Fareed Muhammad Mazhar, Shityakov Sergey
Department of Computer Science, School of Science and Engineering, Università Degli Studi di Verona, 37134 Verona, Italy.
Laboratory of Bioinformatics, Department of Bioinformatics, Biocenter, Würzburg University, 97080 Würzburg, Germany.
Polymers (Basel). 2025 May 16;17(10):1373. doi: 10.3390/polym17101373.
Hydrogels are pivotal in advanced materials, driving innovations in medical fields, such as targeted drug delivery, regenerative medicine, and skin repair. This systematic review explores the transformative impact of in-silico design on hydrogel development, leveraging computational tools such as molecular dynamics, finite element modeling, and artificial intelligence to optimize synthesis, characterization, and performance. We analyze cutting-edge strategies for tailoring the physicochemical properties of hydrogels, including their mechanical strength, biocompatibility, and stimulus responsiveness, to meet the needs of next-generation biomedical applications. By integrating machine learning and computational modeling with experimental validation, this review highlights how in silico approaches accelerate material innovation, addressing challenges and outlining future directions for scalable, personalized hydrogel solutions in regenerative medicine and beyond.
水凝胶在先进材料领域至关重要，推动着医学领域的创新，如靶向药物递送、再生医学和皮肤修复。本系统综述探讨了计算机辅助设计对水凝胶开发的变革性影响，利用分子动力学、有限元建模和人工智能等计算工具来优化合成、表征和性能。我们分析了用于定制水凝胶物理化学性质（包括机械强度、生物相容性和刺激响应性）的前沿策略，以满足下一代生物医学应用的需求。通过将机器学习和计算建模与实验验证相结合，本综述强调了计算机辅助方法如何加速材料创新，应对挑战，并概述了再生医学及其他领域中可扩展的个性化水凝胶解决方案的未来方向。
