Themed collection Accelerating Chemistry Symposium Collection

The grand challenge of materials science, discovery of novel materials with target properties, can be greatly accelerated by machine-learned inverse design strategies.

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.

Linking fragments to generate a focused compound library for a specific drug target is one of the challenges in fragment-based drug design (FBDD).

Stability and compatibility between chemical components are essential parameters that need to be considered in the selection of functional materials in configuring a system.

QCaRA successfully predicted a new synthetic path based on the reaction path network produced by quantum chemical calculation.

Our developed algorithm, BLOX (BoundLess Objective-free eXploration), successfully found “out-of-trend” molecules potentially useful for photofunctional materials from a drug database.

We introduce a representation for the geometric features of the pores of porous molecular crystals. This representation provides a good basis for supervised (predict adsorption properties) and unsupervised (polymorph classification) tasks.

The mechanical properties of nucleic acids underlie biological processes ranging from genome packaging to gene expression. We devise structural mechanics statistical learning method to reveal their molecular origin in terms of chemical interactions.

Evolutionary optimisation and crystal structure prediction are used to explore chemical space for molecular organic semiconductors.

A machine learning exploration of the chemical space surrounding Vaska's complex.

A new multi-label deep neural network architecture is used to combine Infrared and mass spectra, trained on single compounds to predict functional groups, and experimentally validated on complex mixtures.

A neural network is used to reveal composition-dependent structural evolution under operando conditions in CuZn nanocatalysts for CO₂ electroreduction.

Retrosynthetic pathway planning using a template-free model coupled with heuristic Monte Carlo tree search.

UCBShift predicts NMR chemical shifts of proteins that exceeds accuracy of other popular chemical shift predictors on real-world data sets.

Two complementary measurements, fluorescence polarization anisotropy and aggregation-induced emission, allow for in situ optical monitoring of polymerization reaction progress in droplets across varying temporal regimes of the reaction.

The IMPRESSION machine learning system can predict NMR parameters for 3D structures with similar results to DFT but in seconds rather than hours.

Computer Assisted Synthesis Planning (CASP), datasets and their performance.

New crystal forms of two well-studied organic molecules are identified in a computationally targeted way, by combining structure prediction with a robotic crystallisation screen, including a ‘hidden’ porous polymorph of trimesic acid.

High-throughput automation was used to streamline the synthesis, characterisation, and solubility testing, of new Type II porous liquids, accelerating their discovery.

We introduce Delfos, a novel, machine-learning-based QSPR method which predicts solvation free energies for generic organic solutions.

Machine learning enables excited-state molecular dynamics simulations including nonadiabatic couplings on nanosecond time scales.

We utilize Particle Swarm Optimization to optimize molecules in a machine-learned continuous chemical representation with respect to multiple objectives such as biological activity, structural constrains or ADMET properties.

We map the property space of binary copolymers to understand how copolymerisation can be used to tune the optoelectronic properties of polymers.

A family of network algorithms allows the Chematica retrosynthetic platform to plan both cost-effective and chemically diverse syntheses.

Machine learning models, trained to reproduce molecular dynamics results, help interpreting simulations and extracting new understanding of chemistry.

Translation between semantically equivalent but syntactically different line notations of molecular structures compresses meaningful information into a continuous molecular descriptor.

We present a supervised learning approach to predict the products of organic reactions given their reactants, reagents, and solvent(s).

An evolutionary algorithm is developed and used to search for shape persistent porous organic cages.

Chimera enables multi-target optimization for experimentation or expensive computations, where evaluations are the limiting factor.

The standard paradigm in computational materials science is INPUT: STRUCTURE; OUTPUT: PROPERTIES, which has yielded many successes but is ill-suited for exploring large areas of chemical and configurational hyperspace.