Graph transformers are a recent advancement in machine learning, offering a new class of
neural network models for graph-structured data. The synergy between transformers and …

Chemical patents represent a valuable source of information about new chemical
compounds, which is critical to the drug discovery process. Automated information extraction …

In this paper, we provide an overview of the Cheminformatics Elsevier Melbourne University
(ChEMU) evaluation lab 2022, part of the Conference and Labs of the Evaluation Forum …

The task of searching through patent documents is crucial for chemical patent
recommendation and retrieval. This can be enhanced by creating a patent knowledge base …

The discovery of new chemical compounds is a key driver of the chemistry and
pharmaceutical industries, and many other industrial sectors. Patents serve as a critical …

Many new chemical compounds are reported each year in patent documents, leading to
increasing demand for methods for automatic information extraction of chemical compounds …

In this paper, we propose a new set of annotations for the ChEMU Chemical Reaction
Corpus. Our annotations (ChemProp) non-trivially incorporate the signals from ChEMU 2020 …

With the rapid growth of chemical patents, there is increasing demand for automated
extraction of information relating to chemical compounds and their synthesis from patents …