If you had to guess how long it takes for a drug to go from an idea to your pharmacy, what would you guess? Three years? Five years? How about the cost? $30 million? $100 million?<\/p>\n\n\n\n
Well, here\u2019s the sobering truth: 90 percent of all drug possibilities fail. The few that do succeed take an average of 10 years to reach the market and cost anywhere from $2.5 billion to $12 billion to get there.<\/p>\n\n\n\n
But what if we could generate novel molecules to target any disease, overnight, ready for clinical trials? Imagine leveraging machine learning to accomplish with 50 people what the pharmaceutical industry can barely do with an army of 5,000.<\/p>\n\n\n\n
Welcome to the future of AI and low-cost, ultra-fast, and personalized drug discovery. Let\u2019s dive in.<\/p>\n\n\n\n
Around 2012, computer scientist-turned-biophysicist Alex Zhavoronkov<\/a> started to notice that artificial intelligence<\/a> was getting increasingly good at image, voice, and text recognition. He knew that all three tasks shared a critical commonality. In each, massive datasets were available, making it easy to train up an AI.<\/p>\n\n\n\n But similar datasets were present in pharmacology. So, back in 2014, Zhavoronkov started wondering if he could use these datasets and AI to significantly speed up the drug discovery process. He\u2019d heard about a new technique in artificial intelligence known as generative adversarial networks (or GANs). By pitting two neural nets against one another (adversarial), the system can start with minimal instructions and produce novel outcomes (generative). At the time, researchers had been using GANs to do things like design new objects or create one-of-a-kind, fake human faces<\/a>, but Zhavoronkov wanted to apply them to pharmacology.<\/p>\n\n\n\n He figured GANs would allow researchers to verbally describe drug attributes: \u201cThe compound should inhibit protein X at concentration Y with minimal side effects in humans,\u201d and then the AI could construct the molecule from scratch. To turn his idea into reality, Zhavoronkov set up Insilico Medicine<\/a> on the campus of Johns Hopkins University in Baltimore, Maryland, and rolled up his sleeves.<\/p>\n\n\n\n Instead of beginning their process in some exotic locale, Insilico\u2019s \u201cdrug discovery engine\u201d sifts millions of data samples to determine the signature biological characteristics of specific diseases. The engine then identifies the most promising treatment targets and\u2014using GANs\u2014generates molecules (that is, baby drugs) perfectly suited for them. \u201cThe result is an explosion in potential drug targets and a much more efficient testing process,\u201d says Zhavoronkov. \u201cAI allows us to do with fifty people what a typical drug company does with five thousand.\u201d<\/p>\n\n\n\n The results have turned what was once a decade-long war into a month-long skirmish.<\/p>\n\n\n\n In late 2018, for example, Insilico was generating novel molecules in fewer than 46 days, and this included not just the initial discovery, but also the synthesis of the drug and its experimental validation in computer simulations.<\/p>\n\n\n\n Right now, they\u2019re using the system to hunt down new drugs for cancer, aging, fibrosis, Parkinson\u2019s, Alzheimer\u2019s, ALS, diabetes, and many others. The first drug to result from this work, a treatment for hair loss, is slated to start Phase I trials by the end of 2020.<\/p>\n\n\n\n They\u2019re also in the early stages of using AI to predict the outcomes of clinical trials in advance of the trial. If successful, this technique will enable researchers to strip a bundle of time and money out of the traditional testing process.<\/p>\n\n\n\n Beyond inventing new drugs, AI is also being used by other scientists to identify new drug targets\u2014that is, the place to which a drug binds in the body and another key part of the drug discovery process.<\/p>\n\n\n\n Between 1980 and 2006, despite an annual investment of $30 billion, researchers only managed to find about five new drug targets a year. The trouble is complexity. Most potential drug targets are proteins, and a protein\u2019s structure\u2014meaning the way a 2D sequence of amino acids folds into a 3D protein\u2014determines its function.<\/p>\n\n\n\n But a protein with merely a hundred amino acids (a rather small protein) can produce a googol-cubed worth of potential shapes\u2014that\u2019s a one followed by three hundred zeroes. This is also why protein-folding<\/a> has long been considered an intractably hard problem for even the most powerful of supercomputers.<\/p>\n\n\n\n Back in 1994, to monitor supercomputers\u2019 progress in protein-folding, a biannual competition was created. Until 2018, success was fairly rare. But then the creators of DeepMind turned their neural networks loose on the problem. They created an AI that mines enormous datasets to determine the most likely distance between a protein\u2019s base pairs and the angles of their chemical bonds\u2014aka, the basics of protein-folding. They called it AlphaFold.<\/p>\n\n\n\n On its first foray into the competition, contestant AIs were given 43 protein-folding problems to solve. AlphaFold got 25 right. The second-place team managed a meager three. By predicting the elusive ways in which various proteins fold on the basis of their amino acid sequences, AlphaFold may soon have a tremendous impact in aiding drug discovery and fighting some of today\u2019s most intractable diseases.<\/p>\n\n\n\n Another theater of war for improved drugs is the realm of drug delivery. Even here, converging exponential technologies are paving the way for massive implications in both human health and industry shifts.<\/p>\n\n\n\n One key contender is CRISPR<\/a>, the fast-advancing gene-editing technology that stands to revolutionize synthetic biology and treatment of genetically linked diseases. And researchers have now demonstrated how this tool can be applied to create materials that shape-shift on command. Think: materials that dissolve instantaneously when faced with a programmed stimulus, releasing a specified drug at a highly targeted location.