Visible-Light-Mediated Charge Transfer Enables C−C Bond Formation with Traceless Acceptor Groups

The development and application of traceless acceptor groups in photochemical C−C bond formation is described. This strategy was enabled by the photoexcitation of electron donor–acceptor (EDA) complexes with visible light. The traceless acceptors, which were readily prepared from amino acid and peptide feedstocks, could be used to alkylate a wide range of heteroarene and enamine donors under metal- and peroxide-free conditions. The crucial role of the EDA complexes in the mechanism of these reactions was explored through combined experimental and computational studies

Photosensitized intermolecular carboimination of alkenes through the persistent radical effect

An intermolecular, two‐component vicinal carboimination of alkenes has been accomplished by energy transfer catalysis. Oxime esters of alkyl carboxylic acids were used as bifunctional reagents to generate both alkyl and iminyl radicals. Subsequently, addition of the alkyl radical to an alkene generates a transient radical for selective radical–radical cross‐coupling with the persistent iminyl radical. Furthermore, this process provides direct access to aliphatic primary amines and α‐amino acids by simple hydrolysis

Energy transfer catalysis mediated by visible light : principles, applications, directions

Harnessing visible light to access excited (triplet) states of organic compounds can enable impressive reactivity modes. This tutorial review covers the photophysical fundamentals and most significant advances in the field of visible-light-mediated energy transfer catalysis within the last decade. Methods to determine excited triplet state energies and to characterize the underlying Dexter energy transfer are discussed. Synthetic applications of this field, divided into four main categories (cyclization reactions, double bond isomerizations, bond dissociations and sensitization of metal complexes), are also examined

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: SELFIES (SELF-referencIng Embedded Strings). SELFIES has since simplified and enabled numerous new applications in chemistry. In this manuscript, 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.Comment: 34 pages, 15 figures, comments and suggestions for additional references are welcome

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: SELFIES (SELF-referencIng Embedded Strings). SELFIES has since simplified and enabled numerous new applications in chemistry. In this manuscript, 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

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

Visible-Light Photosensitized Aryl and Alkyl Decarboxylative Carbon-Heteroatom and Carbon-Carbon Bond Formations

A general strategy to access both aryl and alkyl radicals by photosensitized decarboxylation of the corresponding carboxylic acids esters has been developed. An energy transfer mediated homolysis of unsymmetrical sigma-bonds for a concerted fragmentation/decarboxylation process is involved. As a result, an independent aryl/alkyl radical generation step enables a series of key C-X and C-C bond forming reactions by simply changing the radical trapping agent.</sub

Visible-light-photosensitized aryl and alkyl decarboxylative functionalization reactions

Despite significant progress in aliphatic decarboxylation, an efficient and general protocol for radical aromatic decarboxylation has lagged far behind. Herein, we describe a general strategy for rapid access to both aryl and alkyl radicals by photosensitized decarboxylation of the corresponding carboxylic acids esters followed by their successive use in divergent carbon–heteroatom and carbon–carbon bond‐forming reactions. Identification of a suitable activator for carboxylic acids is the key to bypass a competing single‐electron‐transfer mechanism and “switch on” an energy‐transfer‐mediated homolysis of unsymmetrical σ‐bonds for a concerted fragmentation/decarboxylation process

A Structure-Based Platform for Predicting Chemical Reactivity

Despite their enormous potential, machine learning methods have only found limited application in predicting reaction outcomes, as current models are often highly complex and, most importantly, are not transferrable to different problem sets. Herein, we present the direct utilization of Lewis structures in a machine learning platform for diverse applications in organic chemistry. Therefore, an input based on multiple fingerprint features (MFF) as a universal molecular representation was developed and used for problem sets of increasing complexity: First, molecular properties across a diverse array of molecules could be predicted accurately. Next, reaction outcomes such as stereoselectivities and yields were predicted for experimental data sets that were previously evaluated using (complex) problem-oriented descriptor models. As a final application, a systematic high-throughput data set showed good correlation when using the MFF model, which suggests that this approach is general and ready for immediate adoption by chemists