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Extending the small-molecule similarity principle to all levels of biology with the Chemical Checker

Nature Biotechnology volume 38, pages 1087–1096 (2020)Cite this article

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A Publisher Correction to this article was published on 21 May 2020

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Abstract

Small molecules are usually compared by their chemical structure, but there is no unified analytic framework for representing and comparing their biological activity. We present the Chemical Checker (CC), which provides processed, harmonized and integrated bioactivity data on ~800,000 small molecules. The CC divides data into five levels of increasing complexity, from the chemical properties of compounds to their clinical outcomes. In between, it includes targets, off-targets, networks and cell-level information, such as omics data, growth inhibition and morphology. Bioactivity data are expressed in a vector format, extending the concept of chemical similarity to similarity between bioactivity signatures. We show how CC signatures can aid drug discovery tasks, including target identification and library characterization. We also demonstrate the discovery of compounds that reverse and mimic biological signatures of disease models and genetic perturbations in cases that could not be addressed using chemical information alone. Overall, the CC signatures facilitate the conversion of bioactivity data to a format that is readily amenable to machine learning methods.

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Fig. 1: CC statistics.
Fig. 2: CC signatures visualized.
Fig. 3: Characterization of compound collections with the CC.
Fig. 4: Signature reversion of Alzheimer’s disease-specific transcriptional profiles.
Fig. 5: Discovery of chemical analogs of biologics.
Fig. 6: Representation of the CCweb resource.

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Data availability

All gene expression signatures have been deposited in the GEO (GSE137202).

Code availability

To facilitate access to our data, we built a web-based resource (https://chemicalchecker.org), which includes all the bioactivity signatures in HDF5 format and the full code of the CC resource.

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References

  1. Sterling, T. & Irwin, J. J. ZINC 15—ligand discovery for everyone. J. Chem. Inform. Model. 55, 2324–2337 (2015).

    Article  CAS  Google Scholar 

  2. Gaulton, A. et al. The ChEMBL database in 2017. Nucleic Acids Res. 45, D945–D954 (2017).

    Article  CAS  PubMed  Google Scholar 

  3. Wang, Y. et al. PubChem BioAssay: 2017 update. Nucleic Acids Res. 45, D955–D963 (2017).

    Article  CAS  PubMed  Google Scholar 

  4. Wishart, D. S. Chapter 3: small molecules and disease. PLOS Comput. Biol. 8, e1002805 (2012).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  5. Duran-Frigola, M., Rossell, D. & Aloy, P. A chemo-centric view of human health and disease. Nature Commun. 5, 5676 (2014).

    Article  CAS  Google Scholar 

  6. Rouillard, A. D. et al. The harmonizome: a collection of processed datasets gathered to serve and mine knowledge about genes and proteins. Database 2016, baw100–baw100 (2016).

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  7. Newman, D. J. & Cragg, G. M. Natural products as sources of new drugs from 1981 to 2014. J. Nat. Prod. 79, 629–661 (2016).

    Article  CAS  PubMed  Google Scholar 

  8. Rodrigues, T., Reker, D., Schneider, P. & Schneider, G. Counting on natural products for drug design. Nat. Chem. 8, 531–541 (2016).

    Article  CAS  PubMed  Google Scholar 

  9. Welsch, M. E., Snyder, S. A. & Stockwell, B. R. Privileged scaffolds for library design and drug discovery. Curr. Opin. Chem. Biol. 14, 347–361 (2010).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  10. Bleicher, K. H., Böhm, H.-J., Müller, K. & Alanine, A. I. Hit and lead generation: beyond high-throughput screening. Nat. Rev. Drug Disc. 2, 369–378 (2003).

    Article  CAS  Google Scholar 

  11. Holbeck, S. L., Collins, J. M. & Doroshow, J. H. Analysis of food and drug administration–approved anticancer agents in the NCI60 panel of human tumor cell lines. Mol. Cancer Therap. 9, 1451–1460 (2010).

    Article  CAS  Google Scholar 

  12. Seashore-Ludlow, B. et al. Harnessing connectivity in a large-scale small-molecule sensitivity dataset. Cancer Discov. 5, 1210–1223 (2015).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  13. Campillos, M., Kuhn, M., Gavin, A.-C., Jensen, L. J. & Bork, P. Drug target identification using side-effect similarity. Science 321, 263–366 (2008).

