BioChemInsight: An Open-Source Toolkit for Automated Identification and Recognition of Optical Chemical Structures and Activity Data in Scientific Publications

• URL: http://arxiv.org/abs/2504.10525v1
• Date: Sat, 12 Apr 2025 04:56:44 GMT
• Title: BioChemInsight: An Open-Source Toolkit for Automated Identification and Recognition of Optical Chemical Structures and Activity Data in Scientific Publications
• Authors: Zhe Wang, Fangtian Fu, Wei Zhang, Lige Yan, Yan Meng, Jianping Wu, Hui Wu, Gang Xu, Si Chen,
• Abstract summary: Existing optical chemical structure recognition tools fail to autonomously associate molecular structures with their bioactivity profiles.<n>BioChemInsight is an open-source pipeline that integrates DECIMER and MolVec for chemical structure recognition, Qwen2.5-VL-32B for compound identifier association, and PaddleOCR for bioactivity extraction and unit normalization.<n>System generates ready-to-use SAR datasets, reducing data preprocessing time from weeks to hours.
• Score: 25.764592266678132
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Automated extraction of chemical structures and their bioactivity data is crucial for accelerating drug discovery and enabling data-driven pharmaceutical research. Existing optical chemical structure recognition (OCSR) tools fail to autonomously associate molecular structures with their bioactivity profiles, creating a critical bottleneck in structure-activity relationship (SAR) analysis. Here, we present BioChemInsight, an open-source pipeline that integrates: (1) DECIMER Segmentation and MolVec for chemical structure recognition, (2) Qwen2.5-VL-32B for compound identifier association, and (3) PaddleOCR with Gemini-2.0-flash for bioactivity extraction and unit normalization. We evaluated the performance of BioChemInsight on 25 patents and 17 articles. BioChemInsight achieved 95% accuracy for tabular patent data (structure/identifier recognition), with lower accuracy in non-tabular patents (~80% structures, ~75% identifiers), plus 92.2 % bioactivity extraction accuracy. For articles, it attained >99% identifiers and 78-80% structure accuracy in non-tabular formats, plus 97.4% bioactivity extraction accuracy. The system generates ready-to-use SAR datasets, reducing data preprocessing time from weeks to hours while enabling applications in high-throughput screening and ML-driven drug design (https://github.com/dahuilangda/BioChemInsight).

