From the start of a synthetic chemist's training, experiments are conducted based on recipes
from textbooks and manuscripts that achieve clean reaction outcomes, allowing the scientist …

Artificial intelligence (AI) coupled with promising machine learning (ML) techniques well
known from computer science is broadly affecting many aspects of various fields including …

Research in chemistry increasingly requires interdisciplinary work prompted by, among
other things, advances in computing, machine learning, and artificial intelligence. Everyone …

Chemical reaction data in journal articles, patents, and even electronic laboratory notebooks
are currently stored in various formats, often unstructured, which presents a significant …

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 …

Prediction of chemical bioactivity and physical properties has been one of the most
important applications of statistical and more recently, machine learning and artificial …

Due to the rapid emergence of antibiotic-resistant bacteria, there is a growing need to
discover new antibiotics. To address this challenge, we trained a deep neural network …

Machine learning (ML) techniques applied to chemical reactions have a long history. The
present contribution discusses applications ranging from small molecule reaction dynamics …

Chemical engineers rely on models for design, research, and daily decision-making, often
with potentially large financial and safety implications. Previous efforts a few decades ago to …

INTRODUCTION The ability to synthesize complex organic molecules is essential to the
discovery and manufacture of functional compounds, including small-molecule medicines …