That headline might seem a bit churlish, given the tremendous amount of energy, investment, and hype in the AI space, as well as undeniable evidence of technological progress. After all, AI today can beat any human in games ranging from chess to Starcraft (DeepMind's AlphaZero and AlphaStar); it can write a B- college history essay in seconds with a few prompts (OpenAI's GPT-3); it can draw on-demand illustrations of surprising creativity and quality (OpenAI's DALL-E 2). For AI proponents like Sam Altman, OpenAI's CEO, these advances herald an era where "AI creative tools are going to be the biggest impact on creative work flows since the computer itself," as he tweeted last month. That may turn out to be true. But in the here and now, I'm still left somewhat underwhelmed.

Machine learning can quickly and precisely evaluate binding free energy used in drug discovery, according to a March 15 study published in The Journal of Physical Chemistry Letters. The new machine learning tool, known as DeepBAR, was discovered by Xinqiang Ding, PhD, and Bin Zhang, PhD, researchers from the Massachusetts Institute of Technology in Cambridge. Drugs are only effective if they stick to their target proteins in the body, which can slow down drug discovery. Existing techniques struggle to balance efficiency and accuracy, researchers said. DeepBAR can accelerate the process because it is much quicker than other methods currently available.

It's these genetic definitions that circumscribe their three-dimensional structures, which in turn determines their capabilities. But protein "folding," as it's called, is notoriously difficult to figure out from a corresponding genetic sequence alone. DNA contains only information about chains of amino acid residues and not those chains' final form. In December 2018, DeepMind attempted to tackle the challenge of protein folding with a machine learning system called AlphaFold. The product of two years of work, the Alphabet subsidiary said at the time that AlphaFold could predict structures more precisely than prior solutions.

If DeepMind's methods can be refined, he and other researchers said, they could speed the development of new drugs as well as efforts to apply existing medications to new viruses and diseases. The breakthrough arrives too late to make a significant impact on the coronavirus. But researchers believe DeepMind's methods could accelerate the response to future pandemics. Some believe it could also help scientists gain a better understanding of genetic diseases along the lines of Alzheimer's or cystic fibrosis. Still, experts cautioned that this technology would affect only a small part of the long process by which scientists identify new medicines and analyze disease.

Frost & Sullivan's recent analysis, Data Science Impacting the Pharmaceutical Industry, finds that data science tools are promising technologies transforming drug discovery costs, speed, and efficiency. When combined with other emerging tech areas, artificial intelligence (AI) technologies move to the next phase of advancements. Hence, they are expected to witness adoption by pharma and biotech companies in the next four to five years. Further, with the COVID-19 pandemic, AI and machine learning (ML) can be used for drug research and clinical trials against the coronavirus to screen large databases and perform docking studies to identify existing potential drugs or design new drugs using advanced learning algorithms. For further information on this analysis, please visit: http://frost.ly/4l2.

For further information on this analysis, please visit: http://frost.ly/4l2. "Applying data science tools in healthcare, especially for drug discovery, has a huge potential to systematically change the entire existing practices and methods," said Aarthi Janakiraman, Technical Insights Research Manager at Frost & Sullivan. "Additionally, pharmaceutical companies and hospitals are adopting this system rapidly, and its application is going to be established in all branches of healthcare." Janakiraman added: "Integrating AI and ML methods into drug discovery pipelines would cut down cost and time, and increase the efficiency of the entire research and development (R&D) process. Going forward, big pharma and mid-sized biotech companies can benefit by partnering with core AI startups and reducing the costs involved in setting up their own capabilities."

Atomwise, a startup using AI to accelerate drug discovery, today secured $123 million in funding. A spokesperson said the funds will enable the startup to scale its technology and team as it expands its portfolio of joint ventures with researchers at the University of Toronto, Duke University School of Medicine, Charles River, Bayer, Eli Lilly, Merck, and others. Fewer than 12% of all drugs entering clinical trials end up in pharmacies, and it takes at least 10 years for medicines to complete the journey from discovery to the marketplace. Clinical trials alone take six to seven years, on average, putting the cost of R&D at roughly $2.6 billion, according to the Pharmaceutical Research and Manufacturers of America. Atomwise claims its AtomNet platform can screen 16 billion chemical compounds for potential hits in under two days, expediting a process that would normally take months or years.

Cyclica, a biotechnology company leveraging AI to accelerate drug discovery, today announced that it raised $23 million. It will use the proceeds to advance its platforms and bolster its commercial plans, according to CEO Naheed Kurji, including growing a pipeline of pre-clinical and clinical assets and pursuing segments like agrochemicals. Traditionally, the design of small molecule therapies -- therapies incorporating chemical probes that aren't useful as drugs, but that inhibit or promote the function of specific proteins -- has focused on disease-associated protein targets. But once a drug enters the body, it interacts with dozens, if not hundreds, of proteins before it is eliminated, and these interactions can impact safety or lead to repurposing opportunities. Moreover, despite how useful chemical probes are, only 4% of human proteins have a known chemical probe available.

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.

Deep Learning enables rapid identification of potent DDR1 Kinase Inhibitors. 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.