SELFIES and the future of molecular string representations.

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.

Readily accessible sp3-rich cyclic hydrazine frameworks exploiting nitrogen fluxionality.

Increased molecular complexity correlates with improved chances of success in the drug development process. Here, a strategy for the creation of sp3-rich, non-planar heterocyclic scaffolds suitable for drug discovery is described that obviates the need to generate multiple stereogenic centers with independent control. Asymmetric transfer hydrogenation using a tethered Ru-catalyst is used to efficiently produce a range of enantiopure cyclic hydrazine building blocks (up to 99% ee). Iterative C-N functionalization at the two nitrogen atoms of these compounds produces novel hydrazine and hydrazide based chemical libraries. Wide chemical diversification is possible through variation in the hydrazine structure, use of different functionalization chemistries and coupling partners, and controlled engagement of each nitrogen of the hydrazine in turn. Principal Moment of Inertia (PMI) analysis of this small hydrazine library reveals excellent shape diversity and three-dimensionality. NMR and crystallographic studies confirm these frameworks prefer to orient their substituents in three-dimensional space under the control of a single stereogenic center through exploitation of the fluxional behavior of the two nitrogen atoms.

Rise of the Robots.

High-throughput experimentation (HTE) is a growing, enabling technology that allows the execution of large, parallel sets of experiments. Often, automation is required to dose compounds on milligram to sub-milligram scale, to run many parallel reactions, and to analyse large datasets. Unique approaches to screen design, implementation, and analysis are required, distinct from traditional synthetic organic chemistry. The discipline also presents a profitable opportunity for individual scientists to learn about and explore fields adjacent to chemistry, including data science, robotics and equipment engineering, and computer programming. This perspective presents the author's viewpoints on the field of HTE, its implementation within a chemistry career, and the automated future of organic chemistry technology.

Reinvestigation of a Catalytic, Enantioselective Alkene Dibromination and Chlorohydroxylation.

Attempts to reproduce eight, putative, enantioselective dibromination and chlorohydroxylation reactions from oft-cited literature studies are described. The reactions were performed with full fidelity to the original report wherever possible. Analysis of the enantiomeric composition was performed by chiral stationary phase HPLC or SFC (CSP-HPLC or CSP-SFC), as opposed to the original report, which used chiral shift reagent NMR spectroscopy. After careful study, the reported levels of enantioselectivity were found to be incorrect. Possible explanations for the false positive results are discussed.