Predicting success in Cu-catalyzed C-N coupling reactions using data science.

Data science is assuming a pivotal role in guiding reaction optimization and streamlining experimental workloads in the evolving landscape of synthetic chemistry. A discipline-wide goal is the development of workflows that integrate computational chemistry and data science tools with high-throughput experimentation as it provides experimentalists the ability to maximize success in expensive synthetic campaigns. Here, we report an end-to-end data-driven process to effectively predict how structural features of coupling partners and ligands affect Cu-catalyzed C-N coupling reactions. The established workflow underscores the limitations posed by substrates and ligands while also providing a systematic ligand prediction tool that uses probability to assess when a ligand will be successful. This platform is strategically designed to confront the intrinsic unpredictability frequently encountered in synthetic reaction deployment.

Data Science Enables the Development of a New Class of Chiral Phosphoric Acid Catalysts.

The widespread success of BINOL-chiral phosphoric acids (CPAs) has led to the development of several high molecular weight, sterically encumbered variants. Herein, we disclose an alternative, minimalistic chiral phosphoric acid backbone incorporating only a single instance of point chirality. Data science techniques were used to select a diverse training set of catalysts, which were benchmarked against the transfer hydrogenation of an 8-aminoquinoline. Using a univariate classification algorithm and multivariate linear regression, key catalyst features necessary for high levels of selectivity were deconvoluted, revealing a simple catalyst model capable of predicting selectivity for out-of-set catalysts. This workflow enabled extrapolation to a catalyst providing higher selectivity than both reported peptide-type and BINOL-type catalysts (up to 95:5 er). These techniques were then successfully applied towards two additional transforms. Taken together, these examples illustrate the power of combining rational design with data science (ab initio) to efficiently explore reactivity during catalyst development.

Data Science Meets Physical Organic Chemistry.

ConspectusAt the heart of synthetic chemistry is the holy grail of predictable catalyst design. In particular, researchers involved in reaction development in asymmetric catalysis have pursued a variety of strategies toward this goal. This is driven by both the pragmatic need to achieve high selectivities and the inability to readily identify why a certain catalyst is effective for a given reaction. While empiricism and intuition have dominated the field of asymmetric catalysis since its inception, enantioselectivity offers a mechanistically rich platform to interrogate catalyst-structure response patterns that explain the performance of a particular catalyst or substrate.In the early stages of an asymmetric reaction development campaign, the overarching mechanism of the reaction, catalyst speciation, the turnover limiting step, and many other details are unknown or posited based on related reactions. Considering the unclear details leading to a successful reaction, initial enantioselectivity data are often used to intuitively guide the ultimate direction of optimization. However, if the conditions of the Curtin-Hammett principle are satisfied, then measured enantioselectivity can be directly connected to the ensemble of diastereomeric transition states (TSs) that lead to the enantiomeric products, and the associated free energy difference between competing TSs (ΔΔG⧧ = -RT ln[(S)/(R)], where (S) and (R) represent the concentrations of the enantiomeric products). We, and others, speculated that this important piece of information can be leveraged to guide reaction optimization in a quantitative way.Although traditional linear free energy relationships (LFERs), such as Hammett plots, have been used to illuminate important mechanistic features, we sought to develop data science derived tools to expand the power of LFERs in order to describe complex reactions frequently encountered in modern asymmetric catalysis. Specifically, we investigated whether enantioselectivity data from a reaction can be quantitatively connected to the attributes of reaction components, such as catalyst and substrate structural features, to harness data for asymmetric catalyst design.In this context, we developed a workflow to relate computationally derived features of reaction components to enantioselectivity using data science tools. The mathematical representation of molecules can incorporate many aspects of a transformation, such as molecular features from substrate, product, catalyst, and proposed transition states. Statistical models relating these features to reaction outputs can be used for various tasks, such as performance prediction of untested molecules. Perhaps most importantly, statistical models can guide the generation of mechanistic hypotheses that are embedded within complex patterns of reaction responses. Overall, merging traditional physical organic experiments with statistical modeling techniques creates a feedback loop that enables both evaluation of multiple mechanistic hypotheses and future catalyst design. In this Account, we highlight the evolution and application of this approach in the context of a collaborative program based on chiral phosphoric acid catalysts (CPAs) in asymmetric catalysis.

Conformational Dynamics in Asymmetric Catalysis: Is Catalyst Flexibility a Design Element?

Traditionally, highly selective low molecular weight catalysts have been designed to contain rigidifying structural elements. As a result, many proposed stereochemical models rely on steric repulsion for explaining the observed selectivity. Recently, as is the case for enzymatic systems, it has become apparent that some flexibility can be beneficial for imparting selectivity. Dynamic catalysts can reorganize to maximize attractive non-covalent interactions that stabilize the favored diastereomeric transition state, while minimizing repulsive non-covalent interactions for enhanced selectivity. This Short Review discusses catalyst conformational dynamics and how these effects have proven beneficial for a variety of catalyst classes, including tropos ligands, cinchona alkaloids, hydrogen-bond donating catalysts, and peptides.

Adding low concentrations of clonidine to ropivacaine for transversus abdominis plane blocks does not reduce plasma ropivacaine levels, suggesting a lack of vasoconstrictor effect.