    Article  CAS  PubMed  Google Scholar 

  14. Petrone, P. M. et al. Rethinking molecular similarity: comparing compounds on the basis of biological activity. ACS Chem. Biol. 7, 1399–1409 (2012).

    Article  CAS  PubMed  Google Scholar 

  15. Papadatos, G., Gaulton, A., Hersey, A. & Overington, J. P. Activity, assay and target data curation and quality in the ChEMBL database. J. Comput. Aided Mol. Des. 29, 885–896 (2015).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  16. Duran-Frigola, M., Mateo, L. & Aloy, P. Drug repositioning beyond the low-hanging fruits. Curr. Opin. Syst. Biol. 3, 95–102 (2017).

    Article  Google Scholar 

  17. Nguyen, D. T. et al. Pharos: collating protein information to shed light on the druggable genome. Nucleic Acids Res. 45, D995–D1002 (2017).

    Article  CAS  PubMed  Google Scholar 

  18. Duran-Frigola, M., Fernandez-Torras, A., Bertoni, M. & Aloy, P. Formatting biological big data for modern machine learning in drug discovery. WIREs Comp. Mol. Sci. 9, e1408 (2018).

    Google Scholar 

  19. Corsello, S. M. et al. The Drug Repurposing Hub: a next-generation drug library and information resource. Nat. Med. 23, 405–408 (2017).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  20. Jokinen, E. & Koivunen, J. P. MEK and PI3K inhibition in solid tumors: rationale and evidence to date. Ther. Adv. Med. Oncol. 7, 170–180 (2015).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  21. Lamb, J. et al. The connectivity map: using gene-expression signatures to connect small molecules, genes, and disease. Science 313, 1929–1935 (2006).

    Article  CAS  PubMed  Google Scholar 

  22. Subramanian, A. et al. A next generation connectivity map: L1000 platform and the first 1,000,000 profiles. Cell 171, 1437–1452 (2017).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  23. Filzen, T. M., Kutchukian, P. S., Hermes, J. D., Li, J. & Tudor, M. Representing high throughput expression profiles via perturbation barcodes reveals compound targets. PLoS Comput. Biol. 13, e1005335 (2017).

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  24. Chen, B. et al. Reversal of cancer gene expression correlates with drug efficacy and reveals therapeutic targets. Nat. Commun. 8, 16022 (2017).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  25. Iorio, F. et al. A landscape of pharmacogenomic interactions in cancer. Cell 166, 740–754 (2016).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  26. Encinas, M. et al. Sequential treatment of SH-SY5Y cells with retinoic acid and brain-derived neurotrophic factor gives rise to fully differentiated, neurotrophic factor-dependent, human neuron-like cells. J. Neurochem. 75, 991–1003 (2000).

    Article  CAS  PubMed  Google Scholar 

  27. Tanzi, R. E. The genetics of Alzheimer disease. Cold Spring Harb. Perspect. Med. 2, a006296 (2012).

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  28. Carvalho-Silva, D. et al. Open Targets Platform: new developments and updates two years on. Nucleic Acids Res. 47, D1056–D1065 (2019).

    Article  CAS  PubMed  Google Scholar 

  29. Perszyk, R. E. et al. GluN2D-containing N-methyl-d-aspartate receptors mediate synaptic transmission in hippocampal interneurons and regulate interneuron activityity. Mol. Pharmacol. 90, 689–702 (2016).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  30. Harold, D. et al. Genome-wide association study identifies variants at CLU and PICALM associated with Alzheimer’s disease. Nat Genet 41, 1088–1093 (2009).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  31. Anselmo, A. C., Gokarn, Y. & Mitragotri, S. Non-invasive delivery strategies for biologics. Nat. Rev. Drug Discov. 18, 19–40 (2018).

    Article  PubMed  CAS  Google Scholar 

  32. Depper, J. M., Leonard, W. J., Robb, R. J., Waldmann, T. A. & Greene, W. C. Blockade of the interleukin-2 receptor by anti-Tac antibody: inhibition of human lymphocyte activation. J. Immunol. 131, 690–696 (1983).

    CAS  PubMed  Google Scholar 

  33. Benson, J. M. et al. Therapeutic targeting of the IL-12/23 pathways: generation and characterization of ustekinumab. Nat. Biotechnol. 29, 615–624 (2011).

    Article  CAS  PubMed  Google Scholar 

  34. Reddy, M. et al. