2024/07/26 by Yuchen Shen, Shen, Yuchen, Barnabás Póczos +1 · 1 citation
Biochemistry, Genetics and Molecular Biology · #Advanced biosensing and bioanalysis techniques #Artificial Intelligence (cs.AI) #Biomolecules (q-bio.BM) #Chemical Physics (physics.chem-ph) #DNA and Biological Computing #FOS: Biological sciences #FOS: Computer and information sciences #FOS: Physical sciences #Gene expression and cancer classification #Machine Learning (cs.LG)
paper · pdf · doi:10.48550/arxiv.2407.19039
openalex publication_date 2024/07/26 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28
With the increasing attention to molecular machine learning, various innovations have been made in designing better models or proposing more comprehensive benchmarks. However, less is studied on the data preprocessing schedule for molecular graphs, where a different view of the molecular graph could potentially boost the model's performance. Inspired by the Byte-Pair Encoding (BPE) algorithm, a subword tokenization method popularly adopted in Natural Language Processing, we propose GraphBPE, which tokenizes a molecular graph into different substructures and acts as a preprocessing schedule independent of the model architectures. Our experiments on 3 graph-level classification and 3 graph-level regression datasets show that data preprocessing could boost the performance of models for molecular graphs, and GraphBPE is effective for small classification datasets and it performs on par with other tokenization methods across different model architectures.