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

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

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

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

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About Insilico Medicine 

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

CTFH and Insilico collaborate on accelerating first-in-class therapeutics using AI

Research Collaboration Highlights

• The collaboration aims to accelerate drug discovery and development with an AI-enabled platform for triple-negative breast cancer;
• CTFH is one of the top pharmaceutical companies in China focused on innovative drug discovery and development, with a strong internal R&D team and integrated R&D capabilities;
• The project value is up to $200 million, including an upfront payment, milestone payments, and royalties based on the net product sales;
• CTFH has achieved significant milestones in first-in-class drug development. With this partnership, CTFH will generate novel molecules with specified properties using Insilico's next-generation AI platform;
• This partnership will speed up the R&D process, reduce the cost and ultimately benefit the patients;

HONG KONG, and NANJING, CHINA - October 9, 2019 (12PM London time) - Insilico Medicine, a company developing an end-to-end drug discovery pipeline utilizing the latest advances in deep learning, has entered into a two-program research collaboration agreement with Jiangsu Chia Tai Fenghai Pharmaceutical Co., Ltd. ("CTFH"), taking on previously undruggable targets. Insilico Medicine will be eligible to receive up to $200 million for the achievement of milestone payments and royalties based on the net sales on the products from the collaboration. This partnership is expected to accelerate drug discovery and development with an AI-enabled platform for triple-negative breast cancer. 

CTFH is an active adopter of state-of-the-art technologies and has achieved significant milestones in first-in-class drug development. Wenyu Xia, General Manager of CTFH, said: "We are very pleased to establish the partnership with Insilico Medicine, entering the new era of AI-enabled drug development. We look forward to a long-term partnership with Insilico Medicine. As the premier AI drug discovery company in the industry, Insilico Medicine has demonstrated capabilities to generate novel molecules with specified properties using its next-generation AI platform. We believe that this collaboration will speed up the R&D process, reduce the cost and provide greater benefits to patients."

Last month, Insilico Medicine published a landmark paper in Nature Biotechnology, demonstrating the application of its generative tensorial reinforcement learning systems in the generation of novel molecules for simple kinases in 46 days, including experimental validation. It also announced a $37 million round led by prominent biotechnology and AI investors.

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About CTFH:

Jiangsu Chia Tai Fenghai Pharmaceutical Co., Ltd. (CTFH) is an integrated pharmaceutical company with capabilities in R&D, production, sales. It is a joint venture between the Chia-Tai Group (CP Pharmaceutical), a world top 500 company, and Jiangsu Agriculture Reclamation Group. CTFH has developed several finished dosages and active pharmaceutical ingredients including a new formulation of sugar infusions and injections, gastrointestinal, cardiovascular, respiratory, neuropsychopathy and oncology medicines, etc. CTFH focuses on advanced R&D technologies and has established long-term relationships with many companies to provide quality services. CTFH strives to become a global leader in the pharmaceutical industry with a relentless pursuit of high product quality, the trust of customers, and benefits for patients.

About Insilico Medicine

Insilico Medicine is an artificial intelligence company headquartered in Hong Kong, with offices in six countries and regions. The Company was the first to apply the generative adversarial networks (GANs) and reinforcement learning (RL) to generate new molecular structures with the specified parameters in 2015. In addition to collaborating with large pharmaceutical companies, Insilico Medicine is also pursuing internal drug discovery programs in different disease areas and anti-aging fields. Recently, Insilico Medicine published some of the results in Nature Biotechnology and secured $37 million in series B funding, led by Qiming Venture Partners, joined by Eight Roads, F-Prime Capital, Lilly Asia Ventures, Sinovation Ventures, Baidu Ventures, Pavilion Capital and BOLD Capital Partners. Website: http://insilico.com/

Insilico Medicine Secures $37M in Series B Funding Led by Qiming Venture Partners

HONG KONG, Sept. 10, 2019 /PRNewswire/ -- Insilico Medicine, a pioneer in next-generation artificial intelligence technology for drug discovery, recently completes a $37 million funding round led by Qiming Venture Partners, joined by Eight Roads, F-Prime Capital, Lilly Asia Ventures, Sinovation Ventures, Baidu Ventures, Pavilion Capital, BOLD Capital Partners and other investors including series A investors.

The Series B funding will be used to commercialize the validated generative chemistry and target identification technology. The company will also build up a senior management team with the experience in the pharmaceutical industry, further develop its pipeline in cancer, fibrosis, NASH, immunology and CNS for the purposes of partnering with the pharmaceutical companies on specific therapeutic programs.

