Machine-Driven Interaction Discovery

Biological systems derive from complex interactions between entities ranging from biomolecules to macroscopic structures, forming intricate networks essential for understanding disease mechanisms and developing therapeutic interventions. Current AI-driven interaction predictors typically operate in isolation, focusing on single tasks and missing the broader picture of how different biological interactions influence each other. Traditional wet-lab approaches for identifying these interactions are expensive, time-consuming, and error-prone. No unified platform currently exists where biologists can predict and analyze multiple types of biological relationships comprehensively, limiting our ability to discover new therapeutic applications and fully understand interconnected biological mechanisms.