Artificial intelligence (AI) and machine learning (ML) are expanding in popularity for broad
applications to challenging tasks in chemistry and materials science. Examples include the …

Surface‐enhanced Raman spectroscopy (SERS), well acknowledged as a fingerprinting
and sensitive analytical technique, has exerted high applicational value in a broad range of …

The number of publications describing chemical structures has increased steadily over the
last decades. However, the majority of published chemical information is currently not …

Molecular structure recognition is the task of translating a molecular image into its graph
structure. Significant variation in drawing styles and conventions exhibited in chemical …

The amount of data available on chemical structures and their properties has increased
steadily over the past decades. In particular, articles published before the mid-1990 are …

Optical chemical structure recognition from scientific publications is essential for
rediscovering a chemical structure. It is an extremely challenging problem, and current rule …

Information extraction from chemistry literature is vital for constructing up-to-date reaction
databases for data-driven chemistry. Complete extraction requires combining information …

In the field of chemical structure recognition, the task of converting molecular images into
machine-readable data formats such as SMILES string stands as a significant challenge …

The rise of deep learning in various scientific and technology areas promotes the
development of AI‐based tools for information retrieval. Optical recognition of organic …

The automatic analysis of chemical literature has immense potential to accelerate the
discovery of new materials and drugs. Much of the critical information in patent documents …