image: A Timeline of the Development of Generative Adversarial Networks (above), followed by a timeline of the use of Generative Adversarial Networks in pharmaceutical science and drug discovery.
September 3rd, 2019, London, UK: Deep Knowledge Analytics salutes its parent company, Deep Knowledge Ventures, in the landmark Nature Biotechnology publication of its portfolio company, Insilico Medicine, demonstrating the design, synthesis and preclinical validation of a novel drug candidate in just 46 days.
The many advances in deep learning reinforcement learning and generative adversarial learning made since 2014 are rapidly transforming multiple industries including search, translation, video games, retail, transportation, and many others. It is relatively easy to validate the performance of the AI systems in imaging, voice, text and other areas where human sensory systems can be used to rapidly verify the validity of the experimental results. However, in the pharmaceutical industry, the validation cycles take decades and cost billions of dollars. Most of the common questions asked by the pharmaceutical industry executives to all of the leading artificial intelligence groups worldwide deal with the novelty of the algorithms and experimental validation of the results in mice or even in humans. There is a grave disconnect between the leaders in AI focusing on the novelty of the algorithms and drug discovery and development experts focusing only on experimental data.
On Monday, September 2nd, a group of collaborators from Insilico Medicine and WuXi AppTec published irrefutable proof where cutting-edge work in artificial intelligence utilizing the next-generation generative tensorial and reinforcement learning was followed by experimental validation. The molecules "imagined" by the Generative Tensorial Reinforcement Learning (GENTRL) techniques were rapidly synthesized, tested in enzymatic, cell-based fibrosis, metabolic stability, microsomal stability assays, and in mice.
The paper Alex Zhavoronkov, et al, 2019, "Deep learning enables rapid identification of potent DDR1 kinase inhibitors", Nature Biotechnology, presents a substantial advance in artificial intelligence for drug discovery. The AI work presented in the paper was done in 2017 and the code is openly available. The company scientists believe that they can go after the many target classes and challenging targets with the universal generative pipeline that can account for cases where no crystal structure exists and one or even no template molecules are known while achieving respectable hit rates.
We sent the article to a number of thought leaders in the industry asking for their opinion:
"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." said Dr. Eric Topol, Executive Vice-President of Scripps Research and Founder and Director of the Scripps Research Translational Institute (Eric Topol has no relationship with the company in question nor its authors).
"Zhavoronkov et al. show that AI techniques can be used to guide our search for good drug molecules in the 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." said Mark DePristo, former Head of Genomics at Google Brain, Co-founder and CEO, BigHat Biosciences.
"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" said Dmitry Kaminskiy, General Partner of Deep Knowledge Ventures.
"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." said Christian Guttmann, Executive Director Nordic AI Institute, Professor AI at the University of New South Wales, and Senior AI Research Fellow AI at Karolinska Institute.
"Exhilarating news in Nature Biotechnology today, as scientists from Insilico Medicine (Hong Kong), 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 bio-marker 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. In addition to the reduced development time and costs, drugs potentially get to market sooner, generating revenues for the companies who developed the drugs. DDR inhibitors are being studied for the treatment of cancer. Since the FDA fast tracks many drugs for serious conditions, there is incredible potential to reduce overall developments costs while increase 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." said Barbara Gilmore, Senior Consultant on Transformational Health at 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 organisations which add AI to their methods will quickly replace those who do not. It is vital these organisations 'Uber' themselves before they get Kodaked" said David Whewel, former Director of Architecture and Software Innovation at Merck Group.
"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!" said Olivier Elemento, Director of the Englander Institute for Precision Medicine & Associate Director of the Institute for Computational Biomedicine at Weill Cornell Medicine.