Insilico Medicine has developed and validated a comprehensive drug discovery pipeline which includes a state-of-the-art molecular generator utilizing multiple proprietary generative and reinforcement learning technologies. The company identified promising targets in a variety of therapeutic modalities including cancer, fibrosis, NASH, immunology and CNS. Through a network of joint ventures, partnerships with early-stage biotechnology and large pharmaceutical companies, Insilico Medicine is powering the new digital-age biopharmaceutical industry.

"We are excited to lead the current round of financing in Insilico Medicine," says Nisa Leung, Managing Partner of Qiming Venture Partners. "The company is an industry leader in the AI-powered drug discovery vertical. We look forward to seeing it shortening the time for drug discovery and creating synergies with our portfolio companies."

The company is powering a network of biotechnology, pharmaceutical companies and academic institutions. Since inception the company published or co-published over 70 papers in peer-reviewed journals and artificial intelligence conferences. In its latest research paper published in Nature Biotechnology Insilico demonstrated animal validation of novel molecules generated using the deep generative tensorial reinforcement learning models in human cells and in animals.

For further information, images or interviews, please contact: sherry.zhang@qimingvc.com

About Qiming Venture Partners

Founded in 2006, Qiming Venture Partners is a leading China venture capital firm with offices in Shanghai, Beijing, Suzhou, Shenzhen, Hong Kong, Seattle, Boston and San Francisco Bay Area. Currently, Qiming Venture Partners manages seven US Dollar funds and five RMB funds with over US$4 billion assets under management.

Qiming Venture Partners strives to be the investor of choice for top entrepreneurs in China. Since its debut, the firm has backed over 310 fast-growing and innovative companies across China in the TMT, healthcare sectors. Over 60 companies are already listed on NYSE, NASDAQ, Shanghai Stock Exchange, Shenzhen Stock Exchange, HKEx and Gretai Securities Market or achieved exit through M&A. There are about 30 portfolio companies that have been recognized as unicorns in the industry.

About Insilico Medicine

Insilico Medicine is an artificial intelligence company headquartered in Hong Kong, with R&D and management resources in six countries sourced through hackathons and competitions. The company and its scientists are dedicated to transforming the pharmaceutical industry by developing and applying the next-generation deep learning approaches to every step of the drug discovery and drug development process. The company is constantly collaborating with the most innovative biopharmaceutical companies with disease-relevant assays to validate its solutions and generate high-quality machine-learnable data.

Since 2015 Insilico Medicine is developing a broad range of generative adversarial networks (GANs) and reinforcement learning approaches to identify novel protein targets, generate novel molecular structures with specified properties and generate synthetic data.

Website: http://insilico.com/

Michael A. Petr to present at the 6th Annual Aging Research, Drug Discovery, and AI Forum in Basel

Thursday, September 5, 2019 - Today the Biogerontology Research Foundation, a leading UK non-profit foundation dedicated to increasing healthy longevity and promoting advances in ageing research, Insilico Medicine, a biotechnology company developing the end-to-end drug discovery pipeline utilizing next generation artificial intelligence, and the Scheibye-Knudsen Lab, University of Copenhagen, announce the presentation of Michael A.Petr, PhD Candidate, The University of Copenhagen, at the 6th Aging Research, Drug Discovery, and AI Forum during the Basel Life Congress, September 10-12, 2019 in Basel, Switzerland.

Now in its 6th year, the Aging Research, Drug Discovery and AI Forum was founded and chaired consecutively by Alex Zhavoronkov, Founder and Chief Science Advisor for the Biogerontology Research Foundation and CEO of Insilico Medicine.

Cockayne syndrome (CS) is a devastating rare genetic disease characterized by progressive neurodegeneration and premature aging, among other clinical symptoms. The disease is caused by the DNA repair genes ERCC6 (CSB) or ERCC8 (CSA) mutation, and currently there is no treatment for these patients. Our previous work shows that molecules (such as acetyl-CoA and citrate), participating in protein and carbohydrate metabolism, are decreased in models of this disease suggesting that these may be key metabolites in Cockayne syndrome and perhaps aging. 

To test this hypothesis we have knocked out CSB and INDY, the citrate transporter, in human cell lines, fruit flies and mice and subsequently investigated aging phenotypes in these models. The results of the study suggested that the loss of citrate transportation exacerbates Cockayne syndrome phenotypes across species and that ketones replenishment may prevent these changes. Our findings support the idea that the intermediary metabolism may be a key regulator of the aging process and, most importantly, may be a target for interventions.

