20.500.12556/RUL-156153 Machine learning heralding a new development phase in molecular dynamics simulations Molecular dynamics (MD) simulations are a key computational chemistry technique that provide dynamic insight into the underlying atomic-level processes in the system under study. These insights not only improve our understanding of the molecular world, but also aid in the design of experiments and targeted interventions. Currently, MD is associated with several limitations, the most important of which are: insufficient sampling, inadequate accuracy of the atomistic models, and challenges with proper analysis and interpretation of the obtained trajectories. Although numerous efforts have been made to address these limitations, more effective solutions are still needed. The recent development of artificial intelligence, particularly machine learning (ML), offers exciting opportunities to address the challenges of MD. In this review we aim to familiarize readers with the basics of MD while highlighting its limitations. The main focus is on exploring the integration of deep learning with MD simulations. The advancements made by ML are systematically outlined, including the development of ML-based force fields, techniques for improved conformational space sampling, and innovative methods for trajectory analysis. Additionally, the challenges and implications associated with the integration of ML and artificial intelligence are discussed. While the potential of ML-MD fusion is clearly established, further applications are needed to confirm its superiority over traditional methods. This comprehensive overview of the new perspectives of MD, which ML has opened up, serves as a gentle introduction to the exciting phase of MD development. molecular dynamics simulations machine learning deep learning artificial intelligence fizikalna kemija molekularna dinamika strojno učenje simulacije true false true Angleški jezik Slovenski jezik Članek v reviji 2024-05-10 16:15:01 2024-05-10 16:15:01 2024-05-11 03:58:06 0000-00-00 00:00:00 2024 0 0 36 str. iss. 4, art. 102 Vol. 57 2024 0000-00-00 Zaloznikova Objavljeno NiDoloceno 0000-00-00 0000-00-00 0000-00-00 544 1573-7462 10.1007/s10462-024-10731-4 191433731 s10462-024-10731-4.pdf s10462-024-10731-4.pdf 1 9D48C4F20FA11BECE7C01F61C893E7EB 4d1d3c7ae14760a7681e310250147ba6080861ca791cde286e82b718be2127a8 47b5c029-0ed8-11ef-99d3-0050569b8976 https://repozitorij.uni-lj.si/Dokument.php?lang=slv&id=183456 https://link.springer.com/article/10.1007/s10462-024-10731-4 1 b054bea1-0ed7-11ef-99d3-0050569b8976 https://repozitorij.uni-lj.si/Dokument.php?lang=slv&id=183455 Fakulteta za farmacijo 0 0 0