Ensuring accuracy and traceability is essential in regulatory affairs, where mistakes can have serious consequences. This article introduces symbolic AI and ontologies as a structured, explainable alternative to generative models such as ChatGPT.
Making data Findable, Accessible, Interoperable, and Reusable (FAIR) is a necessity for modern R&D teams navigating digital transformation. A recent study in Chemical Science introduced a fully open-source, end-to-end workflow to "FAIRify" R&D data.
Delivering a compelling presentation is a key skill for scientists—and the opening minutes often matter most. New research shows that AI can evaluate just the beginning of your talk and still provide meaningful, targeted feedback. It’s a fast, low-effort way to sharpen your delivery where it really counts.
ICLR2025 showcased a surge of LLMs to diverse chemistry challenges, from retrosynthesis planning to materials discovery. A clear theme emerged — successful systems wrapped LLMs inside agentic workflows that orchestrate tasks, integrate chemical context, and refine outputs through feedback loops.
AI agents are modular, autonomous workflows powered by LLMs to solve complex tasks via a divide‑and‑conquer approach. They have the potential to automate tedious R&D tasks, such as patent analysis, freeing scientists to focus on genuine innovation.
