If you want to describe a particular color, you could look to the Pantone color wheel to find its exact hue, saturation and brightness, and how it compares to other colors. But nothing like that has existed for complex odors.

Now, research co-authored by Monell has gotten closer to that work, using machine learning to discriminate among scents and how they relate to each other. The work has potential applications in diagnosing some health conditions like diabetes or liver failure that have distinct olfactory signatures, as well as in the food, flavor, and fragrance industries.

In two Perspectives, authors highlight the strengths and limitations of “self-driving” laboratories, spaces that combine robotics, high-throughput experiments, artificial intelligence (AI), and automated analysis to transform scientific discovery. They were envisioned to help discovery go from a slow process of trial and error into a continuous, adaptive learning cycle. These systems are already being used to discover molecules and optimize materials, sometimes exploring enormous experimental spaces while using far less material and time than conventional approaches.

In one Perspective, Milad Abolhasani highlights a major challenge in using AI tools in laboratories. These tools can generate hypotheses far faster than physical laboratories can test them. Linking instruments, robots, software, and shared datasets could allow laboratories to learn from one another, uncover patterns hidden in complex systems, and reduce redundant experiments. At the same time, greater autonomy will require rigorous safety measures, transparent decision-making, standardized data, and broader access to prevent these technologies from becoming concentrated among a small number of institutions. Ultimately, Abolhasani envisions autonomous laboratories augmenting scientists rather than replacing them, with human researchers setting goals and interpreting evidence while intelligent systems handle much of the experimental exploration.

In another Perspective, Martin Burke and colleagues highlight “blocc” chemistry. Blocc chemistry is a modular approach to building small molecules from standardized chemical building blocks that could make organic synthesis faster, more automated, and accessible to nonspecialists. By enabling robots to repeatedly assemble carbon-carbon bonds, Burke suggests that the method could be used to generate large, standardized datasets that AI systems could use to predict and optimize the properties of new molecules, creating a feedback loop between automated synthesis, testing, and machine learning. According to the author, the approach has already produced promising materials for applications including organic electronics and solar cells. What’s more, blocc chemistry could also democratize molecular discovery and transform chemistry education. However, broader access will require standardized methods as well as safeguards to ensure that automated chemical innovation is used safely and responsibly.