The Suzuki–Miyaura cross-coupling reaction is plagued by protodeboronation, an undesirable side reaction with water that consumes the boronic acid derivatives required for the cross-coupling reaction....

The consideration of transition state (TS) conformer ensembles is required to accurately model a reaction, and thus plays a key role in computational catalyst design. While CREST and GOAT are establis...

We present g-xTB, a next-generation semi-empirical electronic structure method derived from tight-binding (TB) approximations to Kohn–Sham density functional theory (KS-DFT). Designed to bridge the gap between semi-empirical quantum mechanical (SQM) approaches and DFT in terms of accuracy, robustness, and general applicability, g-xTB targets the performance of the ωB97M-V range-separated hybrid density functional with large basis sets while maintaining TB speed. Key innovations include an atom-in-molecule adaptive atomic orbital basis, a refined Hamiltonian incorporating range-separated approximate Fock exchange, up to fourth-order charge-fluctuation terms with a novel first-order electronic contribution, and atomic correction potentials (ACPs), as well as a charge-dependent semi-classical repulsion function. Parameterized on extended and extremely diverse molecular training sets – including “mindless molecules” – g-xTB achieves excellent accuracy across a broad chemical space, including the actinide elements. Benchmarking against around 32k relative energies across thermochemistry, conformational energetics, non-covalent interactions, and reaction barriers shows that g-xTB consistently outperforms GFN2-xTB, often reducing mean absolute errors by half. Notably, it achieves a WTMAD-2 of 9.3 kcal mol−1 on the full GMTKN55 benchmark, comparable to low-cost DFT methods. It also shows substantial improvements for transition-metal complexes, relative spin state energies, and orbital energy gaps – areas where many SQM and even DFT methods often struggle. In summary, g-xTB offers DFT-like accuracy with minimal computational overhead compared to its predecessor, GFN2-xTB, making it a robust, minimally empirical, transferable, and efficient alternative to machine learning interatomic potentials for a wide range of molecular simulations. It is proposed as a general replacement for the GFNn-xTB family and, in many practical cases, a viable substitute for low- and mid-level DFT methods.

Transition state (TS) geometries of chemical reactions are key to understanding reaction mechanisms and estimating kinetic properties. Inferring these directly from 2D reaction graphs offers chemists a powerful tool for rapid and accessible reaction analysis. Quantum chemical methods for computing TSs are computationally intensive and often infeasible for larger molecular systems. Recently, deep learning–based diffusion models have shown promise in generating TSs from 2D reaction graphs for single-step reactions. However, framing TS generation as a diffusion process, by design, requires a prohibitively large number of sampling steps during inference. Here we show that modeling TS generation as an optimal transport flow problem, solved via E(3)-equivariant flow matching with geometric tensor networks, achieves over a hundredfold speedup in inference while improving geometric accuracy compared to the state-of-the-art. This breakthrough increase in sampling efficiency and predictive accuracy enables the practical use of deep learning-based TS generators in high-throughput settings for larger and more complex chemical systems. Our method, GoFlow, thus represents a significant methodological advancement in machine learning-based TS generation, bringing it closer to widespread use in computational chemistry workflows.

The Sharpless asymmetric dihydroxylation remains a key transformation in chemical synthesis, yet its success hides unexpected cases of lower selectivity. A chemoinformatic workflow was developed to allow data-driven analysis of the reaction. A database of 1007 reactions employing AD-mix α and β was curated from the literature, and an alignment-dependent, fragment-based featurization of alkenes was implemented for modeling. This platform converged on machine learning models capable of predicting the magnitude of enantioselectivity for multiple alkene classes, achieving Q2F3 values ≥ 0.8, test r2 values ≥ 0.7 and mean absolute errors (MAE) ≤ 0.3 kcal/mol. The features of alkenes contributing to model performance were assessed with SHapley Additive exPlanations (SHAP) analysis to gather insight into factors underlying predictions. Experimental validation demonstrated that the models could achieve meaningful predictions on numerous out-of-sample alkenes.

The successful integration of large language models (LLMs) into laboratory workflows has demonstrated robust capabilities in natural language processing, autonomous task execution, and collaborative p...

The regio- and site-selectivity of organic reactions is one of the most important aspects when it comes to synthesis planning. Due to that, massive research efforts were invested into computational mo...

Bioisosteric replacement is vital in drug discovery; however, substituting core ring structures is challenging. Now, a strategy that converts pyridines into benzonitriles, via N-oxidation, photochemic...

Nature Catalysis, Published online: 07 March 2025; doi:10.1038/s41929-025-01306-9Controlling the selectivity of electrocatalysed C–H activation with earth-abundant base metals can increase its synthetic impact. Now chemodivergence of an electrocatalysed…

General parameters are highly desirable in the natural sciences - e.g., chemical reaction conditions that enable high yields across a range of related transformations. This has a significant practical...

Medicinal chemistry has always been closer to the arts than other disciplines in the natural sciences. Instead of searching for natural laws, medicinal chemistry creates new molecular entities entaili...