Insilico Medicine and Arctoris to collaborate on COVID-19 by combining robotics and AI

Insilico Medicine and Arctoris announced a technology partnership to jointly discover and profile new therapeutics against COVID-19 by combining robotics and AI

Thursday, 9th of July - Insilico Medicine, a biotechnology company developing an end-to-end drug discovery pipeline utilizing next-generation artificial intelligence, today announced a technology partnership with Arctoris, the world's first fully automated drug discovery platform, offering remotely accessible, pre-optimised and fully validated R&D processes for its partners and clients globally. The partnership enables the two companies to combine their unique strengths in AI-guided drug discovery for target identification and generative chemistry on the one hand, and robotics for rapid generation of high-quality cell-based, molecular and biochemical data on the other. The partnership propels the two companies' abilities to discover and validate novel molecules faster.

Contributing their strengths to the fight against COVID-19, the two companies utilized their unique state-of-the-art techniques to discover, synthesize, and profile a set of inhibitors for COVID-19 treatment. Insilico identified novel small molecules using its AI capabilities, with Arctoris rapidly evaluating the intended biological actions on its robotic platform. In a develop-test-refine loop, the partners iterated new molecules several times faster than the industry standard, providing unprecedented prospects for new drug discovery successes. The JAK inhibitor study profiled a set of potent inhibitors that can help patients by modulating the life-threatening cytokine storm caused by COVID-19.

"We are excited to announce our technology partnership with Insilico Medicine, a world-leader in AI-based drug discovery. We see tremendous value in combining our next-generation robotic platform with Insilico's unique capabilities in drug design. During the COVID-19 pandemic, time is of the essence, and this project demonstrates that together, we are able to accelerate the drug discovery process, and create and progress new drug candidates faster and cheaper," said Martin-Immanuel Bittner MD DPhil, Co-Founder & CEO of Arctoris.

"Arctoris developed an advanced robotics platform that can be used for a very broad spectrum of applications in drug discovery and biological data generation. We are very happy to partner with this Oxford-based company on our COVID-19 program. We discussed the collaboration for quite some time but this pandemic really highlighted the benefits of a close AI- and robotics- powered discovery integration," said Alex Zhavoronkov, PhD, founder, and CEO of Insilico Medicine.

Insilico and Arctoris have already been working together on BioTarget, a collaborative effort supported by Cancer Research UK to find new molecules for cancer treatment from partners globally, crowdsourcing the drug discovery process and making leading experimental tools accessible to researchers worldwide, thereby democratising the drug discovery ecosystem.

Further collaboration will focus on joint drug discovery projects in oncology, a field of particular interest to both Arctoris and Insilico, with their mission to eliminate age-related disease and promote healthy longevity.

For further information, images or interviews, please contact: ai@insilico.com.

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About Insilico Medicine

Since 2014 Insilico Medicine is focusing on generative models, reinforcement learning (RL), and other modern machine learning techniques for the generation of new molecular structures with the specified parameters, generation of synthetic biological data, target identification, and prediction of clinical trials outcomes. Recently, Insilico Medicine secured $37 million in series B funding. Since its inception, Insilico Medicine raised over $52 million, published over 80 peer-reviewed papers, applied for over 25 patents, and received multiple industry awards. Website http://insilico.com/.

For further information, images or interviews, please contact: Sian Marshall, Head of Office, media@arctoris.com

About Arctoris

Arctoris Ltd is an Oxford-based research company that is revolutionising drug discovery for biotechnology companies, pharmaceutical corporations and academia. Arctoris has established the world's first fully automated drug discovery platform, offering pre-optimised and fully validated R&D processes for its partners and customers globally. Accessible remotely, the platform provides on-demand access to a wide range of biochemical, cell biology and molecular biology assays conducted by robotics, enabling rapid, informed decision-making in basic biology, target validation, toxicology and phenotypic screening. These assay capabilities are accessed using a powerful online portal that streamlines experiment planning, ordering, tracking and data analysis. Thanks to the Arctoris platform, partners and clients can rapidly, accurately and cost-effectively perform their research and advance their drug discovery programmes.

