CoChat
Sign Up
PreprintCited by 9Computational Drug Discovery MethodsBioinformatics and Genomic NetworksProtein Degradation and Inhibitors

The DOI matches its Crossref record; 1 of the 1 references it cites were found in a registry or an index.

LIGHTHOUSE illuminates therapeutics for a variety of diseases including COVID-19

Open access
Shimizu, Hideyuki, Kodama, Manabu, Matsumoto, Masaki, Orba, Yasuko, Sasaki, Michihito, Sato, Akihiko, Sawa, Hirofumi, Nakayama, Keiichi I.·2021·iScience
DOI 10.1101/2021.09.25.461785PMC PMC9549714

Preprint — published as 10.1016/j.isci.2022.105314

Open this paper in CoChat — free accountOpen PDF

Abstract

One of the bottlenecks in the application of basic research findings to patients is the enormous cost, time, and effort required for high-throughput screening of potential drugs for given therapeutic targets. Here we have developed LIGHTHOUSE, a graph-based deep learning approach for discovery of the hidden principles underlying the association of small-molecule compounds with target proteins. Without any 3D structural information for proteins or chemicals, LIGHTHOUSE estimates protein-compound scores that incorporate known evolutionary relations and available experimental data. It identified novel therapeutics for cancer, lifestyle-related disease, and bacterial infection. Moreover, LIGHTHOUSE predicted ethoxzolamide as a therapeutic for coronavirus disease 2019 (COVID-19), and this agent was indeed effective against alpha, beta, gamma, and delta variants of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) that are rampant worldwide. We envision that LIGHTHOUSE will bring about a paradigm shift in translational medicine, providing a bridge from bench side to bedside.

Overview

The claims and statistics of this paper haven’t been checked yet.

Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.

A few free checks a day · papers already verified open instantly · results are public

Integrity signals

Registry

Crossref · 10.1101/2021.09.25.461785

Cites

1 of 1 references verified0 citation errors · 0 not checkable

Statistics

Verification

Claims the paper makes

The claims and statistics of this paper haven’t been checked yet.

Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.

A few free checks a day · papers already verified open instantly · results are public

Self-check

Percentages, sums and reported differences re-computed from the paper’s own numbers.

The self-check needs the paper’s full text.

