Advancing predictive modeling in computational chemistry through quantum chemistry, molecular mechanics, and machine learning

Abstract

Computational chemistry plays a critical role in advancing molecular science by bridging theoretical frameworks and experimental observations. It provides detailed insight into the structural, electronic, and reactive properties of molecules and materials. This article examines recent developments that are influencing the direction of the field, with a focus on the integration of quantum chemistry (QC), molecular mechanics (MM), and machine learning (ML) into cohesive modeling strategies. The objective is to assess how these combined approaches are improving the accuracy of simulations, informing molecular design, and contributing to progress in areas such as drug discovery, catalysis, and materials engineering. The review covers core methodologies including ab initio quantum mechanical calculations, hybrid quantum mechanics/molecular mechanics (QM/MM) models, and classical MD techniques. It also discusses emerging advances in data-driven models and neural network-based potentials. More attention is given to recent studies (2018–2025) that demonstrate how these techniques complement one another in addressing complex chemical systems. The analysis shows that combining quantum methods with ML enhances electronic structure predictions, while molecular mechanics provides efficient, large-scale modeling of structural and energetic properties across diverse environments, especially when coupled with simulation techniques such as molecular dynamics or Monte Carlo. In synergy, these tools support the construction of more robust and scalable models, narrowing the gap between computational results and laboratory findings. The article concludes by outlining the expanding influence of integrated computational approaches and their potential to drive innovation across scientific disciplines.

Topics & Concepts