Can your gut microbes tell you how old you really are?

Highlights:

In 2018 scientists from Gladyshev lab specializing in aging research started a collaboration with Insilico Medicine resulting in a widely-publicized proof of concept microbiomic aging clock ; The clock has been validated on multiple independent data sets;
The clock was shown to be biologically-relevant and used to demonstrate that diabetic patients look older than their chronological age;
The study was published in iScience and is expected to be used in new data analysis tools for COVID-19 and longevity research;

June 11th, 2020 - Recent advances in deep learning have allowed AI algorithms to outperform humans in image, text, and voice recognition. One particular use for AI in biology is deep aging clocks. Deep aging clocks are trained on large samples to predict human biological age using different data types, such as: pictures, videos, voice, blood biochemistry, gene and protein expression, and MRI. In a study recently published in iScience, Harvard and Insilico Medicine scientists used thousands of whole genome sequencing samples from gut bacteria to develop and validate a new deep microbiomic aging clock. This new tool indicates that the age of the host is a significant contributor to the gut community dynamics.

Over the last decade human gut microbiome studies have produced multiple surprising results. The bacteria in our gut are now known to be major contributors to the immune function, brain development and activity, central metabolism, obesity pathogenesis and many other processes. The growing realization of the role microbiota plays in human health makes it essential to understand what factors shape gut communities and how to manipulate them.
Such factors include the mode of birth, diet, physical activity and age. The effect age elicits on microflora dynamics is much better understood for the early stages of life. During the first year of life all people are much more similar in terms of diet and behaviour, compared to adults. Consequently, their gut flora goes through clearly defined stages. But upon transitioning to adulthood, multiple confounders such as diet, tobacco and alcohol consumption, and level of physical activity make individual microfloras extremely diverse. The NIH Human Microbiome Project has shown that there is no core community in adult guts, although the various combinations of microbial species tend to have similar functions and metabolic capabilities.

Multiple studies have identified some age-related trends in gut microflora. However, the findings usually have unclear general applicability due to localized sampling. In a joint project between Insilico Medicine and the laboratory of Vadim Gladyshev at Brigham and Women's Hospital and Harvard Medical School, the data from 13 public studies on human gut microbiome were aggregated to explore the possibility of developing an aging clock based on the microflora relative abundance profiles.

The initial attempt to predict chronological age based on gut community species composition was published in BioRxiv in December 2018. Since then the team further improved their approach and recently published their results in the iScience journal. More than 1100 species-level microflora compositions were used to train a Deep Neural Network in a cross-validated manner. The resulting ensemble predicts hosts' age in an independent data set collection with a mean error of 5.9-6.8 years.

The published intestinal age predictor proves that there are microflora succession patterns associated with age progression in the adult. The described workflow can be used to recreate similar models with data from other platforms and explore the effect of specific bacterial taxa on the course of human aging in a more controlled setting. The authors also suggest the ways to identify the microbes with potential to accelerate or slow down aging.

"We are happy to collaborate with the Gladyshev lab on this new microbiomic aging clock, which is the first of its kind. The development of this clock was a long and tedious journey as we originally thought that it would be impossible to build and after the demonstration of the first proof of concept, it took two years to refine and validate. We hope that the demonstrated approach will be used for COVID-19 research and later for longevity research for tracking the effects of different interventions and foods on the predicted intestinal age", said Alex Zhavoronkov, PhD, CEO of Insilico Medicine.

The reported aging clock can be accessed at aging.AI. Insilico Medicine aims to continue developing microbiomic tools and is planning to release COVIDOMIC -- a tool for exploring variables with an effect on the COVID-19 infection outcome, including those derived from patients' respiratory microbiome.

