BEAR: A Novel Virtual Screening Method Based on Large-Scale Bioactivity Data

Yeajee Kwon, Sera Park, Jaeok Lee, Jiyeon Kang, Hwa Jeong Lee, Wankyu Kim

Research output: Contribution to journalArticlepeer-review

3 Scopus citations


Data-driven drug discovery exploits a comprehensive set of big data to provide an efficient path for the development of new drugs. Currently, publicly available bioassay data sets provide extensive information regarding the bioactivity profiles of millions of compounds. Using these large-scale drug screening data sets, we developed a novel in silico method to virtually screen hit compounds against protein targets, named BEAR (Bioactive compound Enrichment by Assay Repositioning). The underlying idea of BEAR is to reuse bioassay data for predicting hit compounds for targets other than their originally intended purposes, i.e., “assay repositioning”. The BEAR approach differs from conventional virtual screening methods in that (1) it relies solely on bioactivity data and requires no physicochemical features of either the target or ligand. (2) Accordingly, structurally diverse candidates are predicted, allowing for scaffold hopping. (3) BEAR shows stable performance across diverse target classes, suggesting its general applicability. Large-scale cross-validation of more than a thousand targets showed that BEAR accurately predicted known ligands (median area under the curve = 0.87), proving that BEAR maintained a robust performance even in the validation set with additional constraints. In addition, a comparative analysis demonstrated that BEAR outperformed other machine learning models, including a recent deep learning model for ABC transporter family targets. We predicted P-gp and BCRP dual inhibitors using the BEAR approach and validated the predicted candidates using in vitro assays. The intracellular accumulation effects of mitoxantrone, a well-known P-gp/BCRP dual substrate for cancer treatment, confirmed nine out of 72 dual inhibitor candidates preselected by primary cytotoxicity screening. Consequently, these nine hits are novel and potent dual inhibitors for both P-gp and BCRP, solely predicted by bioactivity profiles without relying on any structural information of targets or ligands.

Original languageEnglish
Pages (from-to)1429-1437
Number of pages9
JournalJournal of Chemical Information and Modeling
Issue number5
StatePublished - 13 Mar 2023

Bibliographical note

Funding Information:
W.K., Y.K., and S.P. were supported by the National Research Foundation (NRF-2021M3H9A2098572) of Korea. H.J.L. and J.K. were supported by National Research Foundation of Korea (NRF) grants funded by the Korean government (2020R1A2B5B01002489), and H.J.L. and J.L. were supported by Korea Basic Science Institute (National research Facilities and Equipment Center) grant by the Ministry of Education (2021R1A6C101A442). We thank Prof. Marilyn E. Morris (University of Buffalo, NY, USA) for providing the MCF-7/ADR and MCF-7/MX100 cell lines.

Publisher Copyright:
© 2023 American Chemical Society.


Dive into the research topics of 'BEAR: A Novel Virtual Screening Method Based on Large-Scale Bioactivity Data'. Together they form a unique fingerprint.

Cite this