most citedLarge Language Models to Accelerate Organic Chemistry Synthesis

1 citations · 1 across the 1 of their papers we have counts for

collaborators

5 papers

cs.AI2025

ChemActor: Enhancing Automated Extraction of Chemical Synthesis Actions with LLM-Generated Data

Yu Zhang, Ruijie Yu, Jidong Tian +5

With the increasing interest in robotic synthesis in the context of organic chemistry, the automated extraction of chemical procedures from literature is critical. However, this ta…

physics.chem-ph20251 cited

Large Language Models to Accelerate Organic Chemistry Synthesis

Yu Zhang, Yang Han, Shuai Chen +10

Chemical synthesis, as a foundational methodology in the creation of transformative molecules, exerts substantial influence across diverse sectors from life sciences to materials a…

cs.LG2024

A General-Purpose Framework for Chemical Reaction Representation with Atomic Correspondence and Flexible Condition Adaptation

Kaipeng Zeng, Xianbin Liu, Yu Zhang +3

Motivation: Organic synthesis is fundamental to the chemical industry, particularly in domains such as pharmaceutical development. While artificial intelligence offers powerful too…

cs.AI2024

Text-Augmented Multimodal LLMs for Chemical Reaction Condition Recommendation

Yu Zhang, Ruijie Yu, Kaipeng Zeng +5

Identifying reaction conditions that are broadly applicable across diverse substrates is a longstanding challenge in chemical and pharmaceutical research. While many methods are av…

physics.chem-ph2024

UAlign: Pushing the Limit of Template-free Retrosynthesis Prediction with Unsupervised SMILES Alignment

Kaipeng Zeng, Bo yang, Xin Zhao +5

Motivation: Retrosynthesis planning poses a formidable challenge in the organic chemical industry. Single-step retrosynthesis prediction, a crucial step in the planning process, ha…