Machine-learned ranking models have been developed for the prediction of substrate-
specific cross-coupling reaction conditions. Data sets of published reactions were curated …

Identifying optimal synthesis conditions for metal–organic frameworks (MOFs) is a major
challenge that can serve as a bottleneck for new materials discovery and development. A …

Automated synthesis planning is key for efficient generative chemistry. Since reactions of
given reactants may yield different products depending on conditions such as the chemical …

This paper focuses on using natural language descriptions to enhance predictive models in
the chemistry field. Conventionally, chemoinformatics models are trained with extensive …

Graph neural networks (GNNs), which are capable of learning representations from
graphical data, are naturally suitable for modeling molecular systems. This review …

In synthesis planning, it is important to determine suitable reaction conditions such that a
chemical reaction proceeds as intended. Recent research attempts based on machine …

Structured documents, such as scientific literature and medical records, are rich resources of
knowledge. However, most natural language processing techniques treat these documents …

Optimization of synthetic reactions represents a crucial step in expediting the exploration of
innovative pharmaceuticals and materials, serving as an indispensable tool. To achieve a …