Speak to a Protein: An Interactive Multimodal Co-Scientist for Protein Analysis
This addresses the problem of inefficient and inaccessible protein analysis for researchers, enabling real-time hypothesis generation, though it appears incremental as it builds on existing multimodal AI capabilities.
The paper tackles the slow and skill-intensive process of protein analysis by introducing 'Speak to a Protein', an interactive AI system that synthesizes literature, structures, and ligand data into multimodal dialogues, reducing the time from question to evidence and lowering barriers to advanced structural analysis.
Building a working mental model of a protein typically requires weeks of reading, cross-referencing crystal and predicted structures, and inspecting ligand complexes, an effort that is slow, unevenly accessible, and often requires specialized computational skills. We introduce \emph{Speak to a Protein}, a new capability that turns protein analysis into an interactive, multimodal dialogue with an expert co-scientist. The AI system retrieves and synthesizes relevant literature, structures, and ligand data; grounds answers in a live 3D scene; and can highlight, annotate, manipulate and see the visualization. It also generates and runs code when needed, explaining results in both text and graphics. We demonstrate these capabilities on relevant proteins, posing questions about binding pockets, conformational changes, or structure-activity relationships to test ideas in real-time. \emph{Speak to a Protein} reduces the time from question to evidence, lowers the barrier to advanced structural analysis, and enables hypothesis generation by tightly coupling language, code, and 3D structures. \emph{Speak to a Protein} is freely accessible at https://open.playmolecule.org.