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Quantitative estimates of reaction barriers and solvent effects are essential for developing kinetic mechanisms and predicting reaction outcomes. Here, we create a new data set of 5,600 unique elementary radical reactions calculated using the M06-2X/def2-QZVP//B3LYP-D3(BJ)/def2-TZVP level of theory. A conformer search is done for each species using TPSS/def2-TZVP. Gibbs free energies of activation and of reaction for these radical reactions in 40 common solvents are obtained using COSMO-RS for solvation effects. These balanced reactions involve the elements H, C, N, O, and S, contain up to 19 heavy atoms, and have atom-mapped SMILES. All transition states are verified by an intrinsic reaction coordinate calculation. We next train a deep graph network to directly estimate the Gibbs free energy of activation and of reaction in both gas and solution phases using only the atom-mapped SMILES of the reactant and product and the SMILES of the solvent. This simple input representation avoids computationally expensive optimizations for the reactant, transition state, and product structures during inference, making our model well-suited for high-throughput predictive chemistry and quickly providing information for (retro-)synthesis planning tools. To properly measure model performance, we report results on both interpolative and extrapolative data splits and also compare to several baseline models. During training and testing, the data set is augmented by including the reverse direction of each reaction and variants with different resonance structures. After data augmentation, we have around 2 million entries to train the model, which achieves a testing set mean absolute error of 1.16 kcal mol-1 for the Gibbs free energy of activation in solution. We anticipate this model will accelerate predictions for high-throughput screening to quickly identify relevant reactions in solution, and our data set will serve as a benchmark for future studies.

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The open-source package scikit-learn provides various machine learning algorithms and data processing tools, including the Pipeline class, which allows users to prepend custom data transformation steps to the machine learning model. We introduce the MolPipeline package, which extends this concept to cheminformatics by wrapping standard RDKit functionality, such as reading and writing SMILES strings or calculating molecular descriptors from a molecule object. We aimed to build an easy-to-use Python package to create completely automated end-to-end pipelines that scale to large data sets. Particular emphasis was put on handling erroneous instances, where resolution would require manual intervention in default pipelines. MolPipeline provides the building blocks to enable seamless integration of common cheminformatics tasks within scikit-learn's pipeline framework, such as scaffold splits and molecular standardization, making pipeline building easily adaptable to diverse project requirements.

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Predicting Gibbs free energy of solution is key to understanding the solvent effects on thermodynamics and reaction rates for kinetic modeling. Accurately computing solution free energies requires the enumeration and evaluation of relevant solute conformers in solution. However, even after generation of relevant conformers, determining their free energy of solution requires an expensive workflow consisting of several ab initio computational chemistry calculations. To help address this challenge, we generate a large data set of solution free energies for nearly 44,000 solutes with almost 9 million conformers calculated in 41 different solvents using density functional theory and COSMO-RS and quantify the impact of solute conformers on the solution free energy. We then train a message passing neural network to predict the relative solution free energies of a set of solute conformers, enabling the identification of a small subset of thermodynamically relevant conformers. The model offers substantial computational time savings with predictions usually substantially within 1 kcal/mol of the free energy of the solution calculated by using computational chemical methods.

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Assessing the outcomes of chemical reactions in a quantitative fashion has been a cornerstone across all synthetic disciplines. Classically approached through empirical optimization, data-driven modelling bears an enormous potential to streamline this process. However, such predictive models require significant quantities of high-quality data, the availability of which is limited: Main reasons for this include experimental errors and, importantly, human biases regarding experiment selection and result reporting. In a series of case studies, we investigate the impact of these biases for drawing general conclusions from chemical reaction data, revealing the utmost importance of "negative" examples. Eventually, case studies into data expansion approaches showcase directions to circumvent these limitations-and demonstrate perspectives towards a long-term data quality enhancement in chemistry.

