Themed collection Most popular 2021 physical and theoretical chemistry articles, 2021

The process of automating chemistry involves a wide variety of considerations that are often overlooked.

In this review article, we intend to highlight the basic electronic structure principles and various reactivity descriptors as defined within the premise of conceptual density functional theory (CDFT).

Materials discovery is a crucial yet experimentally slow and wasteful process. We discuss how discovery can be accelerated by focusing on making predictions that are synthetically realisable.

The N-glycans structures affect the mechanistic properties of the SARS-CoV-2 S, fine-tuning the glycoprotein. The evolution of the glycan shield led to the loss of N370 glycosylation in SARS-CoV-2 S, where the RBD cleft can bind host-cell glycans.

The main protease (M^pro) of SARS-CoV-2 is central to viral maturation and is a promising drug target. In silico methods reveal structural aspects of how it binds to its 11 natural cleavage sites, the design of novel peptide inhibitors, and insights into drug design.

From quantum chemical and experimental NMR data, a 3D graph neural network, CASCADE, has been developed to predict carbon and proton chemical shifts. Stereoisomers and conformers of organic molecules can be correctly distinguished.

Temperature artifacts in protein structures impact the utility of structural information in computation by misleading validation and application of computational methods in discovering bioactive molecules.

A full picture of a new multi-photon excitation mechanism relying on sTTA upconversion is provided, together with selected photocatalytic applications. All mechanistic steps are investigated and the catalytically active species is observed directly.

Color tunable luminescent materials with multiple emission centers showing delayed fluorescence, single molecular phosphorescence and aggregated molecular phosphorescence are achieved, along with time-dependent and excitation-dependent properties.

The enzymatic transformer was trained with a combination of patent reactions and biotransformations and predicts the structure and stereochemistry of enzyme-catalyzed reaction products with remarkable accuracy.

DeepFrag is a machine-learning model designed to assist with lead optimization. It recommends appropriate fragment additions given the 3D structures of a protein receptor and bound small-molecule ligand.

Interpolation and exploration within the chemical space for inverse design.

Molecular dynamics simulations were used to simulate the molecular conformation and interaction between hosts and guests. This work provides a new concept for the study of molecular packing for the investigation of the luminescence mechanism.

A novel scheme for the automated calculation of the conformational entropy together with a modified thermostatistical treatment provides entropies with unprecedented accuracy even for large, complicated molecules.

A surrogate model is designed to represent a microfluidic material synthesis system using 1000 automatically conducted experiments. With this model, over 600 000 experiments are simulated to optimize an AI-guided material synthesis algorithm.

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).

We develop computationally affordable and encoding independent gradient evaluation procedures for unitary coupled-cluster type operators, applicable on quantum computers.

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.

A random forest algorithm, trained on a concise dataset and validated experimentally, accurately predicts the stereoselectivity of a complex organic coupling varying all reaction parameters as well as previously unknown mechanistic influences.

The electron spin polarization inversion and anisotropic decay of triplet substates explain the short triplet state lifetime of BODIPY derivatives.

Compression is used to derive a long sought-after connection between two central chemical concepts – van-der-Waals (vdW) radii and electronegativity – and how these relate to the driving forces behind chemical and physical transformations.

How quantum spectroscopic simulations can explain water solvation by comparison with experimental spectra.

Prediction of bond dissociation energies for charged molecules with a graph neural network enabled by global molecular features and reaction difference features between products and reactants.

Extension of ALMO-EDA to include contributions from continuum solvent and application to electro- and photochemical CO₂ reduction catalysts.

QM/MM simulations identify the mechanism of reaction of N3, a covalent peptidyl inhibitor of SARS-CoV-2 main protease. Modelling of two novel proposed compounds, B1 and B2, suggests that reversibility of covalent inhibition could be tailored.

The main protease (M^pro) of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) is an attractive target for antiviral therapeutics.

Overtones and combinations of O–H vibrations in the solvent efficiently quench red-emitting fluorophores by resonant energy transfer.

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

A multifunctional, AIE-based, fused five-membered azaheterocycle is photogenerated with light-driven amplification to combat the photobleaching issue and fabricate display materials.

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.