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

Darby Tien Hao Chang, Chao Hsuan Ke, Jung Hsin Lin, Jung Hsien Chiang

Research output: Contribution to journalArticlepeer-review

8 Citations (Scopus)


Motivation: Determination of the binding affinity of a protein-ligand complex is important to quantitatively specify whether a particular small molecule will bind to the target protein. Besides, collection of comprehensive datasets for protein-ligand complexes and their corresponding binding affinities is crucial in developing accurate scoring functions for the prediction of the binding affinities of previously unknown protein-ligand complexes. In the past decades, several databases of protein-ligand-binding affinities have been created via visual extraction from literature. However, such approaches are time-consuming and most of these databases are updated only a few times per year. Hence, there is an immediate demand for an automatic extraction method with high precision for binding affinity collection.Result: We have created a new database of protein-ligand-binding affinity data, AutoBind, based on automatic information retrieval. We first compiled a collection of 1586 articles where the binding affinities have been marked manually. Based on this annotated collection, we designed four sentence patterns that are used to scan full-text articles as well as a scoring function to rank the sentences that match our patterns. The proposed sentence patterns can effectively identify the binding affinities in full-text articles. Our assessment shows that AutoBind achieved 84.22% precision and 79.07% recall on the testing corpus. Currently, 13 616 protein-ligand complexes and the corresponding binding affinities have been deposited in AutoBind from 17 221 articles.

Original languageEnglish
Article numberbts367
Pages (from-to)2162-2168
Number of pages7
Issue number16
Publication statusPublished - 2012 Aug

All Science Journal Classification (ASJC) codes

  • Statistics and Probability
  • Biochemistry
  • Molecular Biology
  • Computer Science Applications
  • Computational Theory and Mathematics
  • Computational Mathematics


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