FragFM: Efficient Fragment-Based Molecular Generation via Discrete Flow Matching

• URL: http://arxiv.org/abs/2502.15805v1
• Date: Wed, 19 Feb 2025 07:01:00 GMT
• Title: FragFM: Efficient Fragment-Based Molecular Generation via Discrete Flow Matching
• Authors: Joongwon Lee, Seonghwan Kim, Wou Youn Kim,
• Abstract summary: We introduce FragFM, a novel fragment-based discrete flow matching framework for molecular graph generation.<n>FragFM generates molecules at the fragment level, leveraging a coarse-to-fine autoencoding mechanism to reconstruct atom-level details.
• Score: 0.3345437353879254
• License: http://creativecommons.org/licenses/by-nc-nd/4.0/
• Abstract: We introduce FragFM, a novel fragment-based discrete flow matching framework for molecular graph generation.FragFM generates molecules at the fragment level, leveraging a coarse-to-fine autoencoding mechanism to reconstruct atom-level details. This approach reduces computational complexity while maintaining high chemical validity, enabling more efficient and scalable molecular generation. We benchmark FragFM against state-of-the-art diffusion- and flow-based models on standard molecular generation benchmarks and natural product datasets, demonstrating superior performance in validity, property control, and sampling efficiency. Notably, FragFM achieves over 99\% validity with significantly fewer sampling steps, improving scalability while preserving molecular diversity. These results highlight the potential of fragment-based generative modeling for large-scale, property-aware molecular design, paving the way for more efficient exploration of chemical space.

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