PI3K inhibitors: review and new strategies
The search is on for effective specific inhibitors for PI3Kα mutants.
Noble gas endohedral fullerenes
This review focuses on the available experimental and theoretical investigations on noble gas (Ng) endohedral fullerenes, addressing the effects of confinement of one or more Ng atoms into the electronic structure and reactivity of fullerenes.
Progress and prospects for accelerating materials science with automated and autonomous workflows
Integrating automation with artificial intelligence will enable scientists to spend more time identifying important problems and communicating critical insights, accelerating discovery and development of materials for emerging and future technologies.
DP4-AI automated NMR data analysis: straight from spectrometer to structure
A robust system for automatic processing and assignment of raw 13C and 1H NMR data DP4-AI has been developed and integrated into our computational organic molecule structure elucidation workflow.
Efficient light-harvesting, energy migration, and charge transfer by nanographene-based nonfullerene small-molecule acceptors exhibiting unusually long excited-state lifetime in the film state
A nonfullerene acceptor, TACIC, showed efficient light-harvesting, exciton diffusion, and charge transfer.
Predicting retrosynthetic pathways using transformer-based models and a hyper-graph exploration strategy
We present an extension of our Molecular Transformer model combined with a hyper-graph exploration strategy for automatic retrosynthesis route planning without human intervention.
Dye-sensitized solar cells under ambient light powering machine learning: towards autonomous smart sensors for the internet of things
Indoor light harvesters enable machine learning on fully autonomous IoT devices at 2.72 × 1015 photons per inference.
Target identification among known drugs by deep learning from heterogeneous networks
Target identification and drug repurposing could benefit from network-based, rational deep learning prediction, and explore the relationship between drugs and targets in the heterogeneous drug–gene–disease network.
Large scale relative protein ligand binding affinities using non-equilibrium alchemy
Relative ligand binding affinity calculations based on molecular dynamics (MD) simulations and non-physical (alchemical) thermodynamic cycles have shown great promise for structure-based drug design.
IMPRESSION – prediction of NMR parameters for 3-dimensional chemical structures using machine learning with near quantum chemical accuracy
The IMPRESSION machine learning system can predict NMR parameters for 3D structures with similar results to DFT but in seconds rather than hours.
Datasets and their influence on the development of computer assisted synthesis planning tools in the pharmaceutical domain
Computer Assisted Synthesis Planning (CASP), datasets and their performance.
Electron density learning of non-covalent systems
Machine learning model of the electron densities for analyzing non-covalent interaction patterns in peptides.
Machine learning enables long time scale molecular photodynamics simulations
Machine learning enables excited-state molecular dynamics simulations including nonadiabatic couplings on nanosecond time scales.
Low-order many-body interactions determine the local structure of liquid water
Two-body and three-body energies, modulated by higher-body terms and nuclear quantum effects, determine the structure of liquid water and require sub-chemical accuracy that is achieved by the MB-pol model but not by existing DFT functionals.
A quantitative uncertainty metric controls error in neural network-driven chemical discovery
A predictive approach for driving down machine learning model errors is introduced and demonstrated across discovery for inorganic and organic chemistry.
Covalency and magnetic anisotropy in lanthanide single molecule magnets: the DyDOTA archetype
The unexpected covalent contribution in the DOTADy-OH2 bond revealed by ab initio calculations of the easy axis of magnetization through simple H2O rotations.
Enantioseparation by crystallization using magnetic substrates
Enantiospecific crystallization of the three amino acids asparagine (Asn), glutamic acid hydrochloride (Glu·HCl) and threonine (Thr), induced by ferromagnetic (FM) substrates, is reported.
Helical orbitals and circular currents in linear carbon wires
Disubstituted odd-carbon cumulenes are linear carbon wires with helical π-orbitals, which results in circular current around the wire.
Selection of cost-effective yet chemically diverse pathways from the networks of computer-generated retrosynthetic plans
A family of network algorithms allows the Chematica retrosynthetic platform to plan both cost-effective and chemically diverse syntheses.
Dual-wavelength efficient two-photon photorelease of glycine by π-extended dipolar coumarins
Efficient photolabile protecting groups: how to achieve exceptional photo-triggered amino-acid delivery upon irradiation in the NIR.
An Al-doped SrTiO3 photocatalyst maintaining sunlight-driven overall water splitting activity for over 1000 h of constant illumination
The development of robust and efficient water splitting photocatalysts overcomes a long-standing barrier to sustainable large-scale solar hydrogen evolution systems.
The chemical reactions in electrosprays of water do not always correspond to those at the pristine air–water interface
This contribution explains the origin of dramatic rate accelerations in chemical reactions taking place in/on aqueous electrosprays. We combine experiments with electrosprays and proton-nuclear magnetic resonance with quantum mechanics to systematically decouple genuine interfacial effects from non-equilibrium conditions.
How machine learning can assist the interpretation of ab initio molecular dynamics simulations and conceptual understanding of chemistry
Machine learning models, trained to reproduce molecular dynamics results, help interpreting simulations and extracting new understanding of chemistry.
Density matrix renormalization group pair-density functional theory (DMRG-PDFT): singlet–triplet gaps in polyacenes and polyacetylenes
The density matrix renormalization group (DMRG) is a powerful method to treat static correlation.
Learning continuous and data-driven molecular descriptors by translating equivalent chemical representations
Translation between semantically equivalent but syntactically different line notations of molecular structures compresses meaningful information into a continuous molecular descriptor.
Determination of the structure and geometry of N-heterocyclic carbenes on Au(111) using high-resolution spectroscopy
The geometry and bonding of N-heterocyclic carbenes to metal surfaces depends on the substituents on the N-atoms.
Relative orientation of the carbonyl groups determines the nature of orbital interactions in carbonyl–carbonyl short contacts
The nature of orbital interactions in a carbonyl–carbonyl short contact is determined by the relative orientation of the two interacting carbonyl groups.
A graph-convolutional neural network model for the prediction of chemical reactivity
We present a supervised learning approach to predict the products of organic reactions given their reactants, reagents, and solvent(s).
Photoelectrocatalytic H2 evolution from integrated photocatalysts adsorbed on NiO
A new approach to increasing the faradaic efficiency of dye-sensitised photocathodes for H2 evolution from water is described, using integrated photocatalysts based on a ruthenium 4,4′-diethoxycarboxy-2,2′-bipyridine chromophore linked via terpyridine or triazole to a Pd or Pt-based H+ reduction catalyst.
Photon-upconverting chiral liquid crystal: significantly amplified upconverted circularly polarized luminescence
By blending a chiral acceptor and a sensitizer into a nematic liquid crystal, a chiral nematic liquid crystal showing amplified upconverted circularly polarized luminescence could be obtained.
About this collection
This specially curated collection pulls together some of the most popular articles from 2019 and 2020 in the field of physical and theoretical chemistry. The collection presents some outstanding contributions to the field, ranging from molecular dynamics to machine learning, and as with all Chemical Science articles – they are all completely free to access and read. We hope you enjoy browsing through this collection.
Most popular 2019-2020 inorganic, main group and crystal engineering chemistry articles
Most popular 2019-2020 materials and energy chemistry articles
Most popular 2019-2020 organic chemistry articles
Most popular 2019-2020 catalysis articles
Most popular 2019-2020 analytical chemistry articles
Most popular 2019-2020 supramolecular chemistry articles
Most popular 2019-2020 chemical biology articles
Most popular 2019-2020 review articles