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Machine learning (ML) has emerged as a general, problem-solving paradigm with many applications in computer vision, natural language processing, digital safety, or medicine. By recognizing complex patterns in data, ML bears the potential to modernise the way how many chemical challenges are approached. In this review, an introduction to ML is given from the perspective of synthetic chemistry: starting from the fundamentals regarding algorithms and best-practice workflows, the review covers different applications of machine learning in synthesis planning, property prediction, molecular design, and reactivity prediction. In particular, different approaches of representing and utilizing organic molecules will be discussed - providing synthetic chemists both with the understanding and the tools required to apply machine learning in the context of their research, and pointers for further studying.

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A general and simple strategy for the site-selective thiolation of various pharmaceutically relevant electron-rich heteroarenes with thiols is reported. This mild and reliable photocatalytic protocol enables C-S coupling at the most electron-rich position of the (multi)halogenated substrates, complementing established methodologies. Experimental and computational studies suggest a radical chain mechanism with the key step being a homolytic aromatic substitution of the heteroaryl halide by an electrophilic thiyl radical, highlighting an underdeveloped reactivity mode.

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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.

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A deaminative strategy for the borylation of aliphatic primary amines is described. Alkyl radicals derived from the single-electron reduction of redox-active pyridinium salts, which can be isolated or generated in situ, were borylated in a visible light-mediated reaction with bis(catecholato)diboron. No catalyst or further additives were required. The key electron donor-acceptor complex was characterized in detail by both experimental and computational investigations. The synthetic potential of this mild protocol was demonstrated through the late-stage functionalization of natural products and drug molecules.

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A visible-light-mediated approach to carbonyl-olefin cross-metathesis is described. Photoinduced hole catalysis was used to promote the formation of 1,3-diols from aldehydes and styrenes, which were then readily fragmented under acidic conditions to form the cross-metathesis products. The use of 1,3-diols as intermediates, rather than the energetically more demanding oxetanes, provides a new, orthogonal mechanistic strategy for carbonyl-olefin cross-metathesis. Furthermore, this approach does not require any metals, ligands, or additives, and provides the products with high levels of E selectivity. A mechanistic rationale is provided and supported by both theoretical calculations and experiments. Additionally, a practical synthesis of a new acridinium-based photocatalyst, including full characterization, is presented.

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The palladium-catalyzed coupling reaction of N-hydroxyphthalimide esters and styrenes to deliver exclusively (E)-substituted olefins under irradiation with visible light is reported. This method tolerates N-hydroxyphthalimide esters derived from primary, secondary, tertiary as well as benzylic carboxylic acids. Notably, Pd(PPh3 )4 is employed as an inexpensive palladium source and no addition of base or classical photocatalyst is required. Mechanistic studies suggest a light-mediated single-electron reduction of the activated acid by a photoexcited palladium(0) species to access alkyl radicals through decarboxylation.

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Herein, we present a novel strategy for the utilization of simple carbonyl compounds, aldehydes and ketones, as intermolecular radical acceptors. The reaction is enabled by visible light photoredox initiated hole catalysis and the in situ Brønsted acid activation of the carbonyl compound. This regioselective alkyl radical addition reaction does not require metals, ligands or additives and proceeds with a high degree of atom economy under mild conditions. The proposed mechanism is supported by both experimental and theoretical studies.

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Suzuki, Negishi, and Kumada couplings are some of the most important reactions for the formation of skeletal C-C linkages. Their widespread use to forge bonds between two aromatic rings has enabled every branch of chemical science. The analogous union between alkyl halides and metallated aryl systems has not been as widely employed due to the lack of commercially available halide building blocks. Redox-active esters have recently emerged as useful surrogates for alkyl halides in cross-coupling chemistry. Such esters are easily accessible through reactions between ubiquitous carboxylic acids and coupling agents widely used in amide bond formation. This article features an amalgamation of in-house experience bolstered by approximately 200 systematically designed experiments to accelerate the selection of ideal reaction conditions and activating agents for the cross-coupling of primary, secondary, and tertiary alkyl carboxylic acids with both aryl and heteroaryl organometallic species.

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