The term “zwitterionic polymers” refers to polymers that bear a pair of oppositely charged
groups in their repeating units. When these oppositely charged groups are equally …

Quantitative structure–activity relationship (QSAR) modelling, an approach that was
introduced 60 years ago, is widely used in computer-aided drug design. In recent years …

The discovery of novel structural classes of antibiotics is urgently needed to address the
ongoing antibiotic resistance crisis,,,,,,,–. Deep learning approaches have aided in exploring …

Electrocatalysts and photocatalysts are key to a sustainable future, generating clean fuels,
reducing the impact of global warming, and providing solutions to environmental pollution …

Drug discovery is adapting to novel technologies such as data science, informatics, and
artificial intelligence (AI) to accelerate effective treatment development while reducing costs …

Graph neural networks (GNNs) have been widely used in molecular property prediction, but
explaining their black-box predictions is still a challenge. Most existing explanation methods …

Alloy modelling has a history of machine-learning-like approaches, preceding the tide of
data-science-inspired work. The dawn of computational databases has made the integration …

Machine learning models are poised to make a transformative impact on chemical sciences
by dramatically accelerating computational algorithms and amplifying insights available from …

Deep learning bears promise for drug discovery, including advanced image analysis,
prediction of molecular structure and function, and automated generation of innovative …

Breakthroughs in molecular and materials discovery require meaningful outliers to be
identified in existing trends. As knowledge accumulates, the inherent bias of human intuition …