2 citations · 3 across the 2 of their papers we have counts for
6 papers · 1 filter
Graph Diffusion Transformers are In-Context Molecular Designers
Gang Liu, Jie Chen, Yihan Zhu +4
In-context learning allows large models to adapt to new tasks from a few demonstrations, but it has shown limited success in molecular design. Existing databases such as ChEMBL con…
Post Hoc Regression Refinement via Pairwise Rankings
Kevin Tirta Wijaya, Michael Sun, Minghao Guo +3
Accurate prediction of continuous properties is essential to many scientific and engineering tasks. Although deep-learning regressors excel with abundant labels, their accuracy det…
Two-Stage Pretraining for Molecular Property Prediction in the Wild
Kevin Tirta Wijaya, Minghao Guo, Michael Sun +3
Molecular deep learning models have achieved remarkable success in property prediction, but they often require large amounts of labeled data. The challenge is that, in real-world a…
Directed Graph Grammars for Sequence-based Learning
Michael Sun, Orion Foo, Gang Liu +2
Directed acyclic graphs (DAGs) are a class of graphs commonly used in practice, with examples that include electronic circuits, Bayesian networks, and neural architectures. While m…
Multimodal Large Language Models for Inverse Molecular Design with Retrosynthetic Planning
Gang Liu, Michael Sun, Wojciech Matusik +2
While large language models (LLMs) have integrated images, adapting them to graphs remains challenging, limiting their applications in materials and drug design. This difficulty st…
Representing Molecules as Random Walks Over Interpretable Grammars
Michael Sun, Minghao Guo, Weize Yuan +10
Recent research in molecular discovery has primarily been devoted to small, drug-like molecules, leaving many similarly important applications in material design without adequate t…