👤 Ulrich Schatzschneider

The InChI (International Chemical Identifier) standard stands as a cornerstone in chemical informatics, facilitating the structure-based identification and exchange chemical information about compounds across various platforms and databases. The InChI as a unique canonical line notation has made chemical structures searchable on the internet at a broad scale. The largest repositories working with InChIs contain more than 1 billion structures. Central to the functionality of the InChI is its codebase, which orchestrates a series of intricate steps to generate unique identifiers for chemical compounds. Up to now, these steps have been sparsely documented and the InChI algorithm had to be seen as a black box. For the new v1.07 release, the code has been analyzed and the major steps documented, more than 3000 bugs and security issues, as well as nearly 60 Google OSS-Fuzz issues have been fixed. New test systems have been implemented that allow users to directly test the code developments. The move to GitHub has not only made the development more transparent but will also enable external contributors to join the further development of the InChI code. Motivation for this modernisation was the urgency to treat molecular inorganic compounds by the InChI in a meaningful way. Until now, no classic string representation fulfills this need of molecular inorganic chemistry. Currently bonds to metal centers are by definition disconnected which makes most inorganic InChIs meaningless at the moment. Herein, we propose new routines to remedy this problem in the representation of molecular inorganic compounds by the InChI. Show less

The biological activity of Pd(II) and Pt(II) complexes toward three different cancer cell lines as well as inhibition of selenoenzyme thioredoxin reductase (TrxR) was modulated in an unexpected way by the introduction of triazolate as a "protective group" to the inner metal coordination sphere using the iClick reaction of [M(N₃)(terpy)]PF₆ [M = Pd(II) or Pt(II) and terpy = 2,2':6',2″-terpyridine] with an electron-poor alkyne. In a cell proliferation assay using A549, HT-29, and MDA-MB-231 human cancer cell lines, the palladium compound was significantly more potent than the isostructural platinum analogue and exhibited submicromolar activity on the most responsive cell line. This difference was also reflected in the inhibitory efficiency toward TrxR with IC₅₀ values of 0.1 versus 5.4 μM for the Pd(II) and Pt(II) complexes, respectively. UV/Vis kinetic studies revealed that the Pt compound binds to selenocysteine faster than to cysteine [k = (22.9 ± 0.2)·10^-3 vs (7.1 ± 0.2)·10^-3 s^-1]. Selective triazolato ligand exchange of the title compounds with cysteine (Hcys) and selenocysteine (Hsec)─but not histidine (His) and 9-ethylguanine (9EtG)─was confirmed by ¹H, ⁷⁷Se, and ¹⁹⁵Pt NMR spectroscopy. Crystal structures of three of the four ligand exchange products were obtained, including [Pt(sec)(terpy)]PF₆ as the first metal complex of selenocysteine to be structurally characterized. Show less

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. Show less

AbstractTUCAN is a canonical serialization format that is independent of domain-specific concepts of structure and bonding. The atomic number is the only chemical feature that is used to derive the TUCAN format. Other than that, the format is solely based on the molecular topology. Validation is reported on a manually curated test set of molecules as well as a library of non-chemical graphs. The serialization procedure generates a canonical “tuple-style” output which is bidirectional, allowing the TUCAN string to serve as both identifier and descriptor. Use of the Python NetworkX graph library facilitated a compact and easily extensible implementation.Graphical Abstract Show less