6 papers
ReCoG: Relational and Compact Context Graph Learning for Few-shot Molecular Property Prediction
Zeyu Wang, Xin Zheng, Yao Lu +3
Few-shot molecular property prediction (FSMPP) is essential in drug discovery and materials design, where high-quality labeled data are often scarce and expensive to obtain. Despit…
Variational Bayesian Flow Network for Graph Generation
Yida Xiong, Jiameng Chen, Xiuwen Gong +3
Graph generation aims to sample discrete node and edge attributes while satisfying coupled structural constraints. Diffusion models for graphs often adopt largely factorized forwar…
Text-guided multi-property molecular optimization with a diffusion language model
Yida Xiong, Kun Li, Jiameng Chen +4
Molecular optimization (MO) is a crucial stage in drug discovery in which task-oriented generated molecules are optimized to meet practical industrial requirements. Existing mainst…
LLM-based Agents Suffer from Hallucinations: A Survey of Taxonomy, Methods, and Directions
Xixun Lin, Yucheng Ning, Jingwen Zhang +21
Driven by the rapid advancements of Large Language Models (LLMs), LLM-based agents have emerged as powerful intelligent systems capable of human-like cognition, reasoning, and inte…
Fragment-Masked Diffusion for Molecular Optimization
Kun Li, Xiantao Cai, Jia Wu +4
Molecular optimization is a crucial aspect of drug discovery, aimed at refining molecular structures to enhance drug efficacy and minimize side effects, ultimately accelerating the…
Knowledge-aware contrastive heterogeneous molecular graph learning
Mukun Chen, Jia Wu, Shirui Pan +4
Molecular representation learning is pivotal in predicting molecular properties and advancing drug design. Traditional methodologies, which predominantly rely on homogeneous graph…