Researchers at Google DeepMind have developed AlphaFold 3, an AI model that can predict the structure of and interactions between biological molecules including proteins, DNA and RNA, and small molecules that could function as drugs. Google DeepMind will make the model available for non-commercial use through AlphaFold server. The landmark innovation, the details of which were published in the journal Nature on May 8, is likely to dramatically accelerate biological research. "It's a big milestone for us today, announcing AlphaFold 3," said Demis Hassabis, CEO of Google DeepMind, at a briefing on May 7 announcing the breakthrough. "Biology is a dynamic system and you have to understand how properties of biology emerge through the interactions between different molecules in the cell. You can think of AlphaFold 3 as our first big step towards that."

Gif-sur-Yvette 91191 Abstract We suggest the implementation of the Dual Use Research of Concern (DURC) framework, originally designed for life sciences, to the domain of generative AI, with a specific focus on Large Language Models (LLMs). With its demonstrated advantages and drawbacks in biological research, we believe the DURC criteria can be effectively redefined for LLMs, potentially contributing to improved AI governance. Acknowledging the balance that must be struck when employing the DURC framework, we highlight its crucial political role in enhancing societal awareness of the impact of generative AI. As a final point, we offer a series of specific recommendations for applying the DURC approach to LLM research. Keywords: Dual Use Research of Concern (DURC), Generative AI, Large Language Models (LLMs), AI Ethics Conflict of interest No conflict of interest to report. Funding This research was supported through projects TechEthos (grant number 101006249) and MultiRATE (grant number 101073929) funded by the European Commission Horizon program. Ethics approval No human subjects were involved in the study. Consent No data needing consent has been used. Data availability statement In this article, we do not analyze or generate any datasets. Author Contribution All authors contributed to the study conception and design. Sections 1 and 4 were written with equal contribution. Sections 2 and 3 were conceived by Adomaitis and later edited by Grinbaum.

Welcome to our August 2023 monthly digest, where you can catch up with any AIhub stories you may have missed, peruse the latest news, find out about recent events, and more. This month, we take a whistle-stop tour around some of the big conferences, popping in to IJCAI, AIES and ICML, find out about interdisciplinary methods in computational creativity, and say goodbye to a well-loved podcast. Nadia Ady and Faun Rice are working on a research project exploring where AI researchers find inspiration and ideas about human intelligence, and what approaches they use to translate ideas from the disciplines that study human intelligence (e.g. We spoke to Nadia and Faun about the project, what they've learnt so far, and how they plan to further develop the work. The 32nd International Joint Conference on Artificial Intelligence (IJCAI 2023) took place in Macao from 19-25 August 2023. The programme included plenary talks, workshops, symposia and tutorials.

The programme of the International Conference on Machine Learning (ICML) featured an invited talk by Jennifer Doudna entitled "The future of ML in biology: CRISPR for health and climate". Jennifer Doudna and Emmanuelle Charpentier won the 2020 Nobel Prize in Chemistry for "the development of a method for genome editing". The method in question is often referred to as CRISPR/Cas9 genetic scissors. Using this technique, researchers can change the DNA of animals, plants and microorganisms with extremely high precision. This technology has already had a huge impact on the biological sciences.

Space biology research aims to understand fundamental effects of spaceflight on organisms, develop foundational knowledge to support deep space exploration, and ultimately bioengineer spacecraft and habitats to stabilize the ecosystem of plants, crops, microbes, animals, and humans for sustained multi-planetary life. To advance these aims, the field leverages experiments, platforms, data, and model organisms from both spaceborne and ground-analog studies. As research is extended beyond low Earth orbit, experiments and platforms must be maximally autonomous, light, agile, and intelligent to expedite knowledge discovery. Here we present a summary of recommendations from a workshop organized by the National Aeronautics and Space Administration on artificial intelligence, machine learning, and modeling applications which offer key solutions toward these space biology challenges. In the next decade, the synthesis of artificial intelligence into the field of space biology will deepen the biological understanding of spaceflight effects, facilitate predictive modeling and analytics, support maximally autonomous and reproducible experiments, and efficiently manage spaceborne data and metadata, all with the goal to enable life to thrive in deep space.

