AutoBind: automatic extraction of protein–ligand-binding affinity data from biological literature

Chang, Darby Tien Hao; Ke, Chao; Lin, Jiunn-Yuan; Chiang, Jung-Hsien

doi:10.1093/bioinformatics/bts367

Cited by 7 publications

(6 citation statements)

References 39 publications

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“…As a determinant of several ADME properties, lipophilicity (logD7.4) is a key physical property in the development of small molecule oral drugs. This dataset, curated from ChEMBL database, can be applied for method benchmarking in regression modeling, cheminformatics, and chemometrics research. PDBbind-full: The full subsets of PDBbind provide a comprehensive collection of experimental binding affinity data for biomolecular complexes. , The primary reference of each complex was examined to collect experimentally determined binding affinity data ( K d , K i , and IC50) of the given complex. This type of information is the much-needed basis for various computational and statistical studies on molecular recognition.…”

Section: Results and Discussionmentioning

confidence: 99%

Molecule Property Prediction Based on Spatial Graph Embedding

Wang

Jiang

et al. 2019

J. Chem. Inf. Model.

111

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Accurate prediction of molecular properties is important for new compound design, which is a crucial step in drug discovery. In this paper, molecular graph data is utilized for property prediction based on graph convolution neural networks. In addition, a convolution spatial graph embedding layer (C-SGEL) is introduced to retain the spatial connection information on molecules. And, multiple C-SGELs are stacked to construct a convolution spatial graph embedding network (C-SGEN) for end-to-end representation learning. In order to enhance the robustness of the network, molecular fingerprints are also combined with C-SGEN to build a composite model for predicting molecular properties. Our comparative experiments have shown that our method is accurate and achieves the best results on some open benchmark datasets.

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Section: Results and Discussionmentioning

confidence: 99%

Molecule Property Prediction Based on Spatial Graph Embedding

Wang

Jiang

et al. 2019

J. Chem. Inf. Model.

111

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“…Sub-task 2 of the BB Task is related to the general problem of relation extraction. A number of different methods including entity co-occurrence based approaches [ 36 , 37 ] and pattern matching based approaches [ 38 - 40 ] have been developed for extracting relations among biomedical entities including genes, proteins, drugs, and diseases. The state-of-the-art techniques for biomedical relation extraction are in general based on using the syntactic analyses of the sentences, usually in conjunction with machine learning methods [ 41 - 45 ].…”

Section: Related Workmentioning

confidence: 99%

Detection and categorization of bacteria habitats using shallow linguistic analysis

Karadeniz

Özgür

2015

BMC Bioinformatics

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BackgroundInformation regarding bacteria biotopes is important for several research areas including health sciences, microbiology, and food processing and preservation. One of the challenges for scientists in these domains is the huge amount of information buried in the text of electronic resources. Developing methods to automatically extract bacteria habitat relations from the text of these electronic resources is crucial for facilitating research in these areas.MethodsWe introduce a linguistically motivated rule-based approach for recognizing and normalizing names of bacteria habitats in biomedical text by using an ontology. Our approach is based on the shallow syntactic analysis of the text that include sentence segmentation, part-of-speech (POS) tagging, partial parsing, and lemmatization. In addition, we propose two methods for identifying bacteria habitat localization relations. The underlying assumption for the first method is that discourse changes with a new paragraph. Therefore, it operates on a paragraph-basis. The second method performs a more fine-grained analysis of the text and operates on a sentence-basis. We also develop a novel anaphora resolution method for bacteria coreferences and incorporate it with the sentence-based relation extraction approach.ResultsWe participated in the Bacteria Biotope (BB) Task of the BioNLP Shared Task 2013. Our system (Boun) achieved the second best performance with 68% Slot Error Rate (SER) in Sub-task 1 (Entity Detection and Categorization), and ranked third with an F-score of 27% in Sub-task 2 (Localization Event Extraction). This paper reports the system that is implemented for the shared task, including the novel methods developed and the improvements obtained after the official evaluation. The extensions include the expansion of the OntoBiotope ontology using the training set for Sub-task 1, and the novel sentence-based relation extraction method incorporated with anaphora resolution for Sub-task 2. These extensions resulted in promising results for Sub-task 1 with a SER of 68%, and state-of-the-art performance for Sub-task 2 with an F-score of 53%.ConclusionsOur results show that a linguistically-oriented approach based on the shallow syntactic analysis of the text is as effective as machine learning approaches for the detection and ontology-based normalization of habitat entities. Furthermore, the newly developed sentence-based relation extraction system with the anaphora resolution module significantly outperforms the paragraph-based one, as well as the other systems that participated in the BB Shared Task 2013.

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“…The Pocketome initiative is complementary to the binding affinity-centered databases such as PDBbind 7, 8 , Binding MOAD 9 , BindingDB 10 , AutoBind 11 , and shares some similar features with PDBSite 12 , ReliBase 13, 14 , MSDsite 15 , sc-PDB 16 , and LigBase 17 . The unique features of the Pocketome include:

Focus on the binding site; multiple binding sites on a single protein or domain are treated separately.

Complete definition of the binding site composition, including protein chains in a homo- or hetero-multimer, catalytic or structural metal ions, and cofactors binding concurrently with the ligands.

Ensemble nature, capturing the compositional and conformational variability of the pocket.

…”

Section: The Pocketome Encyclopediamentioning

confidence: 99%

Compound Activity Prediction Using Models of Binding Pockets or Ligand Properties in 3D

Kufareva¹,

Chen²,

Ilatovskiy³

et al. 2012

CTMC

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Transient interactions of endogenous and exogenous small molecules with flexible binding sites in proteins or macromolecular assemblies play a critical role in all biological processes. Current advances in high-resolution protein structure determination, database development, and docking methodology make it possible to design three-dimensional models for prediction of such interactions with increasing accuracy and specificity. Using the data collected in the Pocketome encyclopedia, we here provide an overview of two types of the three-dimensional ligand activity models, pocket-based and ligand property-based, for two important classes of proteins, nuclear and G-protein coupled receptors. For half the targets, the pocket models discriminate actives from property matched decoys with acceptable accuracy (the area under ROC curve, AUC, exceeding 84%) and for about one fifth of the targets with high accuracy (AUC > 95%). The 3D ligand property field models performed better than 95% in half of the cases. The high performance models can already become a basis of activity predictions for new chemicals. Family-wide benchmarking of the models highlights strengths of both approaches and helps identify their inherent bottlenecks and challenges.

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AutoBind: automatic extraction of protein–ligand-binding affinity data from biological literature

Cited by 7 publications

References 39 publications

Molecule Property Prediction Based on Spatial Graph Embedding

Molecule Property Prediction Based on Spatial Graph Embedding

Detection and categorization of bacteria habitats using shallow linguistic analysis

Compound Activity Prediction Using Models of Binding Pockets or Ligand Properties in 3D

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