Deep learning-based action recognition for analyzing drug-induced bone remodeling mechanisms.

Introduction: Understanding the mechanisms of drug-induced bone remodeling is critical for optimizing therapeutic interventions and minimizing adverse effects in bone health management. Bone remodeling is a highly dynamic process that involves the intricate interplay between osteoblasts, osteoclasts, and osteocytes, regulated by a complex network of signaling pathways and molecular interactions. Traditional experimental and computational approaches often fail to capture this dynamic and multi-scale nature, particularly when influenced by pharmacological agents, which can have both therapeutic and adverse effects. Methods: In this work, we present a novel deep learning-based framework for action recognition, specifically designed to analyze drug-induced bone remodeling mechanisms. Our framework leverages graph neural networks (GNNs) to model the spatial and temporal dependencies of multi-scale biological data, combined with a dynamic signal propagation model to identify key molecular interactions driving bone remodeling. A predictive pharmacological interaction model is integrated to quantify drug-target interactions, assess their systemic impacts, and simulate off-target effects. This approach also evaluates combinatorial drug effects, offering insights into the synergistic or antagonistic behaviors of multiple agents. Results: By incorporating these features, our method provides a comprehensive view of drug-induced changes, enabling accurate prediction of their effects on bone formation and resorption pathways. Discussion: Experimental results highlight the model's potential to advance precision medicine, enabling the development of more effective and safer therapeutic strategies for managing bone health.