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Read the original research paper here: https://www.sciencedirect.com/science/article/pii/S2589004220303849

About Insilico Medicine Since 2014 Insilico Medicine is focusing on generative models, reinforcement learning (RL), and other modern machine learning techniques for the generation of new molecular structures with the specified parameters, generation of synthetic biological data, target identification, and prediction of clinical trials outcomes. Recently, Insilico Medicine secured $37 million in series B funding. Since its inception, Insilico Medicine raised over $52 million, published over 80 peer-reviewed papers, applied for over 25 patents, and received multiple industry awards. Website http://insilico.com/.
For further information, images or interviews, please contact: ai@insilico.com.

Scientists use machine learning to predict major clinical forms of drug cardiotoxicity

June 9, 2020 - We announce the publication of a new research paper titled 'Dual transcriptomic and molecular machine learning predicts all major clinical forms of drug cardiotoxicity' in Frontiers in Pharmacology. The study was conducted in a collaboration between the Computational Cardiovascular Science Group of the University of Oxford and Insilico Medicine.

'Drug-induced adverse effects on the heart are a very important problem, as highlighted recently in the news regarding COVID-19 treatments. In this study, we are very excited to show how our machine learning algorithm can identify drugs that can cause 6 potential forms of cardiac adverse outcomes from gene expression data', said Professor Blanca Rodriguez.

'Thanks to the increasing power of computers and algorithms to learn, this work represents an exemplar of how AI will revolutionise the future of drug development and safety evaluation in the pharma industry. It extends previous efforts in the field to predict not only the likelihood of a drug to induce lethal arrhythmias, but all the main cardiac adverse events associated with drug action. It also establishes the need for stringent testing criteria for the effective application of AI to this critical domain of the life sciences', said Professor Alfonso Bueno-Orovio.

Computational methods can increase the productivity of drug discovery pipelines, through overcoming challenges such as cardiotoxicity identification. In this paper, researchers demonstrated the simultaneous prediction of cardiotoxic relationships for six drug-induced cardiotoxicity types using a machine learning approach on a large collected and curated dataset of transcriptional and molecular profiles. The algorithm generality is demonstrated through validation in an independent drug dataset, in addition to cross-validation.

Alex Zhavoronkov, founder and CEO of Insilico Medicine comments, 'Drug-induced cardiotoxicity is one of the reasons for late-stage clinical trial failures. We see the Rodriguez group at Oxford as the world's main source of accurate cardiotoxicity predictors. The results of their work are adopted by the FDA, and many pharmaceutical companies. We are very happy to collaborate on AI-powered multi-omics cardiotoxicity prediction engines, and have one of our top AI scientists, Polina Mamoshina, defend her doctorate under one of the biggest names in computational biomedicine'.

Polina Mamoshina, is now Senior Research Scientist at Insilico Medicine. She comments, 'In silico or computational models have made great progress in past years. And one of their great features is that they can be humanized and so have increased chances for translation into drug discovery and development pipelines. The scope of this work was to predict drug adverse reactions that were shown in humans. We believe that this work can be extended to side effects manifested in other organs and tissues and that pipeline that we proposed provides a valuable benchmark for future studies'.

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Read the original research paper here: https://ora.ox.ac.uk/objects/uuid:2b143ed7-9630-4802-b707-9fb226203384?fbclid=IwAR2bzf3SdQVSzGge3PeB4DBkzSqU55wK4tDRcaTNmvMoNjg5izuNd_dkiFU
 
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For further information, images or interviews, please contact: ai@insilico.com

About Insilico Medicine
 
Since 2014 Insilico Medicine is focusing on generative models, reinforcement learning (RL), and other modern machine learning techniques for the generation of new molecular structures with the specified parameters, generation of synthetic biological data, target identification, and prediction of clinical trials outcomes. Since its inception, Insilico Medicine raised over $52 million, published over 70 peer-reviewed papers, applied for over 20 patents, and received multiple industry awards. Website http://insilico.com/

New artificial intelligence model to bridge biology and chemistry

Generative biology meets generative chemistry: Bidirectional conditional autoencoder to generate novel molecular structures for the desired transcriptional response

May 19th, 2020, Hong Kong - Insilico Medicine announces the publication of a new research paper titled "Molecular Generation for Desired Transcriptome Changes With Adversarial Autoencoders" in Frontiers in Pharmacology. This is the first study of this kind where novel molecular structures are created for a desired transcriptional response.