"This research is essential to understand the specific mechanism with which the ketogenic diet delays brain aging and to justify the diet's translation into the clinic for premature and normal aging patients," said Michael A.Petr, PhD Candidate, the University of Copenhagen. 

"I am extremely thrilled that Michael A. Petr will join us this year in Basel. Michael A. Petr is a talented young scientist at the Center for Healthy Aging, University of Copenhagen, Denmark, who combines computational approaches with wet-lab experimentation. Recently, he has developed a computer vision based high throughput lifespan machine allowing the testing of hundreds of conditions in parallel. I am therefore very excited to hear about Michael's approach,"said Dr. Daniela Bakula, University of Copenhagen.

"Over the last 5 years, the "Ageing & Drug Discovery" and "AI for Healthcare" forums have been leading events at Basel Life, attracting hundreds of delegates from over 50 countries. This year, we are combining the 2 platforms into a 3 day-event titled "the 6th Ageing, AI and Drug Discovery Forum" to explore the convergence of these 2 cutting edge disciplines. Under the program leadership of Professor Morten Scheibye-Knudsen and Dr. Alex Zhavoronkov, with distinguished scientists and industry experts in the field, we look forward to exploring breakthroughs for this great healthcare need for the planet," said Dr. Bhupinder Bhullar, Chair, Innovation Forum program committee, Basel Life 2019. 

"The 6th annual Ageing Research, Drug Discovery, and AI Forum at Basel Life will have a fresh program featuring some of the most prominent scientists and industry players in ageing and longevity research covering the theory, applications and convergence of these three exciting areas," said Alex Zhavoronkov, Ph.D., Founder and Chief Scientific Advisor of the Biogerontology Research Foundation and CEO of Insilico Medicine, Inc.

The 6th Ageing Research, Drug Discovery, and AI Forum Basel will bring together leaders in the ageing, longevity, and drug discovery field, to describe the latest progress in the molecular, cellular and organismal basis of ageing and the search for interventions. Furthermore, the forum will include opinion leaders in AI to discuss the latest advances of this technology in the biopharmaceutical sector and how this can be applied to interventions. This event intends to bridge academic and commercial research and foster collaborations that will result in practical solutions to one of humanity's most challenging problems: ageing. The Forum will be held in Basel, Switzerland, September 10-12, 2019. 

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About Michael A.Petr

Michael Petr holds a bachelors in Biomedical Engineering from North Carolina State Univeristy, USA, and throughout his bachelors worked in companies like United Therapeutics and GSK. After his bachelors, was a research fellow in Rafael de Cabo's lab at the National Institute on Aging in Baltimore, MD where he comprehensively phenotyped aging mice in vivo with an array of preclinical imaging, computer vision and metabolic readouts. Currently he is a PhD candidate in Morten Scheibye-Knudsen's at the Center for Healthy Aging, University of Copenhagen studying the mechanism of interventions for aging and developing automated phenotyping technologies. His works attempts to bridge automation tech to comprehensive phenotyping of models orgnanisms for rapid readout of aging effects. Outside of his research Michael is active with Engineers Without Borders.

About the Biogerontology Research Foundation

The Biogerontology Research Foundation is the UK's leading non-profit focused on Longevity, supporting ageing research and multiple initiatives relating to advancing Healthy Longevity and expediting the coming paradigm shift from disease treatment to personalized precision prevention. It was the main initial donor that provided financial and organisational support to Longevity International UK for the purpose of establishing the UK All-Party Parliamentary Group for Longevity. It was also actively involved in the successful initiative of adding a new extension code for "ageing-related diseases" accepted in 2018 by the World Health Organization during the last revisions of its International Classification of Diseases framework.

About the University of Copenhagen

With over 40,000 students and more than 9,000 employees, the University of Copenhagen is the largest institution of research and education in Denmark and among the highest ranked universities in Europe. The purpose of the University - to quote the University Statute - is to 'conduct research and provide further education to the highest academic level'. Approximately one hundred different institutes, departments, laboratories, centres, museums, etc., form the nucleus of the University. University Website: http://introduction.ku.dk/presentation/

About Insilico Medicine

Insilico Medicine is an artificial intelligence company headquartered in Hong Kong, with R&D and management resources in Belgium, Russia, UK, Taiwan, and Korea sourced through hackathons and competitions. The company and its scientists are dedicated to extending human productive longevity and transforming every step of the drug discovery and drug development process through excellence in biomarker discovery, drug development, digital medicine, and ageing research. In 2017, NVIDIA selected Insilico Medicine as one of the Top 5 AI companies in its potential for social impact. In 2018, the company was named one of the global top 100 AI companies by CB Insights. In 2018 it received the Frost & Sullivan 2018 North American Artificial Intelligence for Ageing Research and Drug Development Award accompanied with the industry brief. Brief company video: https://www.youtube.com/watch?v=l62jlwgL3v8. 