Related papers

• Chemical knowledge-informed framework for privacy-aware retrosynthesis learning [60.93245342663455]
  Current machine learning-based retrosynthesis gathers reaction data from multiple sources into one single edge to train prediction models. This paradigm poses considerable privacy risks as it necessitates broad data availability across organizational boundaries. In the present study, we introduce the chemical knowledge-informed framework (CKIF), a privacy-preserving approach for learning retrosynthesis models.
  arXiv  Detail & Related papers  (2025-02-26T13:13:24Z)
• Intelligent System for Automated Molecular Patent Infringement Assessment [38.48937966447085]
  PatentFinder is a novel multi-agent and tool-enhanced intelligence system that can accurately and comprehensively evaluate small molecules for patent infringement.<n>PatentFinder features five specialized agents that collaboratively analyze patent claims and molecular structures.<n>PatentFinder autonomously generates detailed and interpretable patent infringement reports, showcasing enhanced accuracy and improved interpretability.
  arXiv  Detail & Related papers  (2024-12-10T12:14:38Z)
• Dumpling GNN: Hybrid GNN Enables Better ADC Payload Activity Prediction Based on Chemical Structure [53.76752789814785]
  DumplingGNN is a hybrid Graph Neural Network architecture specifically designed for predicting ADC payload activity based on chemical structure. We evaluate it on a comprehensive ADC payload dataset focusing on DNA Topoisomerase I inhibitors. It demonstrates exceptional accuracy (91.48%), sensitivity (95.08%), and specificity (97.54%) on our specialized ADC payload dataset.
  arXiv  Detail & Related papers  (2024-09-23T17:11:04Z)
• BatGPT-Chem: A Foundation Large Model For Retrosynthesis Prediction [65.93303145891628]
  BatGPT-Chem is a large language model with 15 billion parameters, tailored for enhanced retrosynthesis prediction. Our model captures a broad spectrum of chemical knowledge, enabling precise prediction of reaction conditions. This development empowers chemists to adeptly address novel compounds, potentially expediting the innovation cycle in drug manufacturing and materials science.
  arXiv  Detail & Related papers  (2024-08-19T05:17:40Z)
• BioBERT-based Deep Learning and Merged ChemProt-DrugProt for Enhanced Biomedical Relation Extraction [2.524192238862961]
  Our approach integrates the ChemProt and DrugProt datasets using a novel merging strategy. The study highlights the potential of automated information extraction in biomedical research and clinical practice.
  arXiv  Detail & Related papers  (2024-05-28T21:34:01Z)
• EnzChemRED, a rich enzyme chemistry relation extraction dataset [3.6124226106001]
  EnzChemRED consists of 1,210 expert curated PubMed abstracts in which enzymes and the chemical reactions they catalyze are annotated. We show that fine-tuning pre-trained language models with EnzChemRED can significantly boost their ability to identify mentions of proteins and chemicals in text. We combine the best performing methods after fine-tuning using EnzChemRED to create an end-to-end pipeline for knowledge extraction from text.
  arXiv  Detail & Related papers  (2024-04-22T14:18:34Z)
• BioT5+: Towards Generalized Biological Understanding with IUPAC Integration and Multi-task Tuning [77.90250740041411]
  This paper introduces BioT5+, an extension of the BioT5 framework, tailored to enhance biological research and drug discovery. BioT5+ incorporates several novel features: integration of IUPAC names for molecular understanding, inclusion of extensive bio-text and molecule data from sources like bioRxiv and PubChem, the multi-task instruction tuning for generality across tasks, and a numerical tokenization technique for improved processing of numerical data.
  arXiv  Detail & Related papers  (2024-02-27T12:43:09Z)
• Bi-level Contrastive Learning for Knowledge-Enhanced Molecule Representations [68.32093648671496]
  We introduce GODE, which accounts for the dual-level structure inherent in molecules.<n> Molecules possess an intrinsic graph structure and simultaneously function as nodes within a broader molecular knowledge graph.<n>By pre-training two GNNs on different graph structures, GODE effectively fuses molecular structures with their corresponding knowledge graph substructures.
  arXiv  Detail & Related papers  (2023-06-02T15:49:45Z)
• Machine Guided Discovery of Novel Carbon Capture Solvents [48.7576911714538]
  Machine learning offers a promising method for reducing the time and resource burdens of materials development. We have developed an end-to-end "discovery cycle" to select new aqueous amines compatible with the commercially viable acid gas scrubbing carbon capture. The prediction process shows 60% accuracy against experiment for both material parameters and 80% for a single parameter on an external test set.
  arXiv  Detail & Related papers  (2023-03-24T18:32:38Z)
• BioRED: A Comprehensive Biomedical Relation Extraction Dataset [6.915371362219944]
  We present BioRED, a first-of-its-kind biomedical RE corpus with multiple entity types and relation pairs. We label each relation as describing either a novel finding or previously known background knowledge, enabling automated algorithms to differentiate between novel and background information. Our results show that while existing approaches can reach high performance on the NER task, there is much room for improvement for the RE task.
  arXiv  Detail & Related papers  (2022-04-08T19:23:49Z)
• AlphaFold Accelerates Artificial Intelligence Powered Drug Discovery: Efficient Discovery of a Novel Cyclin-dependent Kinase 20 (CDK20) Small Molecule Inhibitor [9.89420507558956]
  We successfully applied AlphaFold to identify a first-in-class hit molecule of a novel target without an experimental structure. We identified a small molecule hit compound for CDK20 with a Kd value of 8.9 +/- 1.6 uM within 30 days from target selection and after only 7 compounds. This is the first reported small molecule targeting CDK20 and more importantly, this work is the first demonstration of AlphaFold application in the hit identification process in early drug discovery.
  arXiv  Detail & Related papers  (2022-01-21T07:35:24Z)
• Unassisted Noise Reduction of Chemical Reaction Data Sets [59.127921057012564]
  We propose a machine learning-based, unassisted approach to remove chemically wrong entries from data sets. Our results show an improved prediction quality for models trained on the cleaned and balanced data sets.
  arXiv  Detail & Related papers  (2021-02-02T09:34:34Z)
• Biomedical named entity recognition using BERT in the machine reading comprehension framework [16.320249089801884]
  We propose a new method to implement biomedical named entity recognition (BioNER) Instead of treating the BioNER task as a sequence labeling problem, we formulate it as a machine reading comprehension problem. Our method achieves state-of-the-art (SOTA) performance on the BC4CHEMD, BC5CDR-Chem, BC5CDR-Disease, NCBI-Disease, BC2GM and JNLPBA datasets.
  arXiv  Detail & Related papers  (2020-09-03T10:10:20Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.