish the proof of concept. Insilico Medicine is currently developing multimodal deep neural networks to predict a broad range of properties of drugs, small molecules and natural compounds for a range of applications including treating common and rare diseases, aging, regenerative medicine and increasing response rates in cancer immunotherapy.
"The field of machine learning have recently witnessed an impressive breakthrough in the area of pattern recognition and computer vision. Deep learning, technology to thank for this, continues to disrupt traditional approaches in many other subfields of machine learning. Originally in the 60s, inspired by how the brain works (at least how we understood it back then) deep learning has now developed into a mature engineering concept. The brain however, does not cease to puzzle researchers and, I am sure, contains more sources of inspiration for the future powerful methodologies.", said Sergey Plis, PhD, Director of Machine Learning at the Mind Research Network and CEO of Datalytic Solutions.
Earlier this month Insilico Medicine scientists published the first deep learned biomarker of human age aiming to predict the health status of the patient in a paper titled "Deep biomarkers of human aging: Application of deep neural networks to biomarker development"by Putin et al, in Aging and an overview of recent advances in deep learning in a paper titled"Applications of Deep Learning in Biomedicine" by Mamoshina et al, also in Molecular Pharmaceutics.
"This study is a proof of concept that DNNs can be used to annotate drugs using transcriptional response signatures, but we took this concept to the next level. We developed a pipeline for in silico drug discovery, which has the potential to substantially accelerate preclinical stage for almost any therapeutic and came up with a broad list of predictions with multiple in silico validation steps that, if validated in vitro and in vivo, can almost double the number of drugs in clinical practice", said Alex Aliper, president of research, Insilico Medicine, Inc and the lead author of the study.
Link to the advance online publication:
Complete article can be read at:

Saturday, May 21, 2016

Artificial Neural Networks Guess Patient's Age With Surprising Accuracy


May 19, 2016, Baltimore, MD - Insilico Medicine, Inc announced that a paper titled "Deep Biomarkers of Human Aging: Application of Deep Neural Networks to Biomarker Development" by Putin, et al, was accepted for publication by Aging, one of the highest-impact journals in aging research on 9th of May, 2016 and today became available online as advance publication at
"It is exciting to see the power of deep learning applied to potential aging biomarkers. The availability of such markers is an essential prerequisite for any future clinical trials to try to ameliorate the effects of human aging", said Charles Cantor, PhD, CSO of Agena, Inc, former director of the Human Genome Project (DOE).
The availability of big data coupled with advances in highly-parallel high-performance computing led to a renaissance in artificial neural networks resulting in trained algorithms surpassing human performance in image and voice recognition, autonomous driving and many other tasks. However, the adoption of deep learning in biomedicine and especially in the pharmaceutical industry has been reasonably slow. In order to outperform more traditional machine learning methods, deep neural nets require large amounts of data and expertise with highly-parallel and high-performance graphics processing unit (GPU) computing.
Evgeny Putin, lead author on the paper commented: "While internally we are working on more sophisticated machine learning problems, Aging.AI is a good example, where DNNs outperform other machine learning methods and can be extended into multiple applications".
Insilico Medicine is working on over a dozen different applications of deep learning methods to regenerative medicine, embryonic development, cross-species comparison and drug discovery and repurposing providing contract research services and developing a range of molecules for cancer, metabolic and CNS pathologies.
To develop a data set of blood biochemistry and cell count samples Insilico Medicine collaborated with the largest independent laboratory test service provider in Eastern Europe, Invitro Laboratories. Scientists of both companies went through over a million samples to select a data set, with the optimal number of features from patients that came for routine blood checkups to build a data set of just over sixty thousand samples. Using this data set Insilco Medicine scientists then trained 40 different deep neural networks (DNNs) of different depth with a single neuron output predicting chronological age and optimized using different optimizers and started organizing these DNNs into an ensemble. Experimentally, 21 DNNs providing optimal performance were organized into an ensemble using a stacking model.
Using the best performing DNN in an ensemble scientists identified most important features contributing to the accuracy of predicting human chronological age: albumin, glucose, alkaline phosphatase, urea and erythrocytes. This finding may be relevant for further studies in biomarkers of aging.
"Inspired by Microsoft's, which can recognize your age using a photograph, an approach we also employ in projects with skincare collaborators, we decided to train an ensemble of deep neural networks on a very large number of simple inexpensive historical blood tests linked to age and sex and built a predictor, which is scalable and can include many other data types to build more comprehensive biomarkers of aging. Aging.AI can in principle be extended as a biomarker of biological aging that can be used to assess the efficacy of various therapies", said Poly Mamoshina, research scientist at the Pharma.AI department of Insilico Medicine, Inc.
Read complete article at :