Recent breakthroughs in deep learning have revolutionized computational structural biology, yet foundational predictive architectures remain poorly understood. This dissertation proposal introduces novel algorithmic frameworks to decode the internal spatial and evolutionary reasoning of these black-box models. By extracting and analyzing network attention mechanisms, we demonstrate how AI resolves alternative structural conformations and challenges assumptions regarding its reliance on coevolutionary data. Building on these insights, the proposed research applies these interpretability frameworks to advance structure-based drug design for dynamic proteins and explores emerging quantum computing methodologies to overcome classical bottlenecks in biomolecular modeling.