Clonidine has been used successfully to prolong the duration of action of local anaesthetics in peripheral nerve blocks, but its mechanism of action in this setting remains unclear. Some studies suggest that clonidine exerts a vasoconstrictor effect, limiting the washout of local anaesthetic from its site of deposition. We investigated this potential vasoconstrictor effect, using plasma ropivacaine concentrations as a surrogate measure of vasoconstriction, in patients who received transversus abdominis plane (TAP) blocks with and without clonidine. Eighty women undergoing laparoscopic gynaecological surgery were randomly assigned to receive one of four TAP block solutions: 0.2% ropivacaine (control), ropivacaine with clonidine 2 µg/kg (clonidine), ropivacaine with 1:400,000 adrenaline (adrenaline) or ropivacaine and a subcutaneous injection of clonidine 2 µg/kg (SC clonidine). The primary outcome was total venous plasma ropivacaine concentrations up to 6 h after the block. There were no significant differences in plasma ropivacaine concentrations between the control group and the clonidine group at any timepoint in the study, nor were there differences in either the mean maximum ropivacaine concentration ( Cmax) (1.99 µg/mL versus 2.05 µg/mL, P = 0.712) or the time to maximum concentration ( Tmax) (51.0 min versus 56.0 min, P = 0.537). The SC clonidine group also did not differ significantly from the controls ( Cmax 2.13 µg/mL versus 1.99 µg/mL, P = 0.424; Tmax 43.5 min versus 51.0 min, P = 0.201). Plasma ropivacaine concentrations in the adrenaline group were significantly lower than the controls from 10 to 90 min ( P < 0.003 for each comparison), and the Cmax was less than that of the control group (1.36 µg/mL versus 1.99 µg/mL, P < 0.001) with a longer Tmax (103.5 min versus 51.0 min, P = 0.001). These findings indicate that clonidine at a concentration of 1.35 µg/mL added to ropivacaine for TAP blocks did not produce a reduction in plasma ropivacaine concentrations. This suggests a lack of vasoconstrictor effect during TAP blocks. Further studies should evaluate whether vasoconstriction occurs when clonidine is used at higher concentrations or for other blocks.

Disparate Catalytic Scaffolds for Atroposelective Cyclodehydration.

Catalysts that control stereochemistry are prized tools in chemical synthesis. When an effective catalyst is found, it is often explored for other types of reactions, frequently under the auspices of different mechanisms. As successes mount, a unique catalyst scaffold may become viewed as "privileged". However, the mechanistic hallmarks of privileged catalysts are not easily enumerated or readily generalized to genuinely different classes of reactions or substrates. We explored the concept of scaffold uniqueness with two catalyst types for an unusual atropisomer-selective cyclodehydration: (a) C2-symmetric chiral phosphoric acids and (b) phosphothreonine-embedded, peptidic phosphoric acids. Pragmatically, both catalyst scaffolds proved fertile for enantioselective/atroposelective cyclodehydrations. Mechanistic studies revealed that the determinants of often equivalent and high atroposelectivity are different for the two catalyst classes. A data-descriptive classification of these asymmetric catalysts reveals an increasingly broad set of catalyst chemotypes, operating with different mechanistic features, that creates new opportunities for broad and complementary application of catalyst scaffolds in diverse substrate space.

Parameterization and Analysis of Peptide-Based Catalysts for the Atroposelective Bromination of 3-Arylquinazolin-4(3H)-ones.

We report the development of a method to parameterize and predict the performance of structurally flexible ß-turn-containing peptide catalysts, using the atroposelective bromination of 3-arylquinazolin-4(3H)-ones as a case study. The multivariate correlations obtained for tetrapeptides of two ß-turn types, type I' pre-helical and type II' ß-hairpin, indicate that although one conformer may be associated with a more dominant contribution to the observed enantioselectivity, it is possible that multiple conformers contribute to a complex transition state ensemble.

Enantioselective Dehydrogenative Heck Arylations of Trisubstituted Alkenes with Indoles to Construct Quaternary Stereocenters.

An enantioselective, intermolecular dehydrogenative Heck arylation of trisubstituted alkenes to construct remote quaternary stereocenters has been developed. Using a new chiral pyridine oxazoline ligand, good to high enantioselectivity is achieved for various combinations of indole derivatives and trisubstituted alkenes. However, some combinations of substrates led to lower enantioselectivity, which provided the impetus to use structure enantioselectivity correlations to design a better performing ligand.

Nickel-Catalyzed Decarboxylative Arylation of Azoles with Perfluoro- and Nitrobenzoates.

This manuscript describes a Ni-catalyzed method for the direct arylation of azoles using benzoates. Perfluorophenyl and 2-nitrobenzoates participate in these reactions to afford the corresponding products in modest to good yields. The efficiency of the arylations with perfluorobenzoates is highly dependent on both the degree and position of fluorine atoms in the benzoates.

Results from a pediatric surgical centre justify early intervention in disorders of sex development.

BACKGROUND: Controversy persists surrounding early management of disorders of sex development. We assessed genital appearance, gender identity, and quality of life in prepubertal children who have had early surgical intervention. METHODS: Children treated for disorders of sex development who were 5 to 10 years of age were eligible (n = 54). Children were scored (modified Creighton scale) for anatomical and cosmetic outcome, and both patients and parents completed PedsQL quality-of-life and gender identity questionnaires, with ethics approval. RESULTS: Of 54 patients, 41 presented for review. Treatment began at 13.2 (1.8-250.1) months (median; range) and were reviewed at 7.5 +/- 2.1 (mean +/- SD) years of age. Nineteen were raised as girls and 22 as boys. Girls had good (85%) or satisfactory (15%) anatomical/cosmetic outcome, whereas 52% boys had good, 38% satisfactory, and 10% poor cosmetic outcomes. On gender identity questionnaire, boys scored 3.9 +/- 0.4 (mean +/- SD) and girls 3.6 +/- 0.5; 1 of 19 boys and 3 of 19 girls had lower scores, suggesting risk of gender identity disorder. Quality-of-life scores were 80+ for physical and 65 to 80 for psychosocial scores. CONCLUSIONS: Early intervention is generally associated with positive outcomes for patients and parents. Girls had better anatomical outcomes than boys, and gender dysphoria risks were low in both sexes.