Tag
A 326-page resource announcing the book 'Graph Neural Networks for Molecular Discovery with Python', covering molecular graph representation, geometric deep learning, property prediction, molecule generation, and drug/chemical discovery with practical Python implementations and a free PDF link.
Claude agents have discovered a new molecular mechanism, demonstrating human-like communication with interjections and cues during the discovery process.
Researchers propose a lightweight autoregressive framework for graph generation that uses structure-guided topological ordering to achieve near log-linear complexity, addressing scalability and novelty limitations of existing diffusion and autoregressive methods. The approach supports both LSTM and Mamba-style backbones and shows improved novelty while maintaining validity and uniqueness on molecular and non-molecular benchmarks.