MolParser Datasets

Dataset Examples

Dataset Subsets

Benchmarks

Key Contribution

Introduces MolParser-7M, the largest open-source Optical Chemical Structure Recognition (OCSR) dataset, uniquely combining diverse synthetic data with a large volume of manually-annotated, “in-the-wild” images from real scientific documents to improve model robustness. Also introduces WildMol, a new challenging benchmark for evaluating OCSR performance on real-world data, including Markush structures.

Overview

The MolParser project addresses the challenge of recognizing molecular structures from images found in real-world scientific documents. Unlike existing OCSR datasets that rely primarily on synthetically generated images, MolParser-7M incorporates 400,000 manually annotated images cropped from actual patents and scientific papers, making it the first large-scale dataset to bridge the gap between synthetic training data and real-world deployment scenarios.

Strengths

• Largest open-source OCSR dataset with over 7.7 million pairs
• The only large-scale OCSR training set that includes a significant amount (400k) of “in-the-wild” data cropped from real patents and literature
• High diversity of molecular structures from numerous sources (PubChem, ChEMBL, polymers, etc.)
• Introduces the WildMol benchmark for evaluating performance on challenging, real-world data, including Markush structures
• The “in-the-wild” fine-tuning data (MolParser-SFT-400k) was curated via an efficient active learning data engine with human-in-the-loop validation

Limitations

• The E-SMILES format cannot represent certain complex cases, such as coordination bonds, dashed abstract rings, Markush structures depicted with special patterns, and replication of long structural segments on the skeleton
• The model and data do not yet fully exploit molecular chirality, which is critical for chemical properties
• Performance could be further improved by scaling up the amount of real annotated training data

Technical Notes

Synthetic Data Generation

To ensure diversity, molecular structures were collected from databases like ChEMBL, PubChem, and Kaggle BMS. A significant number of Markush, polymer, and fused-ring structures were also randomly generated. Images were rendered using RDKit and epam.indigo with randomized parameters (e.g., bond width, font size, rotation) to increase visual diversity. The pretraining dataset is composed of the following subsets:

In-the-Wild Data Engine (MolParser-SFT-400k)

A YOLO11 object detection model (MolDet) located and cropped over 20 million molecule images from 1.22 million real PDFs (patents and papers). After de-duplication via p-hash similarity, 4 million unique images remained.

An active learning algorithm was used to select the most informative samples for annotation, targeting images where an ensemble of 5-fold models showed moderate confidence (0.6-0.9 Tanimoto similarity), indicating they were challenging but learnable.

This active learning approach with model pre-annotations reduced manual annotation time per molecule to 30 seconds, approximately 90% savings compared to annotating from scratch. In the final fine-tuning dataset, 56.04% of annotations directly utilized raw model pre-annotations, 20.97% passed review after a single manual correction, 13.87% were accepted after a second round of annotation, and 9.13% required three or more rounds.

The fine-tuning dataset is composed of:

E-SMILES Specification

To accommodate complex patent structures that standard SMILES cannot support, the authors introduced an Extended SMILES format (SMILES<sep>EXTENSION). The EXTENSION component uses XML-like tokens to manage complexities:

• <a>...</a> encapsulates Markush R-groups and abbreviation groups.
• <r>...</r> denotes ring attachments with uncertainty positions.
• <c>...</c> defines abstract rings.
• <dum> identifies a connection point.

This format enables Markush-molecule matching and LLM integration, while retaining RDKit compatibility for the standard SMILES portion.

Reproducibility

The dataset is publicly available on HuggingFace under a CC-BY-NC-SA-4.0 (non-commercial) license. The MolParser-SFT-400k subset is only partially released. The YOLO11-based MolDet detection model is also available on HuggingFace. No public code repository is provided for the MolParser recognition model itself. All experiments were conducted on 8 NVIDIA RTX 4090D GPUs, and throughput benchmarks were measured on a single RTX 4090D GPU.