Instituto de Síntesis Química y Catálisis Homogénea

The dehydrogenative coupling of hydrosilanes with alcohols or water is a fundamental transformation in organosilicon chemistry, enabling the synthesis of alkoxysilanes, silanols, and siloxanes—key materials for coatings and adhesives. Traditionally, these reactions have relied on catalysts based on platinum group metals, which are costly and scarce. The development of Earth‐abundant metal (EAM) catalysts offers a sustainable and cost‐effective alternative. This concept article highlights recent advancements in homogeneous EAM catalysis for these transformations, focusing on mechanistic insights, reactivity trends, and catalytic efficiencies. Catalysts based on manganese, iron, cobalt, copper, or zinc are reshaping the field by offering efficient and selective processes for the synthesis of alkoxysilanes, silanols, and siloxanes. Moreover, a key feature of these catalytic systems is their ability to generate dihydrogen as a byproduct, offering potential applications in hydrogen storage and utilization. Despite the good performances of EAM catalysts in the hydrolysis or alcoholysis of silanes, challenges remain, including improving turnover numbers, substrate scope, and catalyst stability. Future developments integrating computational and experimental approaches will be key to optimizing these systems.

Mitochondria-targeting drugs (mitocans) based on organic cations are emerging as powerful and selective cancer therapeutics. In this study, we have evaluated a novel series of oleanolic acid-derived mitocans, revealing nanomolar-range antiproliferative effects against human solid tumor cells. Continuous live-cell imaging revealed extensive cytoplasmic vacuolation, while mechanistic studies identified paraptosis as the dominant form of cell death. Remarkably, in vivo experiments demonstrated significant tumor growth inhibition in mice, with no detectable toxicity at therapeutic doses. These findings highlight the potential of oleanolic acid-derived mitocans as promising candidates for cancer therapy.

We investigated the functional plasticity of two P450 enzymes, AbnK and AbnG, in the brassicicene biosynthetic pathway. Through in vivo heterologous expression, feeding assays, and in vitro reactions, we show that these enzymes regio- and stereoselectively transform both 5-8-5 and 5-9-5 terpenoid scaffolds in different ways, supported by computational simulations. AbnK also bridges a missing step in brassicicene A biosynthesis. Our findings demonstrate how controlled promiscuity underpins structural diversity, informing strategies to expand chemoenzymatic synthesis.

Copper hexacyanoferrate (Cu2Fe(CN)6) nanocubes with a homogeneous size under 100nm are synthesized by self-assembly from Cu2+ and Fe(CN)6 3- precursors. Similar to previous reports with catalysts containing Cu and Fe, the objective is to produce a nanoparticle catalyst that can promote glutathione (GSH) oxidation thanks to the Cu contribution, plus some ROS production through Fenton-like processes fostered by Fe. Unexpectedly, the catalytic activity for GSH oxidation are much higher (≈50%) than those obtained with equal Cu amounts provided as CuCl2. Furthermore, in the presence of GSH concentrations characteristic of the tumor microenvironment, the nanocubes disassembled homogeneously, without a noticeably change of composition. These results suggest that this strong increase of catalytic activity arises from synergistic coordination of the released Cu2+ and Fe(CN)6 3- ions that facilitate GSH deprotonation, accelerating its oxidation. Given the role of GSH in the nanoparticle disassembly process, a selective action of the catalyst can be obtained: lethal doses as low as 18ppm of Cu are obtained for U251-MG cancer cells while healthy fibroblasts are largely spared.

Data-driven methodologies are transforming chemical research by providing chemists with digital tools that accelerate discovery and promote sustainability. In this context, non-linear machine learning algorithms are among the most disruptive technologies in the field and have proven effective for handling large datasets. However, in data-limited scenarios, linear regression has traditionally prevailed due to its simplicity and robustness, while non-linear models have been met with skepticism over concerns related to interpretability and overfitting. In this study, we introduce ready-to-use, automated workflows designed to overcome these challenges. These frameworks mitigate overfitting through Bayesian hyperparameter optimization by incorporating an objective function that accounts for overfitting in both interpolation and extrapolation. Benchmarking on eight diverse chemical datasets, ranging from 18 to 44 data points, demonstrates that when properly tuned and regularized, non-linear models can perform on par with or outperform linear regression. Furthermore, interpretability assessments and de novo predictions reveal that non-linear models capture underlying chemical relationships similarly to their linear counterparts. Ultimately, the automated non-linear workflows presented have the potential to become valuable tools in a chemist's toolbox for studying problems in low-data regimes alongside traditional linear models.

-Miscellaneous information, including NMR data of complexes and organic products, and DFT calculation data (PDF). -Optimized coordinates for the computed compounds (XYZ).

A bare lead atom is a σ-donor ligand capable of linearly bonding and stabilizing two units of a classical polyhydride complex, with a high-valent metal center. As a proof of concept, we have prepared and characterized the μ2-tetrylide complex (PiPr3)2H4Os═Pb═OsH4(PiPr3)2 in the reaction of OsH6(PiPr3)2 with Pb{N(SiMe3)2}2. Although the Pb-Os bonds exhibit electrostatic interaction, the main orbital interactions result from two dative σ bonds from the lead atom to the osmium centers. The latter also provide much weaker π-backdonations.

Organometallic Chemistry. In their Communication (e202403108), Babil Menjón et al. demonstrate the CF3 group as a synthon of the CF+ unit in palladium chemistry.