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 …

Large-language models (LLMs) such as GPT-4 caught the interest of many scientists.
Recent studies suggested that these models could be useful in chemistry and materials …

The past decade has seen a number of impressive developments in predictive chemistry
and reaction informatics driven by machine learning applications to computer-aided …

The ability to integrate resources and share knowledge across organisations empowers
scientists to expedite the scientific discovery process. This is especially crucial in addressing …

The recent synergy of machine learning (ML) with molecular synthesis has emerged as an
increasingly powerful platform in organic synthesis and catalysis. This merger has set the …

The ever-increasing number of materials science articles makes it hard to infer chemistry-
structure-property relations from literature. We used natural language processing methods to …

Recently, attention mechanism and derived models have gained significant traction in drug
development due to their outstanding performance and interpretability in handling complex …

The urgency of finding solutions to global energy, sustainability, and healthcare challenges
has motivated rethinking of the conventional chemistry and material science workflows. Self …

Large Language Models (LLMs) have emerged as a transformative power in enhancing
natural language comprehension, representing a significant stride toward artificial general …

Motivation: The scientific literature embeds an enormous amount of relational knowledge,
encompassing interactions between biomedical entities, like proteins, drugs, and symptoms …