Given there is no open source software available for the types of analysis required, proprietary algorithms are required. For this, mechanisms to extract data based on the objective of analysis are required together with the selection of the necessary biological entities and features related to the objective of analysis. From this, AI can assist the scientist with interrogating the data to clarify ambiguity or verify the relevance of entities, helping the scientists on a faster path towards drug discovery. For example, a project might utilize a next generation platform to predict the absorption, distribution, metabolism, excretion, and toxicity of new drug candidates far faster than any traditional laboratory testing could achieve. Hence, AI has the potential to provide deep insights into the continuum from chemical structure to in vitro, in vivo, and clinical outcomes.

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. Insilico was able to ideate and generate a novel molecule from start to finish in 21 days. In a similar technique used by DeepMind to outcompete human GO players, GENTRL -- powered by generative chemistry that utilizes modern AI techniques -- can rapidly generate novel molecular structures with specified properties. Insilico has made GENTRL's source code available as open source. "The development of these first six molecules as an experimental validation is just the start," said Alex Zhavoronkov, CEO of Insilico Medicine.

In a world first, Insilico Medicine, a Hong Kong-based startup developing deep neural networks for drug discovery, has successfully synthesized and pre-clinically validate a drug candidate in just 25 days, making the drug discovery process, including the designing stage, take about 46 days. According to Insilico's research team and its collaborators at the University of Toronto, the method of designing new kinds of molecules by using a deep generative artificial intelligence (AI) model – called generative tensorial reinforcement learning (GENTRL) – not only set a record time compared to traditional methods but also proved to be 15 times faster than a typical pharma corporation's efficient R&D process. It's worth pointing out, especially for readers unfamiliar with the big pharmaceutical industry, that it takes more than a decade and millions of dollars to discover and develop a drug candidate. What's even more depressing about this inefficient industry that keeps passing off the illusion of innovation for real innovation, is that in the last twenty-plus years the success rate for a drug candidate entering Phase I trials have stagnated at under 10%. Meanwhile, in pre-clinical phases the failure rates for new compounds is over 99%.

Biotechnology, pharmaceutical, and life sciences industries are where applied artificial intelligence (AI) can greatly accelerate innovation and shorten the product development life-cycle. Developing a drug typically takes 10 to 15 years on average, with only approximately 12 percent of drugs in clinical trials ultimately gaining U.S. Food and Drug Administration (FDA) approval. In an AI milestone in life sciences, Insilico Medicine announced a new machine learning tool for drug discovery that can generate a novel molecule in days instead of years and published their findings in Nature Biotechnology on September 2, 2019. Insilico Medicine is a venture-backed start-up with multiple investors that include WuXi AppTec, Juvenescence, Peter Diamandis' BOLD Capital Partners, and Pavilion Capital. Led by CEO and Founder Alex Zhavoronkov, the company's mission is to extend longevity by applied AI solutions for drug discovery and aging research.

Biotechnology, pharmaceutical, and life sciences industries are where applied artificial intelligence (AI) can greatly accelerate innovation and shorten the product development life-cycle. Developing a drug typically takes 10 to 15 years on average, with only approximately 12 percent of drugs in clinical trials ultimately gaining U.S. Food and Drug Administration (FDA) approval. In an AI milestone in life sciences, Insilico Medicine announced a new machine learning tool for drug discovery that can generate a novel molecule in days instead of years and published their findings in Nature Biotechnology on September 2, 2019. Insilico Medicine is a venture-backed start-up with multiple investors that include WuXi AppTec, Juvenescence, Peter Diamandis' BOLD Capital Partners, and Pavilion Capital. Led by CEO and Founder Alex Zhavoronkov, the company's mission is to extend longevity by applied AI solutions for drug discovery and aging research.

Biotechnology, pharmaceutical, and life sciences industries are where applied artificial intelligence (AI) can greatly accelerate innovation and shorten the product development life-cycle. Developing a drug typically takes 10 to 15 years on average, with only approximately 12 percent of drugs in clinical trials ultimately gaining U.S. Food and Drug Administration (FDA) approval. In an AI milestone in life sciences, Insilico Medicine announced a new machine learning tool for drug discovery that can generate a novel molecule in days instead of years and published their findings in Nature Biotechnology on September 2, 2019. Insilico Medicine is a venture-backed start-up with multiple investors that include WuXi AppTec, Juvenescence, Peter Diamandis' BOLD Capital Partners, and Pavilion Capital. Led by CEO and Founder Alex Zhavoronkov, the company's mission is to extend longevity by applied AI solutions for drug discovery and aging research.

Biotechnology, pharmaceutical and life sciences industries are where applied artificial intelligence (AI) can greatly accelerate innovation and shorten the product development life-cycle. Developing a drug typically takes 10 to 15 years on average, with only approximately 12 percent of drugs in clinical trials ultimately gaining U.S. Food and Drug Administration (FDA) approval. In an AI milestone in life sciences, Insilico Medicine announced a new machine learning tool for drug discovery that can generate a novel molecule in days instead of years, and published their findings in Nature Biotechnology on September 2, 2019. Insilico Medicine is a venture-backed start-up with multiple investors that include WuXi AppTec, Juvenescence, Peter Diamandis' BOLD Capital Partners, and Pavilion Capital. Led by CEO and Founder Alex Zhavoronkov, the company's mission is to extend longevity by applied AI solutions for drug discovery and aging research.

In the gold rush to bring artificial intelligence to the healthcare and biopharma industries, AI has long been pitched as a way to accelerate the pace of drug development and discovery. Sometimes vaguely and sometimes not, many companies have claimed their code can help early research get done quicker, deeper and cheaper. Now, Insilico Medicine may have hit pay dirt, demonstrating in a paper published in Nature Biotechnology that its computer networks could potentially shave years off of traditional hit-to-lead timelines. Over 21 days, the startup and its partners used its AI programs to conceptualize and generate 30,000 novel small molecules that may work against fibrosis. Within 25 more days, they had screened out and synthesized the six most promising compounds, tested them in vitro for selectivity and metabolic stability and had the lead candidate go on to show favorable activity in live mouse models.

Insilico Medicine, a company using artificial intelligence (AI) for drug discovery, published a paper on Monday describing new machine learning techniques designed to substantially accelerate and improve the drug discovery process. The paper, which was published in Nature Biotechnology, is titled "Deep learning enables rapid identification of potent DDR1 kinase inhibitors." The findings describe a timed challenge where the new AI system called Generative Tensorial Reinforcement Learning (GENTRL) had to determine new molecules for drug discovery. The GENTRL program managed to design six novel inhibitors of DDR1, a kinase target implicated in fibrosis and other diseases, in 21 days. According to the research, four compounds were active in biochemical assays, and two were validated in cell-based assays.

Insilico Medicine aims to bring deep learning to the drug discovery process. Hong Kong-based Insilico Medicine published research Monday showing that its deep learning system could identify potential treatments for fibrosis. That system, called generative tensorial reinforcement learning, or GENTRL for short, was able to find six promising treatments in just 21 days, one of which showed promising results in an experiment involving mice. The research has been published in Nature Biotechnology, and the code for the model has been made available on Github. "We've got AI strategy combined with AI imagination," says Insilico CEO Alex Zhavoronkov, who compares the operation of GENTRL to the AlphaGo machine learning system that Google's Deepmind developed to challenge champion Go players.