Pharmaceutical researchers in the United Kingdom are using the artificial intelligence tool AlphaFold to develop new psychedelic drugs, according to research released last month.

The research, which has not yet been peer-reviewed, shows that using an AI tool is as useful as experimentally produced protein structures, which can take up to years to investigate. The findings give further evidence of the utility of AlphaFold, an artificial intelligence tool developed by DeepMind in London.

“AlphaFold is an absolute revolution,” Jens Carlsson, a computational chemist at the University of Uppsala in Sweden, told Nature. “If we have a good structure, we should be able to use it for drug design.”

AlphaFold, a public database that holds structure predictions for nearly every known protein, has proven to be a significant advancement in biological research. With the tool, pharmaceutical companies can use the protein structure of molecules implicated in disease to identify and improve promising new drugs. But not everyone is sold on the AlphaFold AI technology as a tool for developing new drugs.

“There is a lot of hype,” said Brian Shoichet, a pharmaceutical chemist at the University of California, San Francisco. “Whenever anybody says ‘such and such is going to revolutionize drug discovery’, it warrants some skepticism.”

Shoichet noted that more than 10 studies have shown that predictions made by AlphaFold were not as useful as protein structures obtained with experimental methods such as X-ray crystallography when used to identify potential drugs in a modeling method called protein–ligand docking.

Protein-ligand docking is a method commonly used in the early stages of drug development that uses modeling to determine how hundreds of millions or even billions of compounds interact with key regions of a protein to identify substances that alter the protein’s activity. Previous studies have shown that when structures predicted by AlphaFold are used, the models do not do a good job of identifying drugs that are already known to bind to a particular protein.

Shoichet and Bryan Roth, a structural biologist at the University of North Carolina at Chapel Hill, led a team of researchers who came to a similar conclusion when they tested AlphaFold structures of two proteins implicated in neuropsychiatric conditions against known drugs. The researchers hoped to determine if small differences from experimental structures might cause the predicted structures to miss certain compounds that bind to proteins, but also make them able to identify different potentially useful compounds.

The researchers tested the hypothesis by using experimental structures of two proteins to virtually screen hundreds of millions of potential new drugs. One of the proteins, a receptor that senses the neurotransmitter serotonin, was previously determined through cryo-electron microscopy. The other protein’s structure, called the σ-2 receptor, had been mapped with X-ray crystallography.

The researchers ran the same screen with models taken from the AlphaFold database. They then synthesized hundreds of compounds deemed to be the most promising identified by either the AI-predicted or experimental structures and measured their activity. Significantly, the screens with predicted and experimental structures identified different drug candidates.

“There were no two molecules that were the same,” Shoichet said. “They didn’t even resemble each other.”

The researchers noticed, however, that the proportion of flagged compounds that altered protein activity in a significant manner –known as the hit rate– was almost identical for the two groups. AlphaFold structures identified the drugs that activated the serotonin receptor most strongly.

LSD, the drug commonly known as acid, works in part by activating the serotonin receptor. Many researchers are trying to identify drugs that work similarly without causing hallucinogenic effects so they can be used as antidepressants.

“It’s a genuinely new result,” said Shoichet.

Carlsson and his team of researchers found in research that has yet to be published that AlphaFold structures had about a 60% hit rate class of target called G-protein-coupled receptors. He said that having a tool that can reliably predict protein structures would be revolutionary for the drug development industry.

“It would be very convenient if we could push the button and get a structure we can use for ligand discovery,” he said.

But AlphaFold will not take the place of other methods of discovering new drugs. Predicted structures can be helpful for some but not all drug targets, and which applies is not always readily apparent. 

“This is not a panacea,” says Karen Akinsanya, president of research and development for therapeutics at Schrödinger, a drug-software company based in New York City that is using AlphaFold.

AI is not likely to replace experimentation for new drugs, but researchers say the value of AlphaFold and similar tools should be recognized.

“There’s a lot of people that want AlphaFold to do everything, and a lot of structural biologists want to find reasons to say we are still needed,” says Carlsson. “Finding the right balance is difficult.”