Official website: http://insilico.com

About the Scheibye-Knudsen Laboratory

The growing proportion of the elderly population represents an increasing socioeconomic challenge, not least because of age-associated diseases. It is therefore increasingly pertinent to find interventions for age-associated diseases such as Alzheimer's, Parkinson's and cardiovascular diseases. Although the cause of ageing is currently unknown accumulation of damage to our genome, the DNA, may be a contributing factor. In the Scheibye-Knudsen lab we try to understand the cellular and organismal consequences of DNA damage with the aim of developing interventions. We have discovered that DNA damage leads to changes in certain metabolites and that replenishment of these molecules may alter the rate of ageing in model organisms. These findings suggest that normal ageing and age-associated diseases may be malleable to similar interventions. The hope is to develop interventions that will allow everyone to live healthier, happier and more productive lives.

Laboratory website: http://scheibye-knudsen.com/

About Basel Life 2019 

Ageing Research for Drug Discovery Forum description 

In this symposium, leaders in the ageing, longevity, and drug discovery field will describe the latest progress in the molecular, cellular and organismal basis of ageing and the search for interventions. Furthermore, the forum will include opinion leaders in AI to discuss the latest advances of this technology in the biopharmaceutical sector and how this can be applied to interventions. This event intends to bridge academic and commercial research and foster collaborations that will result in practical solutions to one of humanity's most challenging problems: ageing. 

A panel of thought-leaders will give us their cutting edge reports on the latest progress in our quest to extend the healthy lifespan of everyone on the planet. 

Conference Official Website: https://www.basellife.org/2019.html

Ageing research to accelerate with experimental validation in AI-powered drug discovery

Biogerontology Research Foundation scientists have developed and experimentally-validated a new AI engine to power the longevity industry worldwide

Wednesday, September 4th, 2019, London, UK: The Biogerontology Research Foundation salutes its Founder and Chief Scientific Advisor Alex Zhavoronkov on leading a team of researchers who have succeeded to use Artificial Intelligence to design, synthesize and validate a novel drug candidate in just 46 days, compared to the typical 2-3 years required using the standard hit to lead (H2L) approach used by the majority of pharma corporations.

By using a combination of Generative Adversarial Networks (GANs) and Reinforcement Learning (RL), the team of Insilico Medicine researchers behind this study (documented in a paper published in Nature Biotechnology this month) have succeeded in validating the real power that AI has to expedite timelines in drug discovery and development, and to transform the entire process of bringing new drugs to market from a random process rife with dead ends and wrong turns to an intelligent, focused and directed process, that takes into account the specific molecular properties of a given disease target into account from the very first step.

The Biogerontology Research Foundation has collaborated with Insilico Medicine on a number of projects and studies, and has long advocated for the extreme potentials that AI has in terms of making the process of discovering and validating new drugs a faster and more efficient process, especially as it pertains to aging and longevity research and the development of drugs capable of extending human healthspan and compressing the incidence of age-related disease into the last few years of life.

While this is the newest in a long line of steps and accomplishments aiming to turn the theoretical potentials of AI for longevity research into practice, it is also the largest step made thus far, and goes a very long way in terms of proving that potential via hard science. 

"This newest achievement made by Insilico Medicine, a leading AI for drug discovery and longevity company and an official partner of Ageing Research at King's, demonstrates the truly disruptive potential that AI holds in terms of accelerating the pace of progress in drug discovery. Furthermore, this is just the latest step in a much grander agenda of applying AI for ageing and longevity R&D, and to the accelerated translation of that research into real-world therapies for human patients. It is also quite notable that the team released the code behind their algorithm in an open-source format, allowing other researchers to apply their techniques and build upon their achievements for the advancement of the entire field of AI for drug design, ageing research and longevity" said Richard Siow, Ph.D., Director of Ageing Research at King's College London and former Vice-Dean (International), Faculty of Life Sciences & Medicine, King's College London.

