Graph Convolutional Policy Network for Goal-Directed Molecular Graph Generation
proceedings.neurips.cc · 7,269 words · saved by 1 readers
N/A
Graph Convolutional Policy Network for Goal-Directed Molecular Graph Generation Jiaxuan You1∗ Bowen Liu2∗ jiaxuan@stanford.edu liubowen@stanford.edu Rex Ying1 Vijay Pande3 Jure Leskovec1 rexying@stanford.edu pande@stanford.edu jure@cs.stanford.edu 1 Department of Computer Science, 2 Department of Chemistry, 3 Department of Bioengineering Stanford…
saved by
related reading
- TorchDrug: A Powerful and Flexible Machine Learning Platform for Drug Discoveryarxiv.org
- A Gentle Introduction to Graph Neural Networksdistill.pub
- [2209.05582] Graph Neural Networks for Moleculesarxiv.org
- Understanding Convolutions on Graphsdistill.pub
- Beyond Message Passing: a Physics-Inspired Paradigm for Graph Neural Networksthegradient.pub
- Graph Convolutional Networks | Thomas Kipf | Google DeepMindtkipf.github.io
- Let Your Graph Do the Talking: Encoding Structured Data for LLMsarxiv.org
- diffuse.onediffuse.one
- [2207.02505] Pure Transformers are Powerful Graph Learnersarxiv.org
- Relating Graph Neural Networks to Structural Causal Modelsarxiv.org
- [2605.03327] DGPO: Distribution Guided Policy Optimization for Fine Grained Credit Assignmentarxiv.org
- Generative ML in chemistry is bottlenecked by synthesisowlposting.com