FragmentNet: Adaptive Graph Fragmentation for Graph-to-Sequence Molecular Representation Learning
arXiv:2502.01184v2 Announce Type: replace Abstract: Molecular representation learning methods typically tokenize molecules as individual atoms or use rigid, rule-based fragment decompositions, limiting their ability to capture meaningful chemical substructure context. We introduce FragmentNet, a graph-to-sequence model built around a novel adaptive, learned tokenizer that decomposes molecular graphs into chemically valid fragments of adjustable granularity, complemented by chemically aware spatial positional encodings that preserve molecular topology in the resulting sequence. Extending masked
This development reflects the ongoing advancements in AI and machine learning applied to scientific discovery, leveraging recent gains in graph neural networks and sequential modeling for molecular data.
FragmentNet's adaptive approach to molecular representation learning could significantly accelerate drug discovery, materials science, and synthetic biology by enabling more accurate and efficient molecular design and optimization.
This model introduces a more sophisticated and flexible way to represent and understand molecular structures compared to traditional fixed or rule-based methods, potentially leading to breakthroughs in designing novel compounds.
- · Pharmaceuticals
- · Biotechnology
- · Materials Science
- · AI/ML Research
- · Traditional Molecular Modeling Software
- · Brute-force Drug Discovery Methods
More efficient and targeted drug discovery pipelines emerge, reducing R&D costs and time-to-market for new therapies.
The ability to design novel compounds with precise properties could lead to advancements in sustainable materials and energy storage.
Enhanced molecular design capabilities could give rise to entirely new industries focused on custom-designed biomolecules and super-materials.
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Read at arXiv cs.LG