Ulrich Schatzschneider

Abstract Photochemical activation is a very attractive way to achieve precise spatial and temporal control of the biological action of transition‐metal complexes that behave as inactive “prodrugs” in the dark. A significant amount of work has been devoted to metal complexes that act on DNA. In this area, focus has been on ruthenium and rhodium polypyridyl compounds, but copper, iron, cobalt, and vanadium complexes also find increasing application as photoactivable DNA cleaving agents, with excitation sometimes even possible in the near IR region. Most often, the activity of these systems is based on the formation of reactive radical species. Another promising approach is the photochemical generation of covalent DNA binders from inactive precursors, as, for example, by some platinum(IV) compounds. The photolytic liberation of biologically active small molecules from inactive metal complex precursors has also become the target of recent research efforts and complements work on purely organic “caged” compounds. The significant progress made on light‐induced liberation of neurotransmitters as well as small molecule messengers like nitric oxide (NO) or carbon monoxide (CO) is also summarized here.

Cell viability studies of HT29 colon cancer cells treated with the CO-releasing compound [Mn(CO)(3)(tpm)]PF(6) revealed a significant photoinduced cytotoxicity comparable to that of established agent 5-fluorouracil (5-FU), while controls kept in the dark were unaffected at up to 100 microM.

A series of five ruthenium(II) polypyridyl complexes [Ru(bpy)2(N--N)]Cl2 was tested against human HT-29 and MCF-7 cancer cell lines. Cellular uptake efficiency and cytotoxicity were found to increase with the size of the aromatic surface area of the N--N ligand. The most active compound carrying the dppn ligand exhibits a low micromolar IC(50) value against both cell lines comparable to that of cisplatin under similar conditions. Continuous measurement of oxygen consumption, extracellular acidification rate, and impedance of the cell layer with a chip-based sensor system upon exposure to the complexes showed only small changes for the first two parameters throughout the series. A significant and irreversible decrease in impedance was, however, found for the dppn compound. This suggests that its biological activity is related to modifications in cell morphology or cell-cell and cell-matrix contacts.

Artificial intelligence (AI) and machine learning (ML) are expanding in popularity for broad applications to challenging tasks in chemistry and materials science. Examples include the prediction of properties, the discovery of new reaction pathways, or the design of new molecules. The machine needs to read and write fluently in a chemical language for each of these tasks. Strings are a common tool to represent molecular graphs, and the most popular molecular string representation, Smiles, has powered cheminformatics since the late 1980s. However, in the context of AI and ML in chemistry, Smiles has several shortcomings-most pertinently, most combinations of symbols lead to invalid results with no valid chemical interpretation. To overcome this issue, a new language for molecules was introduced in 2020 that guarantees 100% robustness: SELF-referencing embedded string (Selfies). Selfies has since simplified and enabled numerous new applications in chemistry. In this perspective, we look to the future and discuss molecular string representations, along with their respective opportunities and challenges. We propose 16 concrete future projects for robust molecular representations. These involve the extension toward new chemical domains, exciting questions at the interface of AI and robust languages, and interpretability for both humans and machines. We hope that these proposals will inspire several follow-up works exploiting the full potential of molecular string representations for the future of AI in chemistry and materials science.