Themed collection Editor’s Choice – Graeme Day

The library of isostructural porous frameworks enables a systematic survey to optimize the structure and functionality of porous materials.

Dynamics governing ultrafast chemical reactions can be efficiently simulated using analog quantum simulators composed of a coupled system of qudits and bosonic modes.

A structural ensemble derived from cryo-electron microscopy reveals a cryptic pocket site in intermediate states along the opening pathway of the SARS-CoV-2 spike protein.

An implementation of attention within the variational autoencoder framework for continuous representation of molecules. The addition of attention significantly increases model performance for complex tasks such as exploration of novel chemistries.

Essential factors leading to unidirectional charge separation in photosynthetic reaction centers are clarified via nonadiabatic quantum dynamics calculations.

A machine learning model for enantioselectivity prediction using reaction-based molecular representations.

A combination of MD simulations and a polarizable QM/MM description of a bacteriophytochrome in solution reveals the changes in the chromophore-binding pocket and in the overall structure of the phytochrome involved in the signaling mechanism.

Kinetic models of autocatalytic reactions have mathematical forms similar to activation functions used in artificial neural networks. Inspired by these similarities, we use a copper-catalyzed reaction to perform digital image recognition tasks.

A high-throughput workflow for connectivity preserving geometry optimization minimizes unintended structural rearrangements during quantum chemistry big data generation.

Machine learning can circumvent explicit calculation of dielectric response in first principles methods and accelerate simulations of optical properties of complex materials at finite temperature.

A chemically consistent graph architecture enables autonomous identification of novel solid-electrolyte interphase formation pathways from a massive reaction network.

We provide an unsupervised adaptive sampling strategy capable of producing μs-timescale molecular dynamics (MD) simulations of large biosystems using many-body polarizable force fields (PFFs).

Using a cluster-based training scheme and a physical baseline, data efficient machine-learning models for crystal structure prediction are developed, enabling accurate structural relaxations of molecular crystals with unprecedented efficiency.

The retrosynthetic accessibility score (RAscore) is based on AI driven retrosynthetic planning, and is useful for rapid scoring of synthetic feasability and pre-screening of large datasets of virtual/generated molecules.

We investigate via atomistic simulations the capture of lead ions from water by hemicellulose – as representative of the polysaccharides that are common components of vegetables and fruit peels – and the reverse process.

A hierarchical computational strategy for IDP drug virtual screening (IDPDVS) was proposed and successfully applied to identify compounds that bind p53 TAD1 and restore wild-type p53 function in cancer cells.

The coupling of a molecule and a cavity induces nonadiabaticity in the molecule which makes the description of its dynamics complicated.

Correlated wavefunction theory predicts and high-resolution crystal structure analysis confirms the important, stabilizing effect of simultaneous hydrogen bond donor and acceptor interactions in proteins.

Hybrid reactivity models, combining mechanistic calculations and machine learning with descriptors, are used to predict barriers for nucleophilic aromatic substitution.

Using reversed allosteric communication, we performed MD simulations, MSMs, and mutagenesis experiments, to discover allosteric sites. It reproduced the known allosteric site for MDL-801 on Sirt6 and uncovered a novel cryptic allosteric Pocket X.