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For the past few years, artificial intelligence, usually referred to as AI, has been making major waves across the globe. Some love it, claiming it will open up new frontiers for business and automation, leaving the menial jobs to the robots. Others fear and hate it, claiming it is going to take over jobs and calling it out for replacing artist’s work. 

And now, there’s a new concern floating around. Terrifyingly, some experts think that there is a small but significant chance that AI could actually make humanity go extinct. But is that mere speculation, or an actual concern we need to be worried about? 

The findings in questions come from a 2023 survey of 2700 AI researchers who have published work at top conferences, and it is the largest compilation of such information. The survey asked participants about technological milestones, and the impact it will have on society, either good or bad. Nearly 58% of researchers said they consider there to be a 5% chance that AI could either cause human extinction or other catastrophic consequences for humanity. 

“It’s an important signal that most AI researchers don’t find it strongly implausible that advanced AI destroys humanity,” says Katja Grace of the Machine Intelligence Research Institute in California, who authored this study. “I think this general belief in a non-miniscule risk is much more telling than the exact percentage risk.”

Yep, this sounds very concerning to us, but not all experts are worried. Émile Torres of Case Western Reserve University in Ohio claims that many AI experts, in their words, “don’t have a good track record” when it comes to making AI predictions.They also said that AI researchers are by no means experts in forecasting the future of AI, and points to this 2016 survey that she says did a “fairly good job of forecasting” what things could look like for AI in the future. 

Compared with the same survey when completed in 2022, many AI researchers made wrong predictions. They predicted AI would hit certain milestones earlier than previously predicted. This was likely spurred on by the release of ChatGPT in 2022 and the rush to make programs like that available once there was high demand. 

The researchers from the survey predicted that within the next decade, there is a 50% or greater chance that AI will be able to tackle most of the 39 sample tasks that have been laid before them, including perfectly copying a pop song and coding a payment processing site. These are very different and complex tasks, but the researchers claimed that other things, like solving math problems that have plagued mathematicians for years or installing electricity in a home, could take longer. 

The 2016 survey further predicted that there was a 50% chance AI would be able to outperform humans on every task by 2047, and that there was a 50% chance that human jobs could become fully automated by 2116. This was 13 and 48 years, respectively, earlier than the 2023 survey predicted. 

This, of course, speaks to the overall fear that AI will take our jobs and make humans obsolete, which seems to be the biggest concern folk have right now. But again, Torres claims that a lot of these predictions should be taken with a grain of salt. 

“A lot of these breakthroughs are pretty unpredictable, and it’s entirely possible that the field of AI goes through another winter,” they say, referencing what happened in the 1970s and ‘80s. During this time, funding and corporate interest dried up, as the technology was seemingly stalled. If advances don’t happen fast enough, either in line with predictions or what people think AI should be, that could happen again. 

Researchers also warn that, while the far-off threat of human annihilation may seem like the scariest thing to consider, there are more immediate worries to keep in mind. Over 70% of AI researchers said that more immediate threats are deepfakes, manipulation of public opinion, engineered weapons, authoritarian control of populations, and worsening economic inequality. 

The weapons and control sound terrifying, but we’re already starting to see issues with deepfakes and public opinion, so those threats seem very spot-on. Torres especially points out that AI can contribute to misinformation around issues like politics or climate change, which also seems like a more immediate thing to be concerned with. 

“We already have the technology, here and now, that could seriously undermine [the US] democracy,” Torres adds. “We’ll see what happens in the 2024 election.” 

In this sense, Torres seems spot-on. We are already seeing concern over the 2024 elections and what they will bring out in our society, so the added fuel of AI misinformation definitely does not seem positive. 

So, the consensus seems to be that the future could be scary, but the AI threats are likely more immediate and mundane, rather than some far-off dystopian future scenario. 

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A prestigious science award has been given to a man who created a new generative AI language model capable of identifying the exact chemical structure of designer drugs, even drugs that have not been tested on humans yet. 

The winning entry for the 2023 NOMIS & Science Young Explorer competition was a new AI language model trained by Princeton biologist Michael Skinnider. This new AI model can reportedly identify the chemical structure of research chemicals or “legal highs,” terms which refer to any number of chemical compounds which produce psychoactive effects but have not yet been scheduled by the FDA. The speed at which these compounds can be synthesized has created a legislative loophole where chemists and labs can more or less sell and ship dangerous drugs to people with a label on it that says “not for human consumption” without fear of legal repercussions. 

