2 citations · 5 across the 6 of their papers we have counts for
8 papers
RetroPlanner: Multi-Route Expansion and Multi-Objective Reasoning for LLM-Based Retrosynthesis Planning
Ye Rin Kim, Yongjun Jeong, Seongjun Yun +4
Multi-step retrosynthesis planning is often considered solved once a route to purchasable starting materials is found, but such a route should also be practical in real-world chemi…
RetroReasoner: A Reasoning LLM for Strategic Retrosynthesis Prediction
Hanbum Ko, Chanhui Lee, Ye Rin Kim +4
Retrosynthesis prediction aims to identify reactants that can synthesize a given product molecule. Although molecular large language models (LLMs) have recently shown promising res…
ORCA: ORchestrating Causal Agent
Joanie Hayoun Chung, Sumin Lee, Sungbin Lim
Causal analysis on relational databases is challenging, as analysis datasets must be repeatedly queried from complex schemas. Recent LLM systems can automate individual steps, but…
Score-informed Neural Operator for Enhancing Ordering-based Causal Discovery
Jiyeon Kang, Songseong Kim, Chanhui Lee +6
Ordering-based approaches to causal discovery identify topological orders of causal graphs, providing scalable alternatives to combinatorial search methods. Under the Additive Nois…
CAST: Cross Attention based multimodal fusion of Structure and Text for materials property prediction
Jaewan Lee, Changyoung Park, Hongjun Yang +3
Recent advancements in graph neural networks (GNNs) have significantly enhanced the prediction of material properties by modeling crystal structures as graphs. However, GNNs often…
Mol-LLM: Multimodal Generalist Molecular LLM with Improved Graph Utilization
Chanhui Lee, Hanbum Ko, Yuheon Song +6
Recent advances in large language models (LLMs) have led to models that tackle diverse molecular tasks, such as chemical reaction prediction and molecular property prediction. Larg…
Scalable Multi-Task Transfer Learning for Molecular Property Prediction
Chanhui Lee, Dae-Woong Jeong, Sung Moon Ko +6
Molecules have a number of distinct properties whose importance and application vary. Often, in reality, labels for some properties are hard to achieve despite their practical impo…
Can We Utilize Pre-trained Language Models within Causal Discovery Algorithms?
Chanhui Lee, Juhyeon Kim, Yongjun Jeong +9
Scaling laws have allowed Pre-trained Language Models (PLMs) into the field of causal reasoning. Causal reasoning of PLM relies solely on text-based descriptions, in contrast to ca…