ArticleJournal of chemical information and modeling2022
MolMiner: You Only Look Once for Chemical Structure Recognition.
Article in Journal of chemical information and modeling, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 10 papers.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
10 citing papers in PubMed.
- GraphReco: Probabilistic Structure Recognition for Chemical Molecules.ChemistryOpen · 2026Article
- MGTbind: a comprehensive database of molecular glue ternary interactome.Nucleic acids research · 2026Article
- Unveiling optimal molecular features for hERG insights with automatic machine learning.Journal of pharmaceutical analysis · 2025Article
- MolNexTR: a generalized deep learning model for molecular image recognition.Journal of cheminformatics · 2024Article
- Automation and machine learning augmented by large language models in a catalysis study.Chemical science · 2024Review
- PatCID: an open-access dataset of chemical structures in patent documents.Nature communications · 2024Article
- MMSSC-Net: multi-stage sequence cognitive networks for drug molecule recognition.RSC advances · 2024Article
- Automated molecular structure segmentation from documents using ChemSAM.Journal of cheminformatics · 2024Article
- YoDe-Segmentation: automated noise-free retrieval of molecular structures from scientific publications.Journal of cheminformatics · 2023Article
- DECIMER.ai: an open platform for automated optical chemical structure identification, segmentation and recognition in scientific publications.Nature communications · 2023Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
16 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Molecular structures are commonly depicted in 2D printed forms in scientific documents such as journal papers and patents. However, these 2D depictions are not machine readable. Due to a backlog of decades and an increasing amount of printed literatures, there is a high demand for translating printed depictions into machine-readable formats, which is known as Optical Chemical Structure Recognition (OCSR). Most OCSR systems developed over the last three decades use a rule-based approach, which vectorizes the depiction based on the interpretation of vectors and nodes as bonds and atoms. Here, we present a practical software called MolMiner, which is primarily built using deep neural networks originally developed for semantic segmentation and object detection to recognize atom and bond elements from documents. These recognized elements can be easily connected as a molecular graph with a distance-based construction algorithm. MolMiner gave state-of-the-art performance on four benchmark data sets and a self-collected external data set from scientific papers. As MolMiner performed similarly well in real-world OCSR tasks with a user-friendly interface, it is a useful and valuable tool for daily applications. The free download links of Mac and Windows versions are available at https://github.com/iipharma/pharmamind-molminer.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.