As a result of this loophole, law enforcement professionals are often faced with a situation wherein they suspect someone is carrying dangerous drugs but can’t prove it, or someone is experiencing adverse effects from a drug that they are unable to identify and thus, unable to properly treat. Traditional field testing kits can’t identify the drug because traditional field testing kits only look for the most commonly used psychoactive compounds (heroin meth, cocaine, the usual suspects), whereas Skinnider’s AI model is reportedly capable of generating and identifying entirely new chemical structures on the fly. 

“Conventional drugs of abuse such as cocaine or methamphetamine still dominate the market, but enterprising chemists have realized that with slight chemical modifications to these drugs, they can create new derivatives that are completely legal,” Skinnider said in an essay on Science. “And because these synthetic drugs have never been tested on humans, they can have unpredictable and damaging side effects.”

Skinnider began his work by first training an AI language model on a simplified molecular input line entry system, otherwise known as SMILES, which is a fancy way of saying he taught the language model a new language which is used to represent different complex chemical structures in a simple text-based format. 

Skinnider taught the AI model a way of then identifying chemicals using a process called “mass spectrometry” which, god help me I’m probably butchering this, but according to Waters, mass spectrometry is a process of measuring the different ratios of electrical charges at the molecular level of whatever you want to test, drugs in this case, to determine the exact molecular weight of the particles in the sample. These molecular weights are used to identify and map the chemical compound. 

“As a MD/PhD student, I saw firsthand how patients could present with devastating symptoms of designer drug intoxications, but emergency physicians had few options to treat them. I wondered whether artificial intelligence could help,” Skinnider said. “Specifically, I asked whether AI could automatically elucidate the chemical structures of new designer drugs from mass spectrometry data. Scientifically, this was a tall order.”

Skinnider then used information from existing research about commonly used designer drugs to further educate the AI model, using 1,753 known examples of such. What he found was the program could then generate examples of entirely new chemical structures that might have similar effects. Not only that, but it could also be used to predict what undiscovered chemicals are most likely to become popular in the future based on what drug users have responded well to in the past. 

Skinnider intimated that these advancements in drug identification technology have very practical real world applications in identifying and responding to drug crises. He also said his technology has already been used to identify new and dangerous psychoactive compounds. 

“I have now applied this technology to tens of thousands of patient samples and used it to discover several new designer drugs, such as a new analog of fentanyl that emerged last year. Currently, I am working with the British Columbia Centre for Disease Control to implement this AI technology in routine clinical practice to automatically discover new drugs as soon as they are introduced into the population,” Skinnider said. “Ultimately, my dream is that first responders, emergency physicians, and public health officials will all be able to take advantage of generative AI to make more informed decisions when treating patients and managing outbreaks.

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The way drugs are developed is being rewritten as we speak. Discovering and approving new drugs is a lengthy process that requires years of development and a dismal success rate, but artificial intelligence (AI) could rev up the process in a major way.

On May 15, Google Cloud launched two new AI-powered tools designed to help biotech and pharmaceutical companies speed up the discovery of new drugs and advance medicine: Target and Lead Identification Suite and Multiomics Suite.

Currently, developing a new drug from the idea to the rollout of a finished product takes 12–15 years and can cost over $1 billion, according to a study published in the March 2021 issue of British Journal of Pharmacology. In addition, identifying a biological target needed for a viable drug can take up to 12 months according to NIH, National Center for Biotechnology Information.

The tools could help replace traditional ones: X-ray crystallography and nuclear magnetic resonance (NMR), two main processes that determine protein 3D structures, the biological target needed for the first step in drug development, but with a high rate of failure. 

It was announced at the Bio-IT World Conference on May 16-18 at the Hynes Convention Center in Boston, Massachusetts, an event “showcasing the technologies and analytic approaches that solve problems, accelerate science, and drive the future of precision medicine.” The event included speakers from Pfizer Inc., Janssen Pharmaceuticals, and Harvard Medical School.