Citations

Cites

Flagged first, then by citations

Research works printed but not linked: 61

  1. 10.1016/j.xphs.2017.04.051 · DOI
  2. 10.1038/nature03192 · DOI
  3. 2021, Artificial intelligence in drug discovery and development, Drug Discov. Today, 26, 80, 10.1016/j.drudis.2020.10.010 · DOI · Paper page
  4. 10.1016/j.cell.2020.01.021 · DOI · Paper page
  5. 10.1093/bioinformatics/bty593 · DOI
  6. 2019, Compound-protein interaction prediction with end-to-end learning of neural networks for graphs and sequences, Bioinformatics, 35, 309, 10.1093/bioinformatics/bty535 · DOI · Paper page
  7. 2021, DeepPurpose: a deep learning library for drug-target interaction prediction, Bioinformatics, 36, 5545, 10.1093/bioinformatics/btaa1005 · DOI
  8. 2019, Hidden bias in the DUD-E dataset leads to misleading performance of deep learning in structure-based virtual screening, PLoS One, 14, e0220113, 10.1371/journal.pone.0220113 · DOI
  9. 10.1109/34.58871 · DOI · Paper page
  10. Gilmer J. , Schoenholz S.S. , Riley P.F. , Vinyals O. , Dahl G.E. Neural message passing for quantum chemistry. arXiv (2017), 1704.01212 [cs.LG].
  11. Vaswani A. , Shazeer N. , Parmar N. , Uszkoreit J. , Jones L. , Gomez A.N. , Kaiser L. , Polosukhin I. Attention is all you need. arXiv (2017), 1706.03762 [cs.CL].
  12. 1994, The DEF data base of sequence based protein fold class predictions, Nucleic Acids Res, 17, 3616
  13. 10.1093/nar/gkv1277 · DOI
  14. 10.1093/nar/gkw335 · DOI
  15. 2020, Docking-based virtual screening of TβR1 inhibitors: evaluation of pose prediction and scoring functions, BMC Chem, 14, 52, 10.1186/s13065-020-00704-3 · DOI
  16. 2021, Pharmacophore model, docking, QSAR, and molecular dynamics simulation studies of substituted cyclic imides and herbal medicines as COX-2 inhibitors, Heliyon, 7, e06605, 10.1016/j.heliyon.2021.e06605 · DOI
  17. 10.1093/nar/gkv1072 · DOI
  18. Wallach I. , Dzamba M. , Heifets A. AtomNet: A Deep Convolutional Neural Network for Bioactivity Prediction in Structure-based Drug Discovery. arXiv 2015, 1510.02855 [cs.LG].
  19. 10.1021/acs.jcim.6b00740 · DOI
  20. 10.1093/bioinformatics/btv256 · DOI
  21. 10.1016/j.tips.2012.11.001 · DOI
  22. 10.1038/nmeth759 · DOI
  23. 10.1016/S0303-7207(99)00199-9 · DOI
  24. 2019, The discovery and development of liraglutide and semaglutide, Front. Endocrinol. (Lausanne), 10, 155, 10.3389/fendo.2019.00155 · DOI · Paper page
  25. 10.1038/s41573-020-00135-8 · DOI
  26. 2020, A shift in glutamine nitrogen metabolism contributes to the malignant progression of cancer, Nat. Commun, 11, 1320, 10.1038/s41467-020-15136-9 · DOI
  27. 10.1021/acs.jcim.5b00559 · DOI · Paper page
  28. 10.1016/j.jmb.2019.04.002 · DOI
  29. 10.1111/j.1574-6976.2006.00024.x · DOI
  30. 10.1038/nrendo.2017.151 · DOI · Paper page
  31. 2020, F. Dipeptidyl peptidase 4 inhibitors in the treatment of type 2 diabetes mellitus, Nat. Rev. Endocrinol, 16, 642, 10.1038/s41574-020-0399-8 · DOI · Paper page
  32. 2016, Synthesis and biological evaluation of triazole based uracil derivatives as novel DPP-4 inhibitors, Org. Biomol. Chem, 14, 9598, 10.1039/C6OB01818A · DOI
  33. 10.1182/blood-2007-11-123950 · DOI
  34. 2017, Effects of statins on cancer mortality and progression: a systematic review and meta-analysis of 95 cohorts including 1,111,407 individuals, Int. J. Cancer, 140, 1068, 10.1002/ijc.30526 · DOI
  35. 2015, The effect of statins on cancer cells—review, Tumour Biol, 36, 4889, 10.1007/s13277-015-3551-7 · DOI
  36. 10.1016/S1470-2045(14)70119-6 · DOI
  37. 10.1038/nrc.2016.76 · DOI
  38. 2017, The current evidence on statin use and prostate cancer prevention: Are we there yet?, Nat. Rev. Urol, 14, 107, 10.1038/nrurol.2016.199 · DOI
  39. 2018, Assessment of statin-induced interstitial pneumonia in patients treated for hyperlipidemia using a health insurance claims database in Japan, Pulm. Pharmacol. Ther, 50, 88, 10.1016/j.pupt.2018.04.003 · DOI
  40. 10.1016/j.cell.2020.04.026 · DOI · Paper page
  41. 2020, Discovery of SARS-CoV-2 antiviral drugs through large-scale compound repurposing, Nature, 586, 113, 10.1038/s41586-020-2577-1 · DOI
  42. 2021, Transcriptomic profiling of SARS-CoV-2 infected human cell lines identifies HSP90 as target for COVID-19 therapy, iScience, 24, 102151, 10.1016/j.isci.2021.102151 · DOI
  43. 2020, In-hospital use of statins is associated with a reduced risk of mortality among individuals with COVID-19, Cell Metab, 32, 176, 10.1016/j.cmet.2020.06.015 · DOI
  44. 10.1038/s41467-021-21553-1 · DOI
  45. 2021, Carbonic anhydrase inhibitors suppress seizures in a rat model of birth asphyxia, Epilepsia, 62, 1971, 10.1111/epi.16963 · DOI
  46. 2020, Structure-activity relationship of human carbonic anhydrase-II inhibitors: detailed insight for future development as anti-glaucoma agents, Bioorg. Chem, 95, 103557, 10.1016/j.bioorg.2019.103557 · DOI
  47. 2021, Diversity oriented deep reinforcement learning for targeted molecule generation, J. Cheminform, 13, 21, 10.1186/s13321-021-00498-z · DOI
  48. 10.1021/ci970429i · DOI
  49. 10.1038/s41592-021-01100-y · DOI · Paper page
  50. 2021, Tissue-specific enhancer functional networks for associating distal regulatory regions to disease, Cell Syst, 12, 353, 10.1016/j.cels.2021.02.002 · DOI
  51. SMOTE: Synthetic Minority Over-sampling Technique
  52. Survey of Imbalanced Data Methodologies
  53. Foundations of data imbalance and solutions for a data democracy
  54. 2021, Learning the protein language: evolution, structure, and function, Cell Syst, 12, 654, 10.1016/j.cels.2021.05.017 · DOI · Paper page
  55. Influence-Balanced Loss for Imbalanced Visual Classification
  56. 10.1093/nar/gkaa1100 · DOI · Paper page
  57. 10.1093/nar/gkaa970 · DOI
  58. 10.1038/s41467-019-09234-6 · DOI · Paper page
  59. 2020, Artificial intelligence in oncology, Cancer Sci, 111, 1452, 10.1111/cas.14377 · DOI · Paper page
  60. 2018, Target identification and mechanism of action of picolinamide and benzamide chemotypes with antifungal properties, Cell Chem. Biol, 25, 279, 10.1016/j.chembiol.2017.12.007 · DOI
  61. 10.1371/journal.ppat.1009233 · DOI

