Recent development in Artificial Intelligence (AI) models has propelled their application in scientific discovery, but the validation and exploration of these discoveries require subsequent empirical experimentation. The concept of self-driving laboratories promises to automate and thus boost the experimental process following AI-driven discoveries. However, the transition of experimental protocols, originally crafted for human comprehension, into formats interpretable by machines presents significant challenges, which, within the context of specific expert domain, encompass the necessity for structured as opposed to natural language, the imperative for explicit rather than tacit knowledge, and the preservation of causality and consistency throughout protocol steps. Presently, the task of protocol translation predominantly requires the manual and labor-intensive involvement of domain experts and information technology specialists, rendering the process time-intensive. To address these issues, we propose a framework that automates the protocol translation process through a three-stage workflow, which incrementally constructs Protocol Dependence Graphs (PDGs) that approach structured on the syntax level, completed on the semantics level, and linked on the execution level.

Accelerating drug discovery with Artificial: a whole-lab orchestration and scheduling system for self-driving labs Y ao Fehlis, Paul Mandel, Charles Crain, Betty Liu, David Fuller a a Artificial Inc.,Abstract Self-driving labs are transforming drug discovery by enabling automated, AI-guided experimentation, but they face challenges in orchestrating complex workflows, integrating diverse instruments and AI models, and managing data e fficiently. Artificial addresses these issues with a comprehensive orchestration and scheduling system that unifies lab operations, automates workflows, and integrates AI-driven decision-making. By incorporating AI / ML models like NVIDIA BioNeMo--which facilitates molecular interaction prediction and biomolecular analysis--Artificial enhances drug discovery and accelerates data-driven research. Through real-time coordination of instruments, robots, and personnel, the platform streamlines experiments, enhances reproducibility, and advances drug discovery. Introduction The landscape of drug discovery has long been characterized by a multitude of challenges, including the high costs of research and development, lengthy timelines, and a significant rate of failure during clinical trials (Blanco-Gonzalez et al., 2023; Udegbe et al., 2024; Khanna, 2012; Mo ffat et al., 2017).

Proteins were made in a laboratory by a completely autonomous robot.Credit: Panther Media GmbH/Alamy A'self-driving' laboratory comprising robotic equipment directed by a simple artificial intelligence (AI) model successfully reengineered enzymes without any input from humans -- save for the occasional hardware fix. "It is cutting-edge work," says Héctor García Martín, a physicist and synthetic biologist at Lawrence Berkeley National Laboratory in Berkeley, California. "They are fully automating the whole process of protein engineering." Self-driving labs meld robotic equipment with machine-learning models capable of directing experiments and interpreting results to design new procedures. The hope, say researchers, is that autonomous labs will turbo-charge the scientific process and come up with solutions that humans might not have thought of on their own.

Self Driving Labs (SDLs) that combine automation of experimental procedures with autonomous decision making are gaining popularity as a means of increasing the throughput of scientific workflows. The task of identifying quantities of supplied colored pigments that match a target color, the color matching problem, provides a simple and flexible SDL test case, as it requires experiment proposal, sample creation, and sample analysis, three common components in autonomous discovery applications. We present a robotic solution to the color matching problem that allows for fully autonomous execution of a color matching protocol. Our solution leverages the WEI science factory platform to enable portability across different robotic hardware, the use of alternative optimization methods for continuous refinement, and automated publication of results for experiment tracking and post-hoc analysis.

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.

Kebotix, a technology company ushering in the future of new materials discovery, came out of stealth mode with a $5 million seed round led by One Way Ventures. Investors also include Baidu Ventures, an independent venture fund with backing and resources from Baidu; Boston-based Flybridge Capital Partners; Los Angeles-based Embark Ventures; Norway-based Propagator Ventures; and New York-based WorldQuant Ventures. Developing the world's first self-driving lab for materials discovery powered by artificial intelligence (AI) and robotics, Kebotix is committed to accelerating the exploration, discovery, applications, and production of new molecules and materials. "We are building the materials company of the 21st century because how scientists discover new materials has not evolved since the 18th century," said CEO Dr. Jill S. Becker. "Being stuck in the 18th century significantly adds to the challenge of tackling climate change, antibiotic-resistant bacteria, water pollution, and other urgent problems facing the world today."