Insilico Medicine’s drug discovery engine is trained on massive amounts of structural, functional, and phenotypic data in order to predict the biological activity of compounds. Insilico Medicine has published seminal papers in Oncotarget and Molecular Pharmaceutics. Another paper, published in Molecular Pharmaceutics in 2016, demonstrated the proof of concept of the application of deep neural networks for predicting the therapeutic class of the molecule using the transcriptional response data.
Every Drug Can Be Made and Every Disease Can Be Treated
This new study was a close collaboration between Insilico Medicine and WuXi AppTec. WuXi AppTec is a leading pharmaceutical and medical device open-access capability and technology platform company with global operations. WuXi AppTec is committed to enabling innovative collaborators to bring innovative healthcare products to patients, and to fulfilling WuXi’s dream that every drug can be made and every disease can be treated.
“This paper is a significant milestone in our journey towards AI-driven drug discovery. We’ve been working in generative chemistry since 2015. 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 that we developed a few years ago being validated experimentally in animals. When integrated into comprehensive drug discovery pipelines, these models work for many target classes. We work with the leading biotechnology companies to push the limits of generative chemistry and generative biology even further.”
Alex Zhavoronkov, PhD, Founder and CEO, Insilico Medicine
When Deep Knowledge Ventures chose to provide Insilico Medicine’s initial funding round in 2014, we did so because we saw their potential to increase Quality-Adjusted Life Years for the betterment of humanity as a whole. Since then, they have been the first to use cutting edge deep learning techniques like GANs to design novel drug candidates from scratch with specified molecular properties, and succeeding in designing, synthesizing and validating a new drug end-to-end in less than 2 months. We are thrilled by the fact that this paper shows what Insilico Medicine has been developing in R&D all the way back to 2017, and submitted for publication in 2018. Perhaps Insilico Medicine has made even greater progress in applying next-generation AI techniques for drug design, which might be publicly disclosed in 2020.
The GENTRL model is a variational auto encoder with a rich prior distribution of the latent space. Insilico used tensor decompositions to encode the relations between molecular structures and their properties and to learn on data with missing values. They train the model in two steps. First, they learn a mapping of a chemical space on the latent manifold by maximizing the evidence lower bound. Then they freeze all the parameters except for the learnable prior and explore the chemical space to find molecules with a high reward.
The GENTRL source code is open source and available on GitHub. In the repository Insilico provides an implementation of a GENTRL model with an example trained on a MOSES dataset.
Comments From Key Opinion Leaders
“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”
Michael Levitt, PhD, professor of structural biology, Stanford University. Dr. Levitt received the Nobel Prize in Chemistry in 2013
“Using Advanced GANs in the discovery of drugs is a great example of cutting edge application of AI in the pharmaceutical industry – it speeds up a critical process from years to just weeks.”
Christian Guttmann, PhD, Executive Director at the Nordic AI Institute and Senior Research Fellow in Artificial Intelligence at the Karolinska Institute
“Zhavoronkov et al. show that AI techniques can be used to guide our search for good drug molecules in the vastness of chemical space, one of the key challenges in drug discovery today. The work provides compelling evidence that AI can learn from historical datasets to generate novel molecular compounds with drug-like properties, and helps clarify how AI can be used to improve the speed of drug development.”
Mark DePristo, PhD, former Head of Genomics at Google Brain, Co-founder and CEO, BigHat Biosciences
“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,”
Ian Goodfellow, PhD, the original inventor of GANs
“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,”
Kai-Fu Lee, PhD, founder of Sinovation Ventures, former executive of Microsoft and Google, and the original inventor of multiple AI technologies
“This is an important demonstration of the power of AI, using a GAN approach, to markedly accelerate the design and experimental validation of a new molecule, no less one targeting fibrosis, a major unmet medical need.”
Eric Topol, MD, Executive Vice-President of Scripps Research and Founder and Director of the Scripps Research Translational Institute (Dr. Eric Topol has no relationship with the company in question nor its authors).
“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,”
John Baldoni, PhD, CTO of a stealth AI-powered drug development startup and former SVP of Platform Technology and Science at GSK.
“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 pre-clinically 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,”
Charles Cantor, PhD, 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 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,”
Jürgen Schmidhuber, PhD, professor at IDSIA, co-founder of NNAISENSE, and the original inventor of many core techniques and initial concepts in the field of AI.
“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,”
Stevan Djuric, PhD, Adjunct Professor, School of Pharmacy, High Point University and former Vice President, Discovery Chemistry and Technology, Abbvie.
“In a recent Nature Biotechnology article, Zhavoronkov et al., experimentally demonstrate the utility of their novel GENerative Tensorial Reinforcement Learning (GENTRL) strategy for de novo drug design. In this study, GENTRL was used to design novel compounds against Discoidin Domain Receptor Tyrosine Kinase 1 (DDR1), a pro-inflammatory receptor tyrosine kinase involved in idiopathic pulmonary fibrosis and breast cancer. Of most interest, six DDR1 compounds were designed, synthesized, and experimentally tested all within only 46 days. By coupling advanced deep generative AI models, such as GENTRL with robust causal dependency structure prediction of multi-omics data in drug target discovery studies, we now hold the potential to revolutionize the pharmaceutical industry.”
Tom Chittenden, PhD, DPhil, PStat, Chief AI Scientist and Founding Director, Advanced AI Research Laboratory, WuXi NextCODE Genomics
” This study is a significant step forward in the field of de novo small molecule design. GAN has been used before for generating new molecules but A. Zhavoronkov and colleagues have developed Generative Tensorial Reinforcement Learning framework where they have shown how GAN can be complemented with reinforcement learning and prioritize regenerated structure using self-organizing maps strategies. Moreover, what amazes me is the timeline within which lead compounds are generated which are both in vivo and in vitro validated. I appreciate Insilico Medicine’s efforts for sharing their code repository to the open-source community, I’m confident this study will open many avenues towards the research activities within AI in drug discovery.”
