Quantum Mechanics in Biomolecular Simulations: Benefits and Cost-Reduction Strategies for Integrated QM/MM Approaches

Resumo

Quantum calculations play a fundamental role in understanding biological systems at the atomic level, as
they allow detailed investigation of electronic interactions, enzymatic mechanisms, and molecular recognition
processes. However, the direct application of these methods to large biomolecules is limited by the high
computational cost involved. An efficient alternative consists of combining classical Molecular Mechanics
(MM) simulations, such as molecular dynamics, with quantum mechanical (QM) methods, enabling a more
comprehensive multiscale description. This hybrid approach, however, introduces another challenge: classical
simulations generate a very large number of conformations, which makes it impractical to perform quantum
calculations on every frame. Therefore, the efficiency of this strategy depends on selecting a reduced and
representative set of structures to be analyzed in subsequent quantum stages. In this mini-review, we present
three complementary methods to achieve this selection: (1) Clustering, (2) Statistical Inefficiency (SI), and
(3) Optimal Wavelet Signal Compression Analysis (OWSCA). This filtering step makes it possible to explore
quantum effects with high accuracy without compromising conformational representativeness, drastically
reducing the computational cost and expanding the applicability of a sequential workflow of hybrid QM/
MM methodologies to the study of complex biological systems.
Keywords: QM/MM calculations; computational cost; clustering methods; statistical inefficiency;
OWSCA.