Department of Molecular Biology, Massachusetts General Hospital, Boston, MA 02114, USA.
Curr Opin Struct Biol. 2024 Dec;89:102919. doi: 10.1016/j.sbi.2024.102919. Epub 2024 Sep 21.
The integration of artificial intelligence, machine learning and quantum computing into molecular dynamics simulations is catalyzing a revolution in computational biology, improving the accuracy and efficiency of simulations. This review describes the advancements and applications of these technologies to process vast molecular dynamics simulation datasets, adapt parameters of simulations and gain insight into complex biological processes. These advances include the use of predictive force fields, adaptive algorithms and quantum-assisted methodologies. While the integration of artificial intelligence and quantum computing with MD simulations provides insightful and stimulating improvements to our understanding of molecular mechanisms, it could introduce new issues related to data quality, interpretability of models and computational complexity. Modern multidisciplinary approaches are needed to navigate these challenges and exploit the potential of these emerging technologies for MD simulations of biomolecular systems.
人工智能、机器学习和量子计算的整合正在推动计算生物学的革命,提高了模拟的准确性和效率。本文综述了这些技术在处理大规模分子动力学模拟数据集、适应模拟参数和深入了解复杂生物过程方面的进展和应用。这些进展包括使用预测力场、自适应算法和量子辅助方法。虽然人工智能和量子计算与 MD 模拟的整合为我们对分子机制的理解提供了有见地和激励性的改进,但它可能会引入与数据质量、模型可解释性和计算复杂性相关的新问题。需要采用现代多学科方法来应对这些挑战,并利用这些新兴技术在生物分子系统的 MD 模拟中的潜力。