### Summary
The use of artificial intelligence (AI) in scientific discovery has immense potential, allowing for advancements in drug synthesis, material design, weather forecasting, and nuclear reactor operation. AI's ability to autonomously generate knowledge and formulate hypotheses presents exciting long-term possibilities. However, challenges such as standardization, multimodal data integration, transparency of AI models, and responsible use must be addressed.
### Facts
- AI is revolutionizing scientific discovery beyond medicine, enabling faster and more accurate predictions of molecular interactions, protein folding, and nuclear reactor operation.
- The combination of AI and human expertise is impacting everyday life, such as synthesizing novel drugs, designing robust materials, and providing real-time feedback for weather forecasting.
- The future holds the potential for AI to autonomously acquire knowledge and generate hypotheses by analyzing vast amounts of scientific literature and data.
- Challenges in implementing AI include complex software and hardware engineering, the need for standardized data and models, the black-box nature of many AI models, and the misapplication and misuse of AI.
- Solutions to these challenges require interdisciplinary collaboration, involving AI specialists, engineers, government entities, corporations, and educational institutions.
Proper research data management, including the use of AI, is crucial for scientists to reproduce prior results, combine data from multiple sources, and make data more accessible and reusable, ultimately improving the scientific process and benefiting all forms of intelligence.
The global market for artificial intelligence (AI) in drug discovery is projected to grow significantly in the coming years, with emerging companies adding value and the increasing prevalence of chronic diseases driving demand, although limitations and a shortage of AI workforce may hinder growth.
Scientists have used AI to design proteins with two different states, essentially creating biological transistors that can change their shape depending on inputs, opening up new possibilities for biotechnology and medical solutions.
Chemists are developing a chemical map of all possible molecules to accelerate the discovery process for drugs and materials, with the help of artificial intelligence to determine the properties and viability of these molecules.
AI models are becoming more general purpose and can be used as powerful, adaptable tools in various fields, not just for the specific tasks they were initially trained for, opening up new possibilities for AI applications.
Main topic: Aily Labs, an AI-for-enterprise startup, raises €19m in funding to expand its team and further develop its AI models for productivity and efficiency in various industries.
Key points:
1. Aily Labs uses AI models to create products that increase productivity, efficiency, and cost-savings for clients, particularly in the pharmaceutical industry.
2. The startup differentiates itself by leveraging existing open-source AI models and utilizing a combination of machine learning approaches, including classification and regression models.
3. With the funding, Aily Labs plans to expand its GenAI team, diversify its client base beyond pharmaceutical companies, and enhance its capabilities in text generation and competitive intelligence.
AI-assisted drug discovery has led to the discovery of a new antibiotic called halicin, which has the potential to kill antibiotic-resistant bacteria, marking a significant breakthrough in addressing the public health issue of superbugs; the use of AI has expedited the drug discovery process by analyzing vast amounts of medical data and predicting the properties of molecules.
Artificial intelligence (AI) has the potential to greatly improve health care globally by expanding access to health services, according to Google's chief health officer, Karen DeSalvo. Through initiatives such as using AI to monitor search queries for potential self-harm, as well as developing low-cost ultrasound devices and automated screening for tuberculosis, AI can address health-care access gaps and improve patient outcomes.
Artificial intelligence (AI) techniques, particularly machine learning, are increasingly being used in drug research and development (R&D), with applications expanding beyond small molecules to include large-molecule modalities such as antibodies, gene therapies, and RNA-based therapies. These therapies, which make up a significant portion of the biopharma industry's current and future commercial potential, are expected to represent approximately 50% of the oncology market by revenue in 2030, with the majority coming from antibodies.
Former Google executive Mustafa Suleyman warns that artificial intelligence could be used to create more lethal pandemics by giving humans access to dangerous information and allowing for experimentation with synthetic pathogens. He calls for tighter regulation to prevent the misuse of AI.
Scientists have trained an artificial intelligence (AI) system to create an odor map that visually displays the relationships between different smells and accurately predicts what a new molecule would smell like, with the AI's descriptions outperforming those of human panelists in most cases.
AI is being used to transform the healthcare industry in New York while robots have the potential to revolutionize the beauty and cosmetics industry in California, as explained on "Eye on America" with host Michelle Miller.
Artificial intelligence (AI) has the potential to revolutionize scientific discovery by accelerating the pace of research through tools such as literature-based discovery and robot scientists, but the main obstacle is the willingness and ability of human scientists to use these tools.
Researchers have used artificial intelligence to diagnose and predict the risk of developing various diseases, including Parkinson's disease and heart failure, by analyzing images of a person's retinas, achieving better results than previous AI models; meanwhile, a "Pandora's box" of new protein shapes has been discovered through the analysis of over 200 million predicted protein structures.
Professor Brad Pentelute and the Pentelute Lab at MIT are using a combination of chemistry, biology, and engineering to develop new techniques and platforms that have the potential to revolutionize therapeutics, including using nature-inspired research to solve protein delivery problems and building an automated protein printing machine. They are also using machine learning and automation to discover new peptides and proteins that can be used in cancer treatment and other applications, with the goal of rapidly designing molecules with new functions and advancing the field of AI-driven molecule design.
