@inproceedings{
lee2024molecule,
title={Molecule Generation with Fragment Retrieval Augmentation},
author={Seul Lee and Karsten Kreis and Srimukh Prasad Veccham and Meng Liu and Danny Reidenbach and Saee Gopal Paliwal and Arash Vahdat and Weili Nie},
booktitle={The Thirty-eighth Annual Conference on Neural Information Processing Systems},
year={2024},
url={https://openreview.net/forum?id=56Q0qggDlp}
}
Fragment-based drug discovery, in which molecular fragments are assembled into new molecules with desirable biochemical properties, has achieved great success. However, many fragment-based molecule generation methods show limited exploration beyond the existing fragments in the database as they only reassemble or slightly modify the given ones. To tackle this problem, we propose a new fragment-based molecule generation framework with retrieval augmentation, namely *Fragment Retrieval-Augmented Generation* (*f*-RAG). *f*-RAG is based on a pre-trained molecular generative model that proposes additional fragments from input fragments to complete and generate a new molecule. Given a fragment vocabulary, *f*-RAG retrieves two types of fragments: (1) *hard fragments*, which serve as building blocks that will be explicitly included in the newly generated molecule, and (2) *soft fragments*, which serve as reference to guide the generation of new fragments through a trainable *fragment injection module*. To extrapolate beyond the existing fragments, *f*-RAG updates the fragment vocabulary with generated fragments via an iterative refinement process which is further enhanced with post-hoc genetic fragment modification. *f*-RAG can achieve an improved exploration-exploitation trade-off by maintaining a pool of fragments and expanding it with novel and high-quality fragments through a strong generative prior.
Molecule Generation with Fragment Retrieval Augmentation
Seul Lee Karsten Kreis Srimukh Prasad Veccham Meng Liu Danny Reidenbach Saee Gopal Paliwal Arash Vahdat Weili Nie