轻量级分子结构识别系统，每秒处理1520个分子，适合大规模文献挖掘。
MolParser-Mobile: Ultrafast OCSR System for Large-Scale Chemical Literature Mining
- 基于AutoML优化的轻量级端到端框架，仅998万参数。
- 单块RTX 4090D GPU实现每秒1520个分子的识别吞吐量。
- 兼顾速度与精度，适合超大规模化学文献分析场景。
光学化学结构识别（OCSR）是化学文献挖掘的核心，支持分子数据库构建、反应提取及人工智能驱动的科学发现。尽管近年来基于深度学习的方法在识别准确率上取得显著进展，推理吞吐量仍是制约其大规模部署的关键瓶颈。为解决此问题，我们提出MolParser-Mobile，一个由AutoML优化的轻量级端到端OCSR框架。该系统仅含9.98M参数，在单张NVIDIA RTX 4090D GPU上达到每秒1,520个分子的处理速度。尽管设计紧凑，其识别准确率仍具竞争力，且在多个基准测试中表现更优。
原文摘要 · Abstract (English)
Optical Chemical Structure Recognition (OCSR) is a fundamental component of chemical literature mining, enabling molecular database construction, reaction extraction, and AI-driven scientific discovery. Despite substantial progress in recognition accuracy with recent deep learning-based methods, inference throughput remains a critical bottleneck that limits web-scale deployment. To address this challenge, we propose MolParser-Mobile, an AutoML-optimized lightweight end-to-end OCSR framework. MolParser-Mobile contains only 9.98M parameters, while reaching a throughput of 1,520 molecules per second on a single NVIDIA RTX 4090D GPU. Despite its compact design, it maintains competitive and, on several benchmarks, superior recognition accuracy.
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