It is the hope of the Biogerontology Research Foundation that this study motivates additional researchers to harness the potential for AI in longevity research, and provides incentives for larger drug developers to begin on-boarding AI into their drug discovery and development programs, in order to expedite the time it takes to bring life-saving drugs into the hands of real patients.

The Biogerontology Research Foundation also salutes the team's decision to release the code behind the GAN-RL method to the public in a freely-available open-source format, so that other researchers have the power to take this approach and apply it to their own work.

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Reference to the paper:

Alex Zhavoronkov, Yan A. Ivanenkov, Alex Aliper, Mark S. Veselov, Vladimir A. Aladinskiy, Anastasiya V. Aladinskaya, Victor A. Terentiev, Daniil A. Polykovskiy, Maksim D. Kuznetsov, Arip Asadulaev, Yury Volkov, Artem Zholus, Rim R. Shayakhmetov, Alexander Zhebrak, Lidiya I. Minaeva, Bogdan A. Zagribelnyy, Lennart H. Lee Tao Guo, Ala?n Aspuru-Guzik, 2019, Deep learning enables rapid identification of potent DDR1 kinase inhibitors, Nature Biotechnology

DOI: 10.1038/s41587-019-0224-x

Web link: https://www.nature.com/articles/s41587-019-0224-x

About the Biogerontology Research Foundation

The Biogerontology Research Foundation is the UK's leading non-profit focused on Longevity, supporting ageing research and multiple initiatives relating to advancing Healthy Longevity and expediting the coming paradigm shift from disease treatment to personalized precision prevention. It was the main initial donor that provided financial and organisational support to Longevity International UK for the purpose of establishing the UK All-Party Parliamentary Group for Longevity. It was also actively involved in the successful initiative of adding a new extension code for "ageing-related diseases" accepted in 2018 by the World Health Organization during the last revisions of its International Classification of Diseases framework.

Novel molecules designed by artificial intelligence in 21 days are validated in mice

The 2nd of September, London, Insilico Medicine, a global leader in artificial intelligence for drug discovery, today announced the publication of a paper titled, “Deep learning enables rapid identification of potent DDR1 kinase inhibitors,” in Nature Biotechnology. The paper describes a timed challenge, where the new artificial intelligence system called Generative Tensorial Reinforcement Learning (GENTRL) designed six novel inhibitors of DDR1, a kinase target implicated in fibrosis and other diseases, in 21 days. Four compounds were active in biochemical assays, and two were validated in cell-based assays. One lead candidate was tested and demonstrated favorable pharmacokinetics in mice.

The traditional drug discovery starts with the testing of thousands of small molecules in order to get to just a few lead-like molecules and only about one in ten of these molecules pass clinical trials in human patients. Even a slight improvement in the time it takes to discover new drugs or in the probability of success results in significant savings and public benefit.

The authors of the paper pioneered the field of generative chemistry with seminal publications in 2016 and experimental validation of the molecules generated by GENTRL represents a valuable milestone on the path to more efficient drug discovery powered by artificial intelligence. 

Insilico Medicine is developing a comprehensive drug discovery pipeline utilizing artificial intelligence generating novel molecules with the specified properties for a variety of target classes and challenging targets with and without crystal structure rapidly generating leadlike hits. This pipeline was specifically developed to rapidly validate prospective targets with small-molecule chemistry and allow for rapid pharmaceutical drug discovery.

“This paper is certainly a really impressive advance and likely to be applicable to many other problems in drug-design.  Based on state-of-the-art reinforcement learning, I am also very impressed by the breadth of this study involving as it does molecular modeling, affinity measurements, and animal studies”, said Dr. Michael Levitt, professor of structural biology, Stanford University. Dr. Levitt received the Nobel Prize in Chemistry in 2013.

"I interacted with many AI startups in the past and Insilico was the only deep learning company with impressive, demonstrated capabilities integrating target identification and small molecule discovery. They did a lot of theoretical work in GANs from the very beginning and this experimental validation is a significant demonstration that this technology may improve and accelerate drug discovery", said Dr. John Baldoni, CTO of a stealth AI-powered drug development startup and former SVP of Platform Technology and Science at GSK.

“The generative tensorial reinforcement learning in this paper substantially advances the efficiency of biochemistry implementation in drug discovery. Yet to be further experimented at scale, this method signals a breakthrough of pharmaceutical artificial intelligence at industrial level, and may bring significant social and economic impact to our society”, said Dr. Kai-Fu Lee, founder of Sinovation Ventures, former executive of Microsoft and Google, and the original inventor of multiple AI technologies.

