Summary of Molecule Generation with Fragment Retrieval Augmentation, by Seul Lee et al.
Molecule Generation with Fragment Retrieval Augmentation
by Seul Lee, Karsten Kreis, Srimukh Prasad Veccham, Meng Liu, Danny Reidenbach, Saee Paliwal, Arash Vahdat, Weili Nie
First submitted to arxiv on: 18 Nov 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: None
GrooveSquid.com Paper Summaries
GrooveSquid.com’s goal is to make artificial intelligence research accessible by summarizing AI papers in simpler terms. Each summary below covers the same AI paper, written at different levels of difficulty. The medium difficulty and low difficulty versions are original summaries written by GrooveSquid.com, while the high difficulty version is the paper’s original abstract. Feel free to learn from the version that suits you best!
| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary The proposed Fragment Retrieval-Augmented Generation (f-RAG) framework addresses the limitations of existing fragment-based molecule generation methods, which primarily reassemble or modify existing fragments. f-RAG is based on a pre-trained molecular generative model that retrieves two types of fragments: hard and soft fragments. Hard fragments serve as building blocks for the newly generated molecule, while soft fragments guide the generation process through a trainable fragment injection module. The framework iteratively refines its vocabulary by generating new fragments and updating it with post-hoc genetic modification. This approach achieves an improved exploration-exploitation trade-off. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary f-RAG is a new way to create new molecules for medicine by using small pieces of existing molecules. Right now, most methods just take those pieces and reassemble them or make slight changes. But f-RAG does something different. It uses a special computer model that can propose new molecule fragments from the input fragments. This helps generate more diverse and unique molecules. The framework also has an iterative process to refine its vocabulary of available fragments. |
Keywords
» Artificial intelligence » Generative model » Rag » Retrieval augmented generation
