MolParser-Mobile: Ultrafast OCSR System for Large-Scale Chemical Literature Mining

2026-09-05

Abstract

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.

Problem

• Optical Chemical Structure Recognition
• inference throughput
• web-scale deployment

Innovation

• AutoML-optimized
• lightweight
• end-to-end OCSR
• high throughput
• competitive accuracy