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.

Building Machine Learning Small Molecule Melting Points and Solubility Models Using CCDC Melting Points Dataset.

Predicting solubility of small molecules is a very difficult undertaking due to the lack of reliable and consistent experimental solubility data. It is well known that for a molecule in a crystal lattice to be dissolved, it must, first, dissociate from the lattice and then, second, be solvated. The melting point of a compound is proportional to the lattice energy, and the octanol-water partition coefficient (log P) is a measure of the compound's solvation efficiency. The CCDC's melting point dataset of almost one hundred thousand compounds was utilized to create widely applicable machine learning models of small molecule melting points. Using the general solubility equation, the aqueous thermodynamic solubilities of the same compounds can be predicted. The global model could be easily localized by adding additional melting point measurements for a chemical series of interest.

CSD Communications of the Cambridge Structural Database.

The Cambridge Structural Database (CSD) is a collection of over one million experimental three-dimensional structures obtained through crystallographic analyses. These structures are determined by crystallographers worldwide and undergo curation and enhancement by scientists at the Cambridge Crystallographic Data Centre (CCDC) prior to their addition to the database. Though the CSD is substantial and contains widespread chemical diversity across organic and metal-organic compounds, it is estimated that a significant proportion of crystal structures determined are not published or shared through the peer-reviewed journal mechanism. To help overcome this, scientists can publish structures directly through the database as CSD Communications and these structural datasets are made publicly available alongside structures associated with scientific articles. CSD Communications contribute to the collective crystallographic knowledge as nearly two thirds are novel structures that are not otherwise available in the scientific literature. The primary benefits of sharing data through CSD Communications include the long-term preservation of scientific data, the strengthening of a widely data-mined world repository (the CSD), and the opportunity for scientists to receive recognition for their work through a formal and citable data publication. All CSD Communications are assigned unique digital object identifiers (DOIs). Contributions as CSD Communications currently comprise about 3.89% of the total CSD entries. Each individual CSD Communication is free to view and retrieve from the CCDC website.

Photomagnetic studies on spin-crossover solid solutions containing two different metal complexes, [Fe(1-bpp)(2)](x)[M(terpy)2](1-x)[BF4]2 (M = Ru or Co).

The photomagnetic properties of two series of spin-crossover solid solutions, [Fe(1-bpp)(2)](x)[Ru(terpy)(2)](1-x)(BF(4))(2) and [Fe(1-bpp)(2)](x)[Co(terpy)(2)](1-x)(BF(4))(2) (1-bpp = 2,6-bis[pyrazol-1-yl]pyridine), have been investigated. For all the materials, the evolution of the T(LIESST) value, the high-spin → low-spin relaxation parameters and the LITH loops were thoroughly studied. Interestingly in the Fe:Co series, along the photo-excitation, cobalt ions are concomitantly converted from low-spin to high-spin states with the iron centres, and also fully relax after light excitation.