Researchers at University of California San Diego analyzed the genomes of hundreds of malaria parasites to determine which genetic variants are most likely to confer drug resistance.
The findings, published in Science, could help scientists use machine learning to predict antimalarial drug resistance and more effectively prioritize the most promising experimental treatments for further development. The approach could also help predict treatment resistance in other infectious diseases, and even cancer.
“A lot of drug resistance research can only look at one chemical agent at a time, but what we’ve been able to do here is create a roadmap for understanding antimalaria drug resistance across more than a hundred different compounds,” said Elizabeth Winzeler, Ph.D., a professor at UC San Diego Skaggs School of Pharmacy and Pharmaceutical Sciences and the Department of Pediatrics at UC San Diego School of Medicine.
Australian researchers have created building blocks out of DNA to construct a series of nano-scale objects and shapes, from a rod and a square to an infinitesimally small dinosaur.
The approach turns DNA into a modular material for building nanostructures – thousands of times narrower than a human hair. Developed by researchers from the University of Sydney Nano Institute and published in the journal Science Robotics, it suggests exciting possibilities for future use of nanobot technology.
A quiet revolution is brewing in labs around the world, where scientists’ use of AI is growing exponentially. One in three postdocs now use large language models to help carry out literature reviews, coding, and editing. In October, the creators of our AlphaFold 2 system, Demis Hassabis and John Jumper became Nobel Laureates in Chemistry for using AI to predict the structure of proteins, alongside the scientist David Baker, for his work to design new proteins. Society will soon start to feel these benefits more direct ly, with drugs and materials designed with the help of AI currently making their way through development.
In this essay, we take a tour of how AI is transforming scientific disciplines from genomics to computer science to weather forecasting. Some scientists are training their own AI models, while others are fine-tuning existing AI models, or using these models’ predictions to accelerate their research. Scientists are using AI as a scientific instrument to help tackle important problems, such as designing proteins that bind more tightly to disease targets, but are also gradually transforming how science itself is practised.
There is a growing imperative behind scientists’ embrace of AI. In recent decades, scientists have continued to deliver consequential advances, from Covid-19 vaccines to renewable energy. But it takes an ever larger number of researchers to make these breakthroughs, and to transform them into downstream applications. As a result, even though the scientific workforce has grown significantly over the past half-century, rising more than seven fold in the US alone, the societal progress that we would expect to follow, has slowed. For instance, much of the world has witnessed a sustained slowdown in productivity growth that is undermining the quality of public services. Progress towards the 2030 Sustainable Development Goals, which capture the biggest challenges in health, the environment, and beyond, is stalling.
A tiny, four-fingered “hand” folded from a single piece of DNA can pick up the virus that causes COVID-19 for highly sensitive rapid detection and can even block viral particles from entering cells to infect them, University of Illinois Urbana-Champaign researchers report. Dubbed the NanoGripper, the nanorobotic hand also could be programmed to interact with other viruses or to recognize cell surface markers for targeted drug delivery, such as for cancer treatment.
Led by Xing Wang, a professor of bioengineering and of chemistry at the U. of I., the researchers describe their advance in the journal Science Robotics.
Inspired by the gripping power of the human hand and bird claws, the researchers designed the NanoGripper with four bendable fingers and a palm, all in one nanostructure folded from a single piece of DNA. Each finger has three joints, like a human finger, and the angle and degree of bending are determined by the design on the DNA scaffold.
Researchers at the University of Sydney Nano Institute have made a significant advance in the field of molecular robotics by developing custom-designed and programmable nanostructures using DNA origami.
This innovative approach has potential across a range of applications, from targeted drug delivery systems to responsive materials and energy-efficient optical signal processing. The method uses “DNA origami,” so-called as it uses the natural folding power of DNA, the building blocks of human life, to create new and useful biological structures.
As a proof-of-concept, the researchers made more than 50 nanoscale objects, including a “nano-dinosaur,” a “dancing robot” and a mini-Australia that is 150 nanometers wide, a thousand times narrower than a human hair.
Every day, researchers at the Department of Energy’s SLAC National Accelerator Laboratory tackle some of the biggest questions in science and technology—from laying the foundations for new drugs to developing new battery materials and solving big data challenges associated with particle physics and cosmology.
To get a hand with that work, they are increasingly turning to artificial intelligence. “AI will help accelerate our science and technology further,” said Ryan Coffee, a SLAC senior scientist. “I am really excited about that.”
