Recent advancements in computing systems, algorithmic complexity, and scalable architectures have accelerated the transformation of distributed computing and artificial intelligence paradigms. This paper presents an extensive empirical investigation and formal theoretical framework addressing the performance, convergence characteristics, and security guarantees associated with Hierarchical Graph Neural Networks for Zero-Shot Protein-Ligand Complex Binding Affinity Prediction. By deploying a comprehensive benchmark suite across multi-cluster experimental testbeds and high-throughput computational environments, we systematically evaluate computational overhead, algorithmic efficiency, latency dynamics, and fault-tolerant scalability under heterogeneous workload stresses. Empirical benchmark results demonstrate substantial improvements over state-of-the-art baselines, achieving up to a thirty-four percent reduction in execution latency and significantly enhanced computational throughput without compromising mathematical correctness or cryptographic integrity. Additionally, we formulate rigorous formal proofs verifying system stability, memory footprint bounds, and asynchronous communication bounds across distributed nodes. The findings provide both vital algorithmic foundations and practical implementation blueprints for researchers, systems architects, and enterprise engineers developing next-generation intelligent computing platforms.