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.

The synthesis of unnatural α-amino acids is an area of research that has gained a lot of attention in recent years. The availability of synthetic methods for novel unnatural αamino acid derivatives and related compounds will be a critical point in the de novo design of molecules that mimic the conformation of the natural, active peptides. These molecules (peptidomimetics) are designed to display high receptor affinity and selectivity, in addition to enhanced bioavailability and metabolic stability. This review focuses on selected recent synthetic methodologies leading to unnatural α-amino acids including chiral catalysts that enable enantioselective synthesis and microwave-assisted synthesis. Solid phase synthesis and construction of organometallic αamino acids reveal the scope of influence that this field has in organic chemistry. Additionally, the article is aimed to provide a brief insight into the biosynthetic approaches to the synthesis of α-amino acid derivatives, and to give the reader an appreciation of the field.

As the world's population progressively ages, the burden on the socio-economic and health systems is escalating, demanding sustainable and lasting solutions. Cellular senescence, one of the hallmarks of ageing, is a state of irreversible cell cycle arrest that occurs in response to various genotoxic stressors and is considered an important factor in the development of many age-related diseases and therefore a potential therapeutic target. Here, the role of senescent cells in age-related diseases is discussed, focusing on their formation and main characteristics. The mechanisms leading to senescent cells are presented, including replicative and premature senescence as well as senescence that occurs in various physiological processes, such as wound healing. The second part comprises a comprehensive description of various biomarkers currently used for the detection of senescent cells along with the investigated therapeutic approaches, namely senolytics, senomorphics and the clearance of senescent cells by the immune system. Potential delivery systems suitable for such therapies and model organisms to study senescence are also briefly examined. This in-depth overview of cellular senescence contributes to a deeper understanding of a rapidly evolving area aimed to tackle the age-related diseases in a more mechanistic way, as well as highlights future research opportunities.