CNBC reports that Target and Lead Identification Suite is designed to help biotech and pharmaceutical companies predict and understand the structure of proteins, which so happens to make up a fundamental part of drug development. It allows scientists to share and manage molecular data on a protein using Google Cloud’s Analytics Hub to securely exchange data. 

Researchers then use data to predict the structure of a protein with AlphaFold2, a machine learning model developed by Google’s subsidiary. AlphaFold2 runs on Google’s Vertex AI, a platform that allows researchers to build and deploy machine learning models faster.

Within “minutes” AlphaFold2 can predict the 3D structure of a protein with high accuracy.

Multiomics Suite, on the other hand, will help researchers ingest, store, analyze and share mass amounts of genomic data. This suite helps researchers embark on genomic data analysis. 

CNBC reports that Colossal Biosciences, a biotechnology company that has a goal to use DNA and genetic engineering to “reverse extinction.”

Researchers can sequence DNA much faster than the time it takes to decipher and analyze it. But thanks to technology, genomic data in areas like the genetic variations associated with disease. 

The exploration of Investigational New Drugs (INDs) in biotech often overlaps with the cannabis industry, and Google’s new tools could transform business for certain companies. 

Shweta Maniar is Google Cloud’s global director of life sciences strategy and solutions. “We’re helping organizations get medicines to the right people faster,” Maniar told CNBC. “I am personally very excited, this is something that myself and the team have been working on for a few years now.”

The first key step of drug development, which is identifying a biological target to focus on and design a treatment around. A biological target is most commonly a protein, a building block of diseases and other life forms. And to find the target, researchers have to identify the structure of a protein, which determines its function in a disease.  

“If you can understand the role, the protein structure and role, now you can start developing drugs around that,” Maniar said. 

The AI market is projected to reach the trillions if technology keeps up the pace. Google announced OpenAI’s ChatGPT late in 2021, and its generative chatbot Bard in February.

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Hemp has plenty of industrial applications, and while some consumers might tout the benefits of ingesting hemp for constipation, high cholesterol, eczema, arthritis, and other conditions, evidence supporting these uses is still limited. However, new preclinical data suggests that hemp hulls contain some essential helpers for your gut.

Biosciences and artificial intelligence company Brightseed announced the preclinical data, published in peer-reviewed Journal of Food Bioactives, last week. The findings show that two bioactive compounds found in hemp hulls — N-trans caffeoyltyramine (NCT) and N-trans feruloyltyramine (NFT) — have the potential to support gut barrier function.

Hemp’s Link to Gut Health

A bioactive compound is a type of chemical found in foods like fruits, vegetables, nuts, oils, and whole grains that possess actions within the body that can promote good health. These compounds are often studied for their potential to prevent disease. While science has known that bioactives can have a critical impact on our health, the vast majority of bioactive compounds is still unknown with some dubbing it the “dark matter of nutrition.”

Gut health is connected to the immune system, central, and enteric nervous systems; therefore, it can be linked to the body’s overall health. The gut barrier allows the body to uptake essential nutrients and immune sensing, while still restricting pathogenic molecules, toxins, and bacteria. When the intestinal epithelium is more absorbent and easily penetrable, referred to as “leaky gut,” this may be a symptom of impaired gut health.

During this in vitro study, which used Brightseed’s Forager AI platform, proliferative human transverse colon epithelial cells were planted and co-cultured with tumor necrosis factor (TNF) along with NCT, NFT or NCT/NFT over a 48-hour period. Researchers reported that the addition of TNF caused a decrease in transepithelial electrical resistance (TEER) and an increase in intestinal permeability, leaky gut symptoms, while co-administration of NCT and NFT caused a dose-dependent and statistically significant reversal of impaired TEER and intestinal permeability.

“This publication is the latest validation of Brightseed’s approach to discover solutions in nature to restore human health,” said Jim Flatt, Brightseed CEO and co-founder, in a press release. “These insights into how NCT and NFT can support gut function, and specifically gut barrier strength by restoring a healthy epithelium barrier, provide a strong foundation for the clinical research that is underway to substantiate the benefits of natural ingredients for personalized and proactive health solutions.”