Scientists last month unveiled the most exhaustive database yet of the proteins that form the building blocks of life, in a breakthrough where observers said would "fundamentally change biological research". Every cell in every living organism is triggered to perform its function by proteins that deliver constant instructions to maintain health and ward off infection. Unlike the genome -- the complete sequence of human genes that encode cellular life -- the human proteome is constantly changing in response to genetic instructions and environmental stimuli. Understanding how proteins operate -- the shape in which they end up, or "fold" into -- within cells has fascinated scientists for decades. But determining each protein's precise function through direct experimentation is painstaking.

Scientists on Thursday unveiled the most exhaustive database yet of the proteins that form the building blocks of life, in a breakthrough observers said would "fundamentally change biological research". Every cell in every living organism is triggered to perform its function by proteins that deliver constant instructions to maintain health and ward off infection. Unlike the genome -- the complete sequence of human genes that encode cellular life -- the human proteome is constantly changing in response to genetic instructions and environmental stimuli. Understanding how proteins operate -- the shape in which they end up, or "fold" into -- within cells has fascinated scientists for decades. But determining each protein's precise function through direct experimentation is painstaking.

While some of the applications for artificial intelligence involve say, winning games of Texas hold'em or recreating pretty paintings, there are areas where the technology could have truly profound consequences. Among those is medical care, and a major breakthrough from Alphabet's DeepMind AI could be a gamechanger in this regard, with the system demonstrating an ability to predict the 3D structures of unique proteins, overcoming a problem that has plagued biologists for half a century. By understanding the 3D shapes of different proteins, scientists can better understand what they do and how the cause diseases, which in turn paves the way for better drug discovery. Beyond that, as a central component to the chemical processes for all living things, more expedient mapping of 3D protein structures would benefit many fields of biological research, but this process has proven painstaking. This is because while modern scientific tools such as X-ray crystallography and cryo-electron microscopy allow researchers to study these structures in amazing new detail, they all still hinge on a process of trial and error.

Once the three-billion-letter-long human genome was sequenced, we rushed into a new "omics" era of biological research. Scientists are now racing to sequence the genomes (all the genes) or proteomes (all the proteins) of various organisms – and in the process are compiling massive amounts of data. For instance, a scientist can use "omics" tools such as DNA sequencing to tease out which human genes are affected in a viral flu infection. But because the human genome has at least 25,000 genes in total, the number of genes altered even under such a simple scenario could potentially be in the thousands. Although sequencing and identifying genes and proteins gives them a name and a place, it doesn't tell us what they do.

A New Biology for a New Century Obstacles to an Exponential Increase in Synthetic Biology Productivity Machine Learning's Predictive Capabilities Machine Learning Needs Automation To Be Truly Effective Predictive Synthetic Biology Will Dramatically Impact Biology and Inspire Computer Science Biology has changed radically in the past two decades, transitioning from a descriptive science into a design science. The discovery of DNA as the repository of genetic information, and of recombinant DNA as an effective way to modify it, has first led into the development of genetic engineering and later the field of synthetic biology. Synthetic biology(1) goes beyond the historical practice of a biological research based on describing and cataloguing (e.g., Linnaean taxonomic classification or phylogenetic tree development), and aims to design biological systems to a given specification (e.g., production of a given amount of a medical drug or targeted invasion of a specific type of cancer cell). This transition into an industrialized synthetic biology is expected to affect most human activities, from improving human health, to producing renewable biofuels to combat climate change.(2) Some examples commercially available now include synthetic leather and spider silk, renewable biodiesel that propels the Rio de Janeiro public bus system, vegan burgers with meat taste, and sustainable skin-rejuvenating cosmetics.