Department of Biochemical Engineering and Biotechnology, Indian Institute of Technology Delhi, New Delhi 110016, India.
Institute of Analytical and Bioanalytical Chemistry, Ulm University, Albert-Einstein-Allee 11, 89081 Ulm, Germany.
Int J Mol Sci. 2023 Apr 5;24(7):6785. doi: 10.3390/ijms24076785.
The rational design of molecularly imprinted polymers has evolved along with state-of-the-art experimental imprinting strategies taking advantage of sophisticated computational tools. In silico methods enable the screening and simulation of innovative polymerization components and conditions superseding conventional formulations. The combined use of quantum mechanics, molecular mechanics, and molecular dynamics strategies allows for macromolecular modelling to study the systematic translation from the pre- to the post-polymerization stage. However, predictive design and high-performance computing to advance MIP development are neither fully explored nor practiced comprehensively on a routine basis to date. In this review, we focus on different steps along the molecular imprinting process and discuss appropriate computational methods that may assist in optimizing the associated experimental strategies. We discuss the potential, challenges, and limitations of computational approaches including ML/AI and present perspectives that may guide next-generation rational MIP design for accelerating the discovery of innovative molecularly templated materials.
分子印迹聚合物的合理设计是随着最先进的实验印迹策略的发展而发展的，这些策略利用了复杂的计算工具。计算方法可以筛选和模拟创新的聚合成分和条件，取代传统的配方。量子力学、分子力学和分子动力学策略的结合允许进行大分子建模，以研究从预聚合阶段到聚合后阶段的系统转化。然而，到目前为止，预测设计和高性能计算来推进 MIP 的发展还没有得到充分的探索和全面的实践。在这篇综述中，我们专注于分子印迹过程的不同步骤，并讨论了适当的计算方法，这些方法可能有助于优化相关的实验策略。我们讨论了包括 ML/AI 在内的计算方法的潜力、挑战和局限性，并提出了可能指导下一代合理 MIP 设计的观点，以加速创新的分子模板材料的发现。