Using AI to Uncover Bioactive Compounds

Brightseed’s Forager identified activity from the two bioactive compounds on a key biological receptor governing gut health. NCT and NFT have also been shown to act as agonists for the HNF4a protein, which when expressed in the gut is linked to a high-fat diet and can be associated with poor intestinal health and decrease gut barrier integrity.

Forager scoured the plant kingdom for novel sources of these two bioactive compounds and identified hemp hulls as one of the richest sources. Last fall, Brightseed launched Brightseed Bio 01, a hemp fiber for food and beverages, containing NCT and NFT to support gut strength and gut lining maintaining healthy gut barrier function. The hemp fiber is formulated to optimize bioactive content while retaining the fiber and nutrient profile of hemp and meant to be used in cereals, granolas, nutrition bars, functional beverages, and more.

Sofia Elizondo, COO and co-founder of Brightseed, similarly expressed excitement as Brightseed looks to continue similar insights, in developing a “portfolio of bioactive ingredients to meet important consumer health areas.”

“Brightseed’s focus on computational bioactive discovery paired with advanced metabolomics is enabling the functional food, beverage, and dietary supplements industries to unlock the full potential of bioactives for human health,” Elizondo added.

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A new app is aiming to democratize the legal world through the power of artificial intelligence. Human attorneys tend to be quite expensive, but it turns out robot lawyers are cheap. 

The app, called “DoNotPay,” is billed as “the world’s first robot lawyer.”

It uses “artificial intelligence to help consumers fight against large corporations and solve their problems like beating parking tickets, appealing bank fees, and suing robocallers,” according to the app’s website. 

Here’s how it works, via CBS News: The “AI-creation runs on a smartphone, listens to court arguments and formulates responses for the defendant,” and the “AI lawyer tells the defendant what to say in real-time, through headphones.”

According to CBS News, the app will “be the first of its kind to help a defendant fight a traffic ticket in court next month,” and the company behind the app says that it has already “used AI-generated form letters and chatbots to help people secure refunds for in-flight Wifi that didn’t work, as well as to lower bills and dispute parking tickets, among other issues.”

The “DoNotPay” app is yet another example of “generative artificial intelligence,” technology that can generate text and other forms of content in response to human commands. 

Generative AI has attracted considerable attention from investors since last month’s release of “ChatGPT,” a chatbot from the San Francisco-based lab OpenAI that created a sensation with its music and stories. 

The New York Times reported last week that more than a million people have used “ChatGPT,” and that OpenAI “is in talks to complete a deal that would value it at around $29 billion, more than twice its valuation in 2021.”

CBS News reported that DoNotPay “has raised $27.7 million from tech-focused venture capital firms, including Andreessen Horowitz and Crew Capital.”

“In the past year, AI tech has really developed and allowed us to go back and forth in real time with corporations and governments,” DoNotPay CEO Joshua Browder told CBS News. “We spoke live [with companies and customer service reps] to lower bills with companies; and what we’re doing next month is try to use the tech in a courtroom for the first time.”

The proliferation of artificial intelligence has set off plenty of alarm bells. As Politico noted in a story this week about DoNotPay, “the public release of increasingly advanced AI tools has raised questions about everything from high school plagiarism to the very essence of what it is to be human.” 

And while many have found chatbots to be fun, a robot lawyer might just be too creepy for some. 

It also happens to be illegal in many jurisdictions. 

“Some courts allow defendants to wear hearing aids, some versions of which are bluetooth-enabled. That’s how Browder determined that DoNotPay’s technology can legally be used in this case,” CBS News reported. “However, the tech isn’t legal in most courtrooms. Some states require that all parties consent to be recorded, which rules out the possibility of a robot lawyer entering many courtrooms. Of the 300 cases DoNotPay considered for a trial of its robot lawyer, only two were feasible.”

Browder told CBS that it’s “within the letter of the law, but I don’t think anyone could ever imagine this would happen.” 

“It’s not in the spirit of law, but we’re trying to push things forward and a lot of people can’t afford legal help. If these cases are successful, it will encourage more courts to change their rules,” Browder said. 

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