TUCAN: A molecular identifier and descriptor applicable to the whole periodic table from hydrogen to oganesson

Making the InChI FAIR and sustainable while moving to inorganics

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.

Python tools for structural tasks in chemistry

In recent decades, the use of computational approaches and artificial intelligence in the scientific environment has become more widespread. In this regard, the popular and versatile programming language Python has attracted considerable attention from scientists in the field of chemistry. It is used to solve a variety of chemical and structural problems, including calculating descriptors, molecular fingerprints, graph construction, and computing chemical reaction networks. Python offers high-quality visualization tools for analyzing chemical spaces and compound libraries. This review is a list of tools for the above tasks, including scripts, libraries, ready-made programs, and web interfaces. Inevitably this manuscript does not claim to be an all-encompassing handbook including all the existing Python-based structural chemistry codes. The review serves as a starting point for scientists wishing to apply automatization or optimization to routine chemistry problems.

Deciphering antifungal and antibiofilm mechanisms of isobavachalcone against Cryptococcus neoformans through RNA-seq and functional analyses

Cryptococcus neoformans has been designated as critical fungal pathogens by the World Health Organization, mainly due to limited treatment options and the prevalence of antifungal resistance. Consequently, the utilization of novel antifungal agents is crucial for the effective treatment of C. neoformans infections. This study exposed that the minimum inhibitory concentration (MIC) of isobavachalcone (IBC) against C. neoformans H99 was 8 µg/mL, and IBC dispersed 48-h mature biofilms by affecting cell viability at 16 µg/mL. The antifungal efficacy of IBC was further validated through microscopic observations using specific dyes and in vitro assays, which confirmed the disruption of cell wall/membrane integrity. RNA-Seq analysis was employed to decipher the effect of IBC on the C. neoformans H99 transcriptomic profiles. Real-time quantitative reverse transcription PCR (RT-qPCR) analysis was performed to validate the transcriptomic data and identify the differentially expressed genes. The results showed that IBC exhibited various mechanisms to impede the growth, biofilm formation, and virulence of C. neoformans H99 by modulating multiple dysregulated pathways related to cell wall/membrane, drug resistance, apoptosis, and mitochondrial homeostasis. The transcriptomic findings were corroborated by the antioxidant analyses, antifungal drug sensitivity, molecular docking, capsule, and melanin assays. In vivo antifungal activity analysis demonstrated that IBC extended the lifespan of C. neoformans-infected Caenorhabditis elegans. Overall, the current study unveiled that IBC targeted multiple pathways simultaneously to inhibit growth significantly, biofilm formation, and virulence, as well as to disperse mature biofilms of C. neoformans H99 and induce cell death.

Using Machine Learning to Predict the Antibacterial Activity of Ruthenium Complexes

Rising antimicrobial resistance (AMR) and lack of innovation in the antibiotic pipeline necessitate novel approaches to discovering new drugs. Metal complexes have proven to be promising antimicrobial compounds, but the number of studied compounds is still low compared to the millions of organic molecules investigated so far. Lately, machine learning (ML) has emerged as a valuable tool for guiding the design of small organic molecules, potentially even in low-data scenarios. For the first time, we extend the application of ML to the discovery of metal-based medicines. Utilising 288 modularly synthesized ruthenium arene Schiff-base complexes and their antibacterial properties, a series of ML models were trained. The models perform well and are used to predict the activity of 54 new compounds. These displayed a 5.7x higher hit-rate (53.7 %) against methicillin-resistant Staphylococcus aureus (MRSA) compared to the original library (9.4 %), demonstrating that ML can be applied to improve the success-rates in the search of new metalloantibiotics. This work paves the way for more ambitious applications of ML in the field of metal-based drug discovery.

Metals to combat antimicrobial resistance

Bacteria, similar to most organisms, have a love-hate relationship with metals: a specific metal may be essential for survival yet toxic in certain forms and concentrations. Metal ions have a long history of antimicrobial activity and have received increasing attention in recent years owing to the rise of antimicrobial resistance. The search for antibacterial agents now encompasses metal ions, nanoparticles and metal complexes with antimicrobial activity ('metalloantibiotics'). Although yet to be advanced to the clinic, metalloantibiotics are a vast and underexplored group of compounds that could lead to a much-needed new class of antibiotics. This Review summarizes recent developments in this growing field, focusing on advances in the development of metalloantibiotics, in particular, those for which the mechanism of action has been investigated. We also provide an overview of alternative uses of metal complexes to combat bacterial infections, including antimicrobial photodynamic therapy and radionuclide diagnosis of bacterial infections.

The current landscape of author guidelines in chemistry through the lens of research data sharing

As the primary method of communicating research results, journals garner an enormous impact on community behavior. Publishing the underlying research data alongside journal articles is widely considered good scientific practice. Ideally, journals and their publishers place these recommendations or requirements in their author guidelines and data policies. Several efforts are working to improve the infrastructure, processes, and uptake of research data sharing, including the NFDI4Chem consortium, working groups within the RDA, and IUPAC, including the WorldFAIR Chemistry project. In this article, we present the results of a large-scale analysis of author guidelines from several publishers and journals active in chemistry research, showing how well the publishing landscape supports different criteria and where there is room for improvement. While the requirement for deposition of X-ray diffraction data is commonplace, guidelines rarely mention machine-readable chemical structures and metadata/minimum information standards. Further evaluation criteria included recommendations on persistent identifiers, data availability statements, data deposition into repositories as well as of open analytical data formats. Our survey shows that publishers and journals are starting to include aspects of research data in their guidelines. We as authors should accept and embrace the guidelines with increasing requirements for data availability, data interoperability, and re-usability to improve chemistry research.

Graph neural networks for materials science and chemistry

Machine learning plays an increasingly important role in many areas of chemistry and materials science, being used to predict materials properties, accelerate simulations, design new structures, and predict synthesis routes of new materials. Graph neural networks (GNNs) are one of the fastest growing classes of machine learning models. They are of particular relevance for chemistry and materials science, as they directly work on a graph or structural representation of molecules and materials and therefore have full access to all relevant information required to characterize materials. In this Review, we provide an overview of the basic principles of GNNs, widely used datasets, and state-of-the-art architectures, followed by a discussion of a wide range of recent applications of GNNs in chemistry and materials science, and concluding with a road-map for the further development and application of GNNs.