"I met Alex when working at OpenAI and have been excited to see him pioneer the use of GANs/RL for the pharmaceutical industry since 2016. One major criticism of GANs is that their usefulness has been limited to image editing applications, so I'm glad that Alex and his team are finding ways to use them for molecular generation", said Dr. Ian Goodfellow, the original inventor of Generative Adversarial Networks (GANs)

"This technology builds on our early work on adversarial and generative neural networks since 1990. Insilico has been working on generative models for drug discovery since 2015, and I am happy to see that their GENTRL system produced molecules that were experimentally validated in cells and in mice. AI will have a transformative effect on the pharmaceutical industry, and we need more experimental validation results to accelerate progress", said Dr. Jürgen Schmidhuber, a professor at IDSIA, co-founder of NNAISENSE, and the original inventor of many core techniques and initial concepts in the field of artificial intelligence.

“Reduction of cycle time and overall cost of goods is critical to the future success of Pharma drug discovery activities. In this paper, Insilico highlight a novel AI based technology (GAN-RL) which allowed them to identify lead molecules with efficacy in animal models in notably short timeframes. If this technology proves broadly useful it may well have transformational potential for future lead generation efforts”, said Dr. Stevan Djuric, Adjunct Professor, School of Pharmacy, High Point University and former Vice President, Discovery Chemistry and Technology, Abbvie.

“Much hyperbole exists about the promise of artificial intelligence (AI) in improving medical care and in the development of new medical tools. Here however is a paper “Deep learning enables rapid identification of potent DDR1 kinase inhibitors” recently published in Nature Biotechnology that describes an application of  AI in drug discovery that is indeed important. A new drug candidate was proposed and tested preclinically in a remarkably short period of time. The results are significant for two reasons. The AI procedures replaced the role normally played by medicinal chemists, and these individuals are in limited supply. The acceleration in rate translates into longer patent coverage that improves the economics of drug development. If this approach can be generalized it could become a widely adopted method in the pharmaceutical industry”,  said Dr. Charles Cantor, a professor at Boston University, co-founder of Retrotope, Inc, and former Chief Scientist of the Human Genome Project with the US Department of Energy.

“This paper is a significant milestone in our journey towards AI-driven drug discovery. We work in generative chemistry since 2015 and when Insilico's and Alán's theoretical papers were published in 2016 everyone was very skeptical. Now, this technology is going mainstream and we are happy to see the models developed a few years ago and producing molecules against simpler targets being validated experimentally in animals. When integrated into comprehensive drug discovery pipelines, these models work for many target classes and we work with the leading biotechnology companies to push the limits of generative chemistry and generative biology even further”, said Alex Zhavoronkov, PhD, the founder and CEO of Insilico Medicine, the lead author of the study.

Reference to the paper:

Alex Zhavoronkov, et, al, Deep learning enables rapid identification of potent DDR1 kinase inhibitors, Nature Biotechnology, DOI: 10.1038/s41587-019-0224-x

Link to the paper:

https://www.nature.com/articles/s41587-019-0224-x

About Insilico Medicine

For further information, images or interviews, please contact:

Contact: Connie Zheng, connie@czcomms.com

Insilico Medicine is an artificial intelligence company headquartered in Hong Kong, with R&D and management resources in Greater China, USA, Russia, UK, and Korea sourced through hackathons and competitions. The company and its scientists are dedicated to transforming the pharmaceutical industry by developing and applying the next-generation deep learning approaches to every step of the drug discovery and drug development process.  The company is constantly collaborating with the most innovative biopharmaceutical companies with disease-relevant assays to validate its solutions and generate high-quality machine-learnable data.

Since 2015 Insilico Medicine is developing a broad range of generative adversarial networks (GANs) and reinforcement learning approaches to identify novel protein targets, generate novel molecular structures with specified properties and generate synthetic data. In addition to working collaborations with the large pharmaceutical companies, the company is pursuing internal drug discovery programs in a range of cancers, fibrosis, CNS diseases, sarcopenia, metabolic diseases,  dermatological diseases, and senescence.

In 2015 Insilico Medicine became the finalist of the NVIDIA Early Stage Challenge and 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 and received the Frost & Sullivan 2018 North American Artificial Intelligence for Aging Research and Drug Development Award accompanied with the industry brief.

Website: http://insilico.com/