The breakthrough of CRISPR technology in the past two decades has allowed biologists to refine the manipulation of DNA, to slice and dice it in order to create organisms tailored to particular purposes. That free-wheeling editing of genes, though, produces a new problem: how to organize all the complexity of the different edited pieces of DNA. That's especially important for the multi-hundred-billion-dollar portion of the drug market called biologics, basically engineered proteins that can achieve a particular purpose. If you're going to engineer new proteins through CRISPR, you need to do it in a systemic way, which is a fairly demanding combinatorial problem. Hence, some smart young biotechs are turning to deep learning forms of artificial intelligence, as deep learning is a technology that loves combinatorial problems.
DeepMind's AlphaFold represents the first time a significant scientific problem has been solved by ... [ ] AI. It can be difficult to distinguish between substance and hype in the field of artificial intelligence. In order to stay grounded, it is important to step back from time to time and ask a simple question: what has AI actually accomplished or enabled that makes a difference in the real world? This summer, DeepMind delivered the strongest answer yet to that question in the decades-long history of AI research: AlphaFold, a software platform that will revolutionize our understanding of biology. In 1972, in his acceptance speech for the Nobel Prize in Chemistry, Christian Anfinsen made a historic prediction: it should in principle be possible to determine a protein's three-dimensional shape based solely on the one-dimensional string of molecules that comprise it. Finding a solution to this puzzle, known as the "protein folding problem," has stood as a grand challenge in the field of biology for half a century.
If you had to guess how long it takes for a drug to go from an idea to your pharmacy, what would you guess? Well, here's 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. 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.
Updated An oral medication designed by scientists with the help of AI algorithms could one day treat patients with COVID-19 and other types of diseases caused by coronaviruses. Insilico Medicine, a biotech startup based in New York, announced on Tuesday it had nominated a drug candidate for preclinical trials – the stage before you start testing it on humans. Today's mRNA vaccines boost the body's immunity to COVID-19 by aiding the generation of antibodies capable of blocking the virus's spike protein, stopping the bio-nasty from infecting cells. The small molecule developed by Insilico, however, is used to treat people already infected, and works by preventing the coronavirus from replicating. The preclinical candidate has a specialized structure to target the 3C-like (3CL) protease, an enzyme involved in the viral reproduction of the SARS-CoV-2 coronavirus, Feng Ren, Insilico's chief scientific officer, explained.
For more than a decade, molecular biologist Martin Beck and his colleagues have been trying to piece together one of the world's hardest jigsaw puzzles: a detailed model of the largest molecular machine in human cells. This behemoth, called the nuclear pore complex, controls the flow of molecules in and out of the nucleus of the cell, where the genome sits. Hundreds of these complexes exist in every cell. Each is made up of more than 1,000 proteins that together form rings around a hole through the nuclear membrane. These 1,000 puzzle pieces are drawn from more than 30 protein building blocks that interlace in myriad ways. Making the puzzle even harder, the experimentally determined 3D shapes of these building blocks are a potpourri of structures gathered from many species, so don't always mesh together well. And the picture on the puzzle's box -- a low-resolution 3D view of the nuclear pore complex -- lacks sufficient detail to know how many of the pieces precisely fit together. In 2016, a team led by Beck, who is based at the Max Planck Institute of Biophysics (MPIBP) in Frankfurt, Germany, reported a model1 that covered about 30% of the nuclear pore complex and around half of the 30 building blocks, called Nup proteins.