Accelerating drug discovery and repurposing by combining transcriptional signature connectivity with docking
Issued Date
2024-08-30
Resource Type
eISSN
23752548
Scopus ID
2-s2.0-85203083425
Journal Title
Science Advances
Volume
10
Issue
35
Rights Holder(s)
SCOPUS
Bibliographic Citation
Science Advances Vol.10 No.35 (2024)
Suggested Citation
Thorman A.W., Reigle J., Chutipongtanate S., Yang J., Shamsaei B., Pilarczyk M., Fazel-Najafabadi M., Adamczak R., Kouril M., Bhatnagar S., Hummel S., Niu W., Morrow A.L., Czyzyk-Krzeska M.F., McCullumsmith R., Seibel W., Nassar N., Zheng Y., Hildeman D.A., Medvedovic M., Herr A.B., Meller J. Accelerating drug discovery and repurposing by combining transcriptional signature connectivity with docking. Science Advances Vol.10 No.35 (2024). doi:10.1126/sciadv.adj3010 Retrieved from: https://repository.li.mahidol.ac.th/handle/20.500.14594/101164
Title
Accelerating drug discovery and repurposing by combining transcriptional signature connectivity with docking
Author's Affiliation
College of Engineering and Applied Science
Cincinnati Children's Hospital Medical Center
University of Cincinnati
Uniwersytet Mikołaja Kopernika w Toruniu
University of Cincinnati College of Medicine
College of Medicine and Life Sciences
Faculty of Medicine Ramathibodi Hospital, Mahidol University
VA Medical Center
Cincinnati Children's Hospital Medical Center
University of Cincinnati
Uniwersytet Mikołaja Kopernika w Toruniu
University of Cincinnati College of Medicine
College of Medicine and Life Sciences
Faculty of Medicine Ramathibodi Hospital, Mahidol University
VA Medical Center
Corresponding Author(s)
Other Contributor(s)
Abstract
We present an in silico approach for drug discovery, dubbed connectivity enhanced structure activity relationship (ceSAR). Building on the landmark LINCS library of transcriptional signatures of drug-like molecules and gene knockdowns, ceSAR combines cheminformatic techniques with signature concordance analysis to connect small molecules and their targets and further assess their biophysical compatibility using molecular docking. Candidate compounds are first ranked in a target structure–independent manner, using chemical similarity to LINCS analogs that exhibit transcriptomic concordance with a target gene knockdown. Top candidates are subsequently rescored using docking simulations and machine learning–based consensus of the two approaches. Using extensive benchmarking, we show that ceSAR greatly reduces false-positive rates, while cutting run times by multiple orders of magnitude and further democratizing drug discovery pipelines. We further demonstrate the utility of ceSAR by identifying and experimentally validating inhibitors of BCL2A1, an important antiapoptotic target in melanoma and preterm birth–associated inflammation.