Gopal Karemore, PhD, Principal Data Scientist, Novo Nordisk
“Deep Knowledge Ventures provided Insilico Medicine’s initial funding round in 2014, and has remained a close advisor in the company’s journey towards becoming a global leader in the application of advanced AI for aging research. Insilico Medicine is one of our most promising portfolio companies, not only in terms of its potential ROI, but also because of its potential impact on serious problems facing humanity. Deep Knowledge Ventures continues to make the AI for Drug Discovery sector a major priority in its strategic agenda, and will soon launch a new subsidiary fund, AI-Pharma, which will use hybrid investment technologies combining the profitability of venture funds with the liquidity of hedge funds, significantly de-risking the interests of LPs and simultaneously providing the best and most promising AI companies with a relevant amount of investment.”
Margaretta Colangelo, Managing Partner, Deep Knowledge Ventures
“Exhilarating news in Nature Biotechnology today, as scientists from Insilico Medicine report that an AI process called GENTRL, has facilitated the identification of new small molecule kinase inhibitors, DNA damage response (DDR1) inhibitors, in a two month time frame, reducing the current non-AI early ‘research/preclinical development’ time estimates for new drugs by approximately 94%. The cost savings for biomarker drugs using AI processes is huge. Not only is the end-to-end development time reduced, but so too are the costs related to R&D scientific, professional and technical personnel, which account for approximately 29% of the total cost to develop a drug, according to Tufts CSDD. Since the FDA fast tracks many drugs for serious conditions, there is incredible potential to reduce overall developments costs while increasing the speed which novel drugs can be approved for very sick patients waiting for them. This welcome news comes at a time when soaring costs for drug development, arguably are being recouped in high prices of novel innovative therapies hitting the market.”
Barbara Gilmore, Senior Consultant, Transformational Health, Frost & Sullivan
“It is extremely exciting seeing Deep Learning and other techniques being used to help pinpoint drug discovery in a matter of days. In particular, exploiting large, publicly-available data sets to accelerate this process can give huge benefits for low cost. The data-driven approach will give better and faster results than the traditional methods, leading to faster drug discovery and safer, more reliable results than clinical trials on their own. While it’s unlikely that AI will replace the current methods overnight, it’s obvious that organizations which add AI to their methods will quickly replace those who do not. It is vital these organizations ‘Uber’ themselves before they get Kodaked”
David Whewel, former Director of Architecture & Software Innovation, Merck Group
“This is the first time that an AI company has designed a novel drug from scratch, synthesized it and preclinically validated it end-to-end in days rather than years – 15 times faster than the approach used by even the most efficient big pharma players. This is a true game changer, and proves that AI will be the central driver in drug development for years to come.”
Robin Starbuck Farmanfarmaian, author of The Patient as CEO: How Technology Empowers the Healthcare Consumer
“This newest achievement made by Insilico Medicine, a leading AI for drug discovery and longevity company and an official partner of Aging 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 aging 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, aging research and longevity”
Richard Siow, PhD, Director of Aging Research at King’s and former Vice-Dean (International), Faculty of Life Sciences & Medicine, King’s College London
“Besides cost savings, vendors need to demonstrate high-quality results that can be measured and compared against standard practices potentially reducing the burden on sponsors. Within the drug discover space Insilico Medicine is one such successful company that leverages Deep Learning Platform solutions for Drug Repurposing and Biomarker Development. Through their commercial partnerships and peer-reviewed publications the company has clearly demonstrated its strong position. AI is becoming a significant source of competitive advantage and differentiation. Frost & Sullivan finds a moderate level of investment towards appropriate AI products and services for R&D can provide up to 5x-8x times returns on investment. For example, deep learning and GANs (Generative adversarial networks) are providing opportunities for reducing the timeline for molecule hit discovery in a matter of weeks when compared to years with the traditional approach. Target validation, compound discovery, and repurposing supported by Deep Learning and Big Data will lead to further advances and recognizable benefits. With advances in Deep neural networks based models, the field of de novo drug design will start to produce truly novel drug candidates.”
Kamaljit Behera, Senior Industry Analyst for Transformational Health, Frost & Sullivan
“As far as I know, this marks the first ever demonstration that AI can generate entirely novel, synthesizable, active molecules against a specific pharmacological target. In my view, the fact that they were able to generate entirely novel, pharmacologically viable compounds using AI is the most amazing achievement here. Of course it’s even more amazing that they established this ground-breaking proof of concept in just 46 days!”
Olivier Elemento, PhD, Director of the Englander Institute for Precision Medicine & Associate Director of the Institute for Computational Biomedicine at Weill Cornell Medicine
“When Deep Knowledge Ventures chose to provide Insilico Medicine’s initial funding round in 2014, we did so because we saw their potential to increase Quality-Adjusted Life Years (QALY) for the betterment of humanity as a whole. Since then they have been the first to use cutting edge deep learning techniques like Generative Adversarial Networks to design novel drug candidates from scratch with specified molecular properties in 2016, and in 2018 to succeed in designing, synthesizing and validating a new drug end to end in less than 2 months. I am also thrilled by the fact that this article visualizes what Insilico Medicine has been making in their R&D already back in 2017 and submitted for publication in 2018. I would not be surprised to find out that since then they have made even greater progress in applying next-generation AI techniques for drug design, which might be publicly disclosed in 2020”
Dmitry Kaminskiy, General Partner, Deep Knowledge Ventures