The use of generative AI, combined with federated and active learning, can accelerate the development of protein drugs by improving predictions of drug properties and enabling collaboration among biopharmaceutical companies while protecting their competitive interests.
Google's AI arm, DeepMind, has developed AlphaMissense, an AI tool that can predict the harmfulness of genetic mutations with 90% accuracy, potentially aiding in the research and treatment of rare diseases.
Major drugmakers are using artificial intelligence (AI) to accelerate drug development by quickly finding patients for clinical trials and reducing the number of participants needed, potentially saving millions of dollars. AI is increasingly playing a substantial role in human drug trials, with companies such as Amgen, Bayer, and Novartis using AI tools to scan vast amounts of medical data and identify suitable trial patients, significantly reducing the time and cost of recruitment. The use of AI in drug development is on the rise, with the US FDA receiving over 300 applications that incorporate AI or machine learning in drug development from 2016 through 2022.
The use of AI tools like AlphaFold to predict protein structures and aid in drug discovery is gaining momentum, but questions remain about the quality and validation of the predicted interactions.
Researchers from the University of Eastern Finland, along with industry and supercomputers, have used machine learning to speed up virtual drug screening by 10-fold, reducing the processing time of 1.56 billion drug-like molecules.
Ginkgo Bioworks and Recursion Pharmaceuticals are both using artificial intelligence (AI) in their biotech businesses, but while Ginkgo is focused on streamlining the bioengineering and biomanufacturing process to cut costs and scale revenue, Recursion uses AI and its large dataset for drug development and plans to license its data and tools to other biotechs, making Ginkgo the better AI-enabled biotech stock for now.
Artificial intelligence (AI) is rapidly transforming various fields of science, but its impact on research and society is still unclear, as highlighted in a new Nature series which explores the benefits and risks of AI in science based on the views of over 1,600 researchers worldwide.
AI tools in science are becoming increasingly prevalent and have the potential to be crucial in research, but scientists also have concerns about the impact of AI on research practices and the potential for biases and misinformation.
Artificial intelligence (AI) tools, such as CellProfiler, are revolutionizing image-based research in the life sciences by automating image analysis and accelerating workflows, but bioinformaticians need to bridge their skills gaps and familiarize themselves with AI tools to fully harness their potential.
Researchers at Northwestern University have developed an AI-based algorithm that can design purpose-specific robots within seconds by utilizing the principles of natural evolution, compressing billions of years of evolution into an instant and removing the blindfold of evolution's lack of foresight. The algorithm can now run on an ordinary laptop computer and deliver results in less than half a minute, offering potential applications in disaster response, sewage system repairs, and medical procedures.
Scientists at the U.S. Department of Energy's Argonne National Laboratory have developed an autonomous microscopy technique that uses artificial intelligence (AI) to selectively scan points of interest, revolutionizing data acquisition and saving valuable time in experiments.
Researchers at Northwestern University in Chicago have developed an algorithm that allows an AI robot to design and create other robots in seconds, compressing billions of years of evolution into an instant.
Generative artificial intelligence (AI) is potentially shortening the drug discovery process before clinical trials, according to claims made by companies, but independent verification and clinical trials are needed to determine its efficacy.
Researchers have successfully used artificial intelligence (AI) tools, such as AlphaFold, to map the structure of a protein from the Langya henipavirus, a virus related to some deadly pathogens, allowing them to develop a prototype vaccine and highlighting the potential of AI in preparing for future pandemics.
Researchers at Northwestern University have developed an artificial intelligence program that can design functioning robots from scratch in seconds, by using a mathematical trick to predict how changes to the robots' bodies will affect their behavior.
Artificial intelligence (AI) is being used to design synthetic proteins, greatly speeding up the process of drug development and protein design in scientific research.
Researchers at Northwestern University in the United States have developed an artificial intelligence (AI) program that can design robots from scratch in just 26 seconds, representing a significant leap in AI development with the ability to generate innovative ideas and designs.
Companies are focusing on learning how to effectively deploy AI tools, realizing that poorly crafted prompts and unspecialized models can lead to inaccuracies and inefficiencies, with some firms creating prompt libraries and in-house models to improve AI output. Specialist fine-tuning and the use of libraries of embeddings are becoming crucial for companies to personalize AI models and achieve better outcomes. While some believe the importance of prompts will decrease as AI becomes more intelligent, others argue that engineered prompts will still be needed for irregular tasks.
Startup Aionics is using AI tools to accelerate the discovery and development of better batteries by quickly screening billions of candidate molecules and designing new formulations targeted at specific applications.
Scientists are using AI to design synthetic proteins in order to speed up the scientific discovery process.
Researchers at Northwestern University have developed an AI system capable of designing functional robots within seconds, demonstrating the generation of novel robot designs without human biases or previous blueprints for the first time.
