Predictive chemistry: machine learning for reaction deployment, reaction development, and reaction discovery
The field of predictive chemistry relates to the development of models able to describe how
molecules interact and react. It encompasses the long-standing task of computer-aided …
molecules interact and react. It encompasses the long-standing task of computer-aided …
Artificial Intelligence for Surface‐Enhanced Raman Spectroscopy
Surface‐enhanced Raman spectroscopy (SERS), well acknowledged as a fingerprinting
and sensitive analytical technique, has exerted high applicational value in a broad range of …
and sensitive analytical technique, has exerted high applicational value in a broad range of …
Retrosynthesis prediction with an interpretable deep-learning framework based on molecular assembly tasks
Automating retrosynthesis with artificial intelligence expedites organic chemistry research in
digital laboratories. However, most existing deep-learning approaches are hard to explain …
digital laboratories. However, most existing deep-learning approaches are hard to explain …
Recent advances in deep learning for retrosynthesis
Retrosynthesis is the cornerstone of organic chemistry, providing chemists in material and
drug manufacturing access to poorly available and brand‐new molecules. Conventional rule …
drug manufacturing access to poorly available and brand‐new molecules. Conventional rule …
Retrosynthesis prediction with an iterative string editing model
Retrosynthesis is a crucial task in drug discovery and organic synthesis, where artificial
intelligence (AI) is increasingly employed to expedite the process. However, existing …
intelligence (AI) is increasingly employed to expedite the process. However, existing …
Llasmol: Advancing large language models for chemistry with a large-scale, comprehensive, high-quality instruction tuning dataset
Chemistry plays a crucial role in many domains, such as drug discovery and material
science. While large language models (LLMs) such as GPT-4 exhibit remarkable …
science. While large language models (LLMs) such as GPT-4 exhibit remarkable …
Dual-view molecular pre-training
Molecular pre-training, which is about to learn an effective representation for molecules on
large amount of data, has attracted substantial attention in cheminformatics and …
large amount of data, has attracted substantial attention in cheminformatics and …
Exploring chemical reaction space with machine learning models: Representation and feature perspective
Chemical reactions serve as foundational building blocks for organic chemistry and drug
design. In the era of large AI models, data-driven approaches have emerged to innovate the …
design. In the era of large AI models, data-driven approaches have emerged to innovate the …
The impact of large language models on scientific discovery: a preliminary study using gpt-4
MR AI4Science, MA Quantum - arxiv preprint arxiv:2311.07361, 2023 - arxiv.org
In recent years, groundbreaking advancements in natural language processing have
culminated in the emergence of powerful large language models (LLMs), which have …
culminated in the emergence of powerful large language models (LLMs), which have …
Re-evaluating retrosynthesis algorithms with syntheseus
Automated synthesis planning has recently re-emerged as a research area at the
intersection of chemistry and machine learning. Despite the appearance of steady progress …
intersection of chemistry and machine learning. Despite the appearance of steady progress …