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20232026
most citedControllable Data Generation Via Iterative Data-Property Mutual Mappings

1 citations · 1 across the 9 of their papers we have counts for

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cs.LG2026

MolWorld: Molecule World Models for Actionable Molecular Optimization

Yang Qiao, Bo Pan, Hao-Wei Pang +3

Molecular optimization in drug discovery aims to discover molecules with improved target properties, but practical lead optimization often requires more than high predicted scores.…

cs.LG2026

Retrieval-Augmented Foundation Models for Matched Molecular Pair Transformations to Recapitulate Medicinal Chemistry Intuition

Bo Pan, Peter Zhiping Zhang, Hao-Wei Pang +4

Matched molecular pairs (MMPs) capture the local chemical edits that medicinal chemists routinely use to design analogs, but existing ML approaches either operate at the whole-mole…

cs.LG2026

Transformer-Based Approach for Automated Functional Group Replacement in Chemical Compounds

Bo Pan, Zhiping Zhang, Kevin Spiekermann +4

Functional group replacement is a pivotal approach in cheminformatics to enable the design of novel chemical compounds with tailored properties. Traditional methods for functional…

cs.LG2025

Can Past Experience Accelerate LLM Reasoning?

Bo Pan, Liang Zhao

Allocating more compute to large language models (LLMs) reasoning has generally been demonstrated to improve their effectiveness, but also results in increased inference time. In c…

cs.LG2024

GraphNarrator: Generating Textual Explanations for Graph Neural Networks

Bo Pan, Zhen Xiong, Guanchen Wu +3

Graph representation learning has garnered significant attention due to its broad applications in various domains, such as recommendation systems and social network analysis. Despi…

cs.LG2024

TAGA: Text-Attributed Graph Self-Supervised Learning by Synergizing Graph and Text Mutual Transformations

Zheng Zhang, Yuntong Hu, Bo Pan +2

Text-Attributed Graphs (TAGs) enhance graph structures with natural language descriptions, enabling detailed representation of data and their relationships across a broad spectrum…