SMiCRM: A Benchmark Dataset of Mechanistic Molecular Images

Optical chemical structure recognition (OCSR) systems aim to extract the molecular structure information, usually in the form of molecular graph or SMILES, from images of chemical molecules. While many tools have been developed for this purpose, challenges still exist due to different types of noises that might exist in the images. Specifically, we focus on the “arrow-pushing” diagrams, a typical type of chemical images to demonstrate electron flow in mechanistic steps. We present Structural molecular identifier of Molecular images in Chemical Reaction Mechanisms (SMiCRM), a dataset designed to benchmark machine recognition capabilities of chemical molecules with arrow-pushing annotations. Comprising 453 images, it spans a broad array of organic chemical reactions, each illustrated with molecular structures and mechanistic arrows. SMiCRM offers a rich collection of annotated molecule images for enhancing the benchmarking process for OCSR methods. This dataset includes a machine-readable molecular identity for each image as well as mechanistic arrows showing electron flow during chemical reactions. It presents a more authentic and challenging task for testing molecular recognition technologies, and achieving this task can greatly enrich the mechanisitic information in computer-extracted chemical reaction data.光学化学结构识别（Optical Chemical Structure Recognition, OCSR）系统旨在从化学分子图像中提取分子结构信息，这类信息通常以分子图或SMILES（Simplified Molecular Input Line Entry System，简化分子线性输入规范）的形式呈现。尽管目前已开发出多款面向该任务的工具，但由于图像中可能存在多种类型的噪声，该领域仍存在诸多挑战。具体而言，本数据集聚焦于"推箭头式"示意图——一类用于展示反应机理步骤中电子流动的典型化学图像。我们构建了化学反应机理分子图像结构标识符（SMiCRM）数据集，其专为评估带推箭头标注的化学分子机器识别能力而打造。该数据集共包含453张图像，涵盖了广泛的有机化学反应类型，每张图像均配有分子结构与机理箭头。SMiCRM提供了丰富的带标注分子图像集合，可用于完善OCSR方法的基准测试流程。本数据集为每张图像提供了机器可读的分子标识，同时附带了展示化学反应中电子流动的机理箭头。该任务为分子识别技术的测试提供了更贴近真实场景且更具挑战性的测试场景，完成该任务可极大丰富计算机提取的化学反应数据中的机理信息。