arXiv (OAI Expanded)
open access
Fraglingo: Molecular Design via Attachment-Aware Autoregressive Fragment Generation
We introduce Fraglingo, an autoregressive molecular generator that constructs molecules step by step from chemically meaningful fragments connected through predefined attachment sites. At each generation step, Fraglingo jointly predicts which fragment to add and how it should attach by producing an
Conceptio Open Archive
Analyze & explore related research in Conceptio
Connect this record directly into your research workflow or AI agent. Explore citations across open repositories, query full text, and export structured proof bundles.
This document is indexed with metadata only: full text is not available in the archive for this record. Open the official source ↗
Related documents
- FracGen: Learning How Objects Stretch and Tear with Physics-Informed Video Generation#1142447
- Mechanistic Reaction Prediction via Discrete Flow Matching on Graph-Structured Electron Occupation#780777
- Mechanistic Reaction Prediction via Discrete Flow Matching on Graph-Structured Electron Occupation#779402
- Squidly harnesses enzyme functional hierarchy and contrastive learning to efficiently predict catalytic residues from sequence.#659568
- Molecular Dynamics Workflows to Compute Large-Scale Sets of Absolute Binding Free Energies Aiding Drug Candidate and Binding Pose Selection.#239706
Record · ID 1149451
Retrieved via Conceptio: every document is proof-bundled with source, license, and retrieval metadata.
Retrieved via Conceptio: every document is proof-bundled with source, license, and retrieval metadata.
Bring this data into your agent: Connect via MCP or API · 20 free credits to start · no card required
Read the Docs → · View CLI source on GitHub · Conceptio Open Knowledge Archive
Read the Docs → · View CLI source on GitHub · Conceptio Open Knowledge Archive