In this study, Insilico Medicine researchers developed a new model, the Bidirectional Adversarial Autoencoder, that learns a joint distribution of molecular structures and induced transcriptional response. The model can generate molecular structures for a given transcriptional response and vise versa. As a result, Insilico Medicine provided a model that combines both generative biology and generative chemistry. Using this model, researchers can run virtual screening, discover novel molecular structures, and predict transcriptional responses--one model to solve many problems.
"This paper shows that it is possible to generate novel molecular structures that induce the desired transcriptional response. At Insilico, we have been working on this project since 2016 and have created critical intellectual property covering the original ideas in generative biology proposed and patented by Alex Zhavoronkov and Alex Aliper. I hope that the generative chemistry and biology developed at Insilico will become household tools for big pharmaceutical companies. Many of these tools are available in our upcoming AI platform soon to be available for deployment at customer premises", said Daniil Polykovskiy, group leader at Insilico Medicine and senior author of the study.

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About Insilico Medicine
Since 2014 Insilico Medicine is focusing on generative models, reinforcement learning (RL), and other modern machine learning techniques for the generation of new molecular structures with the specified parameters, generation of synthetic biological data, target identification, and prediction of clinical trials outcomes. Since its inception, Insilico Medicine raised over $52 million, published over 70 peer-reviewed papers, applied for over 20 patents, and received multiple industry awards.
Website http://insilico.com/
Media Contact
For further information, images or interviews, please contact:
ai@insilico.com
About Frontiers Research Topics
Frontiers' Research Topics are peer-reviewed article collections around themes of cutting-edge research. Defined, managed, and led by renowned researchers, they unite the world's leading experts around the hottest topics in research, stimulating collaboration and accelerating science.
About Frontiers in Pharmacology
Frontiers in Pharmacology is a leading journal in its field, publishing rigorously peer-reviewed research across disciplines, including basic and clinical pharmacology, medicinal chemistry, pharmacy and toxicology.

Frost & Sullivan recognizes Insilico as innovation leader in drug discovery

April 30, 2020 - Today Insilico Medicine has announced that Frost & Sullivan named it as a top innovator in drug discovery, highlighting it as a technology pioneer in this industry. 

Generative AI is an emerging technology in both chemistry and biology. Insilico Medicine has been working in these areas since 2015, and holds the critical intellectual property in generative biology for generation of synthetic biological data, target discovery, and cross-tissue and cross-species analysis; generative chemistry for generation of novel molecules, and generative medicine for prediction of clinical trials outcomes. According to recent legal opinions, drugs generated using AI may be classified as being invented by AI. Insilico published its first peer-reviewed papers in this area in 2016, and multiple experimental proofs of concept for JAK3 and DDR1 kinases with many other targets being pursued internally. 

The company generated and released a range of molecules targeting COVID-19 protease generated in 4 days. Generative biology technology for the target discovery has been developed and utilized internally since 2015, and the first landmark experiment in target discovery is expected to be published in 2020. 

"Insilico Medicine is a true innovator and visionary in the application of AI for novel product discovery and precision medicine. The deep generative models based on neural networks, Generative Tensorial Reinforcement Learning (GENTRL) models developed by the company generates and tests new leads with breakthrough productivity over conventional industry benchmarks." said Amol Jadhav, PhD, Industry Analyst at Frost & Sullivan. "Frost & Sullivan is pleased to include Insilico Medicine in recognition of its pioneering research, validated full stack technology, growing pipeline of molecules, extensive portfolio and commitment toward high-profile partnerships".

