Proteins often rely on conformational dynamics to perform their biological functions. A detailed understanding of protein dynamics is fundamental to revealing the biophysical principles of life and to accelerating therapeutic discovery. However, purely data-driven artificial intelligence (AI) methods face significant challenges in capturing the full spectrum of protein conformational dynamics. This review highlights recent advances in overcoming these challenges through the integration of biophysical constraints with AI-driven approaches. By combining fundamental biophysical principles, experimentally measured biophysical data, and physics-based methodologies into AI models, the integrated approaches show promise in enhancing both the performance and interpretability of protein dynamics predictions. Several key perspectives and future directions in the field are also discussed.
Huang et al. (Tue,) studied this question.