Knotted molecules occur naturally and can be designed to obtain unique biological and material properties. Here, the authors report a framework for generating polymer conformations that incorporates a knot-type classifier — which guides the generative process toward the specified topologies — and integrate it within an existing protein backbone diffusion model, providing a potential route for designing knotted polymers and proteins.

Functionalized oxetanes and azetidines hold potential as bioisosteres, but their use in agrochemical and pharmaceutical screenings is hindered by synthetic challenges. Here, the authors employ XtalFluor-E® for a mild one-step deoxyamination, achieving 30–97% yields in one hour or less, and provide mechanistic insights through multivariate linear regression and density functional theory, enhancing their synthetic accessibility.

Cholesterol is a key modulator of membrane protein structure and function, yet predicting cholesterol-binding sites remains challenging due to the relatively limited data on cholesterol-binding pockets compared to drug-like ligand binding sites. Here, the authors report CholBindNet, an interpretable, atom-based graph neural network that relies on a positive-unlabeled (PU) training strategy, demonstrating its superior performance over existing models and assessing strong, moderate, and weak cholesterol-binding sites in the PIEZO2 ion channel against all-atom molecular dynamics simulations.

Amino acid and polyamine metabolism are crucial for cellular functions, yet its pathway usage varies with context and compensatory mechanisms. Here, the authors employ short-term, low-dose heavy water labeling to trace these metabolic processes, revealing tissue-specific biosynthetic capacities.

Both aromatic and positively charged residues play a role in stabilizing biomolecular condensates formed by intrinsically disordered regions of proteins, but they differ in their interaction types and relative strengths, which are challenging to quantify. Here, the authors use molecular dynamics simulations and alchemical transformations together with quantum chemical calculations to resolve the differences in arginine and lysine cation-π interaction strengths in different media, showing that these residues maintain a hierarchy of interaction strengths across molecular environments.

Advancements in deep learning have revolutionized drug and material design, yet the scarcity of large-scale datasets with detailed physicochemical data limits molecular representation learning. Here, the authors introduce qcMol, a dataset of 1.2 million molecules with DFT-level quantum chemical annotations and flexible data formats, serving as the pre-training resource as well as the benchmark test set for machine learning models.

Chemistry programmes worldwide are experiencing declining student enrolment, reflecting shifting career perceptions and competition from emerging fields. This Comment outlines how the discipline is evolving into an integrative field that combines molecular science with data science, automation, and systems thinking, thereby necessitating an evolution of both research practices and educational structures.

Professor Nicola Gaston is the Director of the MacDiarmid Institute for Advanced Materials and Nanotechnology, a New Zealand Centre of Research Excellence, and a Professor in Physics at the University of Auckland, where her research group conducts quantum-mechanical simulations of the electronic and thermodynamic behaviour of nanoscale materials.

Mechanoradicals are known to be sources of damage during polymer processing, and it remains challenging to stabilize, repair or utilize them. Recently, mechanoradicals formed during chain scission were intercepted directly by bis(butyl trithiocarbonate) in the solid state, redirecting the degradation pathways to repolymerization (repair) and complete depolymerization (recovery).

We are pleased to present our Editors’ Highlights Collection, featuring some of our favourite content published in Communications Chemistry in 2025. Here, we highlight why each paper was selected by our Editors and Editorial Board Members.

Mental health should be a priority for all and especially for underrepresented groups, including the women in chemistry. Here, the authors discuss and propose supportive mechanisms within academia for the mental health of undergraduate women in chemistry.

Dr Laura Cattaneo is a group leader at the Max Planck Institute for Nuclear Physics in Heidelberg, Germany. She has an interdisciplinary research background in chemical physics, connecting materials science, liquid crystals, and ultrafast processes in matter.