Artificial intelligence (AI) is being increasingly integrated into scientific discovery to augment
and accelerate research, hel** scientists to generate hypotheses, design experiments …

Abstract Machine learning plays an increasingly important role in many areas of chemistry
and materials science, being used to predict materials properties, accelerate simulations …

Large language models (LLMs) have shown strong performance in tasks across domains
but struggle with chemistry-related problems. These models also lack access to external …

Over the last decades, excellent computational chemistry tools have been developed.
Integrating them into a single platform with enhanced accessibility could help reaching their …

Abstract Machine learning has provided a means to accelerate early-stage drug discovery
by combining molecule generation and filtering steps in a single architecture that leverages …

Geometric deep learning (GDL) is based on neural network architectures that incorporate
and process symmetry information. GDL bears promise for molecular modelling applications …

New tools enable new ways of working, and materials science is no exception. In materials
discovery, traditional manual, serial, and human-intensive work is being augmented by …

Introduction: Artificial intelligence (AI) has inspired computer-aided drug discovery. The
widespread adoption of machine learning, in particular deep learning, in multiple scientific …

Chemical reaction networks (CRNs), defined by sets of species and possible reactions
between them, are widely used to interrogate chemical systems. To capture increasingly …

The recent advances in neural language models have also been successfully applied to the
field of chemistry, offering generative solutions for classical problems in molecular design …