"I would like to congratulate my global teams on their tireless efforts and excellence in both AI and drug discovery that are now recognized by Frost & Sullivan. For five years we made bets on disruptive technologies with very limited resources and often struggled. But now these bets paid off and we are getting a new software suite we refer to as the Pharmaceutical Operating System to be released this year," said Alex Zhavoronkov, PhD, co-founder and CEO of Insilico Medicine. 

Insilico Medicine has been at the forefront of the competition and has established collaborations and partnerships with over 150 academic and industry partners worldwide. 

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About Frost & Sullivan

For over five decades, Frost & Sullivan has become world-renowned for its role in helping investors, corporate leaders and governments navigate economic changes and identify disruptive technologies, Mega Trends, new business models and companies to action, resulting in a continuous flow of growth opportunities to drive future success. http://www.frost.com

About Insilico Medicine

Since 2014 Insilico Medicine is focusing on generative models, reinforcement learning (RL), and other modern machine learning techniques for the generation of new molecular structures with the specified parameters, generation of synthetic biological data, target identification, and prediction of clinical trials outcomes. Recently, Insilico Medicine secured $37 million in series B funding. Since its inception, Insilico Medicine raised over $52 million, published over 80 peer-reviewed papers, applied for over 25 patents, and received multiple industry awards. Website http://insilico.com/

Media Contact

For further information, images or interviews, please contact: ai@insilico.com

Insilico Medicine enters into a collaboration research with Astellas Pharma Inc. to apply novel generative artificial intelligence system for a conventionally challenging target family

      Insilico Medicine has announced that it has entered into a research collaboration agreement with Astellas Pharma Inc. to utilize Insilico Medicine's novel generative artificial intelligence technology aimed at accelerating development of candidates against a conventionally challenging target family. This collaboration builds on AI platform developed by Insilico Medicine and will apply Astellas' expertise in drug discovery. 

 

"Astellas is at the forefront of innovation in drug discovery and is seen as a leader in developing and acquiring new emerging technology. This collaboration will exploit the capabilities of our entire generative chemistry platform which experienced exponential increases in performance and quality over the past few years. We are very happy to collaborate with some of the most intelligent and sophisticated scientists in the world on a very interesting target which, has no known examples to learn from, and our platform utilizing meta learning, zero-shot generative reinforcement learning, and genetic algorithms, holds a lot of promise," Alex Zhavoronkov, PhD, founder, and CEO of Insilico Medicine.

Under the terms of the agreement, Insilico Medicine will receive an upfront payment and milestones. Insilico Medicine will closely collaborate with Astellas, which will synthesize, optimize and characterize the molecules generated using artificial intelligence. 

"We are excited to collaborate with Astellas' experienced drug discovery team in applying our state-of-the-art AI technologies in molecular generation for this high-value previously challenging target family. Insilico Medicine is a world leader in artificial intelligence for drug discovery and the original inventor of the many approaches. We believe that this collaboration with one of the most innovative pharmaceutical companies will help deliver the much needed drugs to the patients sooner," said Jimmy Yen-Chu Lin, PhD, CEO of Insilico Medicine Taiwan.

Earlier this year, Insilico Medicine's generative chemistry technology was highlighted by the MIT Technology Review as a breakthrough of 2020, and is actively used to generate novel chemistry for the key SARS-CoV-2 proteins.

About Astellas Pharma Inc

http://www.astellas.com/en

About Insilico Medicine Since 2014 Insilico Medicine is focusing on generative models, reinforcement learning (RL), and other modern machine learning techniques for the generation of new molecular structures with the specified parameters, generation of synthetic biological data, target identification, and prediction of clinical trials outcomes. Recently, Insilico Medicine secured $37 million in series B funding. Since its inception, Insilico Medicine raised over $52 million, published over 80 peer-reviewed papers, applied for over 25 patents, and received multiple industry awards.