Metadata

Authors
Shimizu, H., Kodama, M., Matsumoto, M., Orba, Y., Sasaki, M., Sato, A., Sawa, H., & Nakayama, K. I.
Affiliations
  • Tokyo Medical and Dental University (JP)
  • Kyushu University (JP)
  • Niigata University (JP)
  • Hokkaido University (JP)
Corresponding author
  • Shimizu, Hideyuki
  • Nakayama, Keiichi I.
ORCID
Venue
iScience
Publisher
openRxiv
Published
2021-09-25
Volume
25
Issue
11
Pages
105314-105314
ISSN
2589-0042 (ISSN-L)
DOI
10.1101/2021.09.25.461785
PMC
PMC9549714
OpenAlex
W4304083950
Cited by
9
References
1
Citations by year
2026: 2 · 2025: 2 · 2024: 2 · 2023: 2 · 2022: 1
FWCI
0.95
Type
journal article
Language
en
Open access
Licence
cc-by
Locations
  • iScience (publishedVersion, cc-by)
  • bioRxiv (Cold Spring Harbor Laboratory) (acceptedVersion, cc-by-nc-nd)
  • PubMed Central (publishedVersion, cc-by)
  • PubMed (publishedVersion)
  • DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion, cc-by-sa)
Funders
  • Takeda Science Foundation
  • Japan Agency for Medical Research and Development (21wm0425002)
  • Russian Science Foundation
  • Japan Society for the Promotion of Science (22K19288, 20H05930, 18H05215, 21K15068, 20K21386, 23K23870, 21K17856)
Topics
  • Life Sciences › Pharmacology, Toxicology and Pharmaceutics › Drug Discovery › Computational Drug Discovery Methods
  • Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Bioinformatics and Genomic Networks
  • Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Protein Degradation and Inhibitors
Keywords
lighthouses, graph-based deep learning, small molecule drug discovery, COVID-19 treatment, ethoxzolamide, SARS-CoV-2 variants, protein evolution, drug repurposing, cancer therapeutics, bacterial infection, lifestyle-related diseases

About these checks

Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.

Statistics and arithmetic are recomputed, not judged.

Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.

What counts as a claim ›How we measure confidence ›How we check this ›

Are you an author, or do you see a problem with this page? Request a review