<\/p>\n\n\n\n Yet another potential boon for targeted drug delivery is nanotechnology, whereby medical nanorobots have now been used to fight incidences of cancer. In a recent review of medical micro- and nanorobotics, lead authors (from the University of Texas at Austin and University of California, San Diego) found numerous successful tests of in vivo operation of medical micro- and nanorobots.<\/p>\n\n\n\n Covid-19 is uniting the global scientific community with its urgency, prompting scientists to cast aside nation-specific territorialism, research secrecy, and academic publishing politics in favor of expedited therapeutic and vaccine development efforts. And in the wake of rapid acceleration across healthcare technologies, Big Pharma is an area worth watching right now, no matter your industry. Converging technologies will soon enable extraordinary strides in longevity and disease prevention, with companies like Insilico leading the charge.<\/p>\n\n\n\n Riding the convergence of massive datasets, skyrocketing computational power, quantum computing, cognitive surplus capabilities, and remarkable innovations in AI, we are not far from a world in which personalized drugs, delivered directly to specified targets, will graduate from science fiction to the standard of care.<\/p>\n\n\n\n Rejuvenational biotechnology will be commercially available sooner than you think. When I asked Alex for his own projection, he set the timeline at \u201cmaybe 20 years\u2014that\u2019s a reasonable horizon for tangible rejuvenational biotechnology.\u201d<\/p>\n\n\n\n How might you use an extra 20 or more healthy years in your life? What impact would you be able to make?<\/p>\n\n\n\n (1) A360 Executive Mastermind: If you\u2019re an exponentially and abundance-minded entrepreneur who would like coaching directly from me, consider joining my Abundance 360 Mastermind, a highly selective community of 360 CEOs and entrepreneurs who I coach for 3 days every January in Beverly Hills, Ca. Through A360, I provide my members with context and clarity about how converging exponential technologies will transform every industry. I\u2019m committed to running A360 for the course of an ongoing 25-year journey as a \u201ccountdown to the Singularity.\u201d<\/em><\/p>\n\n\n\n If you\u2019d like to learn more and consider joining our 2021 membership, apply here<\/a>.<\/em><\/p>\n\n\n\n (2) Abundance-Digital Online Community: I\u2019ve also created a Digital\/Online community of bold, abundance-minded entrepreneurs called Abundance-Digital. Abundance-Digital is Singularity University\u2019s \u2018onramp\u2019 for exponential entrepreneurs\u2014those who want to get involved and play at a higher level. Click here to learn more<\/a>.<\/em><\/p>\n\n\n\n (Both A360 and Abundance-Digital are part of Singularity University\u2014your participation opens you to a global community.)<\/em><\/p>\n\n\n\n This article originally appeared on\u00a0diamandis.com<\/a>. Read the\u00a0original article here<\/a>.<\/em><\/p>\n\n\n\n Image Credit:\u00a0andreas160578<\/a>\u00a0from\u00a0Pixabay<\/a><\/em><\/p>\n\n\n\n <\/p>\n\n\n\n <\/p>\n\n\n\n <\/a>PETER H. DIAMANDIS, MD<\/a> Diamandis is the founder and executive chairman of the XPRIZE Foundation, which leads the world in designing and operating large-scale incentive competitions. He is also the executive founder and director of Singularity University, a global learning and innovation community using exponential technologies to tackle the world\u2019s biggest challenges and build a better future for all. As an en…\u00a0Learn More<\/a><\/p>\n\n\n\n Original Article<\/a><\/strong><\/p>\n","protected":false},"excerpt":{"rendered":" If you had to guess how long it takes for a drug to go from an idea to your pharmacy, what would you guess? Three years? Five years? How about the cost? $30 million? $100 million? Well, here\u2019s the sobering truth: 90 percent of all drug possibilities fail. The few that do succeed take an […]\n","protected":false},"author":1,"featured_media":1049,"comment_status":"open","ping_status":"open","sticky":false,"template":"","format":"standard","meta":{"episode_type":"","audio_file":"","podmotor_file_id":"","podmotor_episode_id":"","cover_image":"","cover_image_id":"","duration":"","filesize":"","filesize_raw":"","date_recorded":"","explicit":"","block":"","footnotes":""},"categories":[13],"tags":[],"series":[],"class_list":["post-1048","post","type-post","status-publish","format-standard","has-post-thumbnail","hentry","category-articulos-ingles"],"episode_featured_image":"https:\/\/singularityumexico.com\/wp-content\/uploads\/2021\/08\/atom-1331961_1280-AI-drug-discovery.jpg","episode_player_image":"https:\/\/singularityumexico.com\/wp-content\/uploads\/2023\/05\/11711533-1673157178559-89a95be153719-4-scaled.jpg","download_link":"","player_link":"","audio_player":false,"episode_data":{"playerMode":"dark","subscribeUrls":{"apple_podcasts":{"key":"apple_podcasts","url":"","label":"Apple Podcasts","class":"apple_podcasts","icon":"apple-podcasts.png"},"stitcher":{"key":"stitcher","url":"","label":"Stitcher","class":"stitcher","icon":"stitcher.png"},"google_podcasts":{"key":"google_podcasts","url":"","label":"Google Podcasts","class":"google_podcasts","icon":"google-podcasts.png"},"spotify":{"key":"spotify","url":"","label":"Spotify","class":"spotify","icon":"spotify.png"}},"rssFeedUrl":"https:\/\/singularityumexico.com\/en\/feed\/podcast\/the-feedback-loop-by-singularity","embedCode":"Protein Folding<\/h3>\n\n\n\n
Drug Delivery<\/h3>\n\n\n\n
Drugs From the Future<\/h3>\n\n\n\n
\n\n\n\nJoin Me<\/em><\/h3>\n\n\n\n
Author:<\/strong><\/h4>\n\n\n\n
How AI Will Make Drug Discovery Low-Cost, Ultra-Fast, and Personalized<\/a><\/blockquote>