Website http://insilico.com/

Insilico collaborates with Boehringer Ingelheim on AI system for target discovery

Insilico enters into a research collaboration with Boehringer Ingelheim to apply novel generative artificial intelligence system for discovery of potential therapeutic targets

Wednesday, 15th of April, 2020 (6:00AM, Hong Kong) - Insilico Medicine is pleased to announce that it has entered into a research collaboration with Boehringer Ingelheim to utilize Insilico's generative machine learning technology and proprietary Pandomics Discovery Platform with the aim of identifying potential therapeutic targets implicated in a variety of diseases.

"Insilico Medicine is very impressed with the Research Beyond Borders group at Boehringer Ingelheim capabilities in the search of potential drug targets. In this collaboration, Insilico will provide additional AI capabilities to discover novel targets for a variety of diseases to benefit the patients worldwide. We are very happy to partner with such an advanced group" said Alex Zhavoronkov, PhD, founder, and CEO of Insilico Medicine.

"We believe that Insilico's exclusive Pandomics platform will provide huge boost to our ability to explore and identify drug targets. We look forward to using AI to significantly improve the drug discovery process and contribute to human health." said from Dr. Weiyi Zhang, Head of External Innovation Hub, Boehringer Ingelheim Greater China. 

In September 2019, Insilico Medicine announced a $37 million round led by prominent biotechnology and AI investors.

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About Insilico Medicine

Since 2014 Insilico Medicine is focusing on generative models, reinforcement learning (RL), and other modern machine learning techniques for the generation of new molecular structures with the specified parameters, generation of synthetic biological data, target identification, and prediction of clinical trials outcomes. Since its inception, Insilico Medicine raised over $52 million, published over 70 peer-reviewed papers, applied for over 20 patents, and received multiple industry awards.

Website http://insilico.com/

Media Contact

For further information, images or interviews, please contact:

ai@insilico.com

About Boehringer Ingelheim 

Improving the health of humans and animals is the goal of the research-driven pharmaceutical company Boehringer Ingelheim. The focus in doing so is on diseases for which no satisfactory treatment option exists to date. The company therefore concentrates on developing innovative therapies that can extend patients' lives. In animal health, Boehringer Ingelheim stands for advanced prevention. 

Family-owned since it was established in 1885, Boehringer Ingelheim is one of the pharmaceutical industry's top 20 companies. Some 50,000 employees create value through innovation daily for the three business areas human pharmaceuticals, animal health and biopharmaceuticals. In 2018, Boehringer Ingelheim achieved net sales of around 17.5 billion euros. R&D expenditure of almost 3.2 billion euros, corresponded to 18.1 per cent of net sales. 

As a family-owned company, Boehringer Ingelheim plans in generations and focuses on long-term success. The company therefore aims at organic growth from its own resources with simultaneous openness to partnerships and strategic alliances in research. In everything it does, Boehringer Ingelheim naturally adopts responsibility towards mankind and the environment. 

More information about Boehringer Ingelheim can be found on http://www.boehringer-ingelheim.com or in our annual report: http://annualreport.boehringer-ingelheim.com

About Boehringer Ingelheim Partnering Day 2020

Boehringer Ingelheim Partnering Day 2020 will be held in Shanghai on Nov 3. It aims to engage and collaborate with Chinese innovative start-ups or individuals and inspire local partners to gear up innovation. Boehringer Ingelheim Innovation Prize is part of Boehringer Ingelheim Partnering Day. It is the first innovation prize in biomedicine sponsored by a multinational pharmaceutical company in China. Boehringer Ingelheim is calling for startups or individuals who have innovative ideas or business plans to participate.

Please click the link below to sign up online!

https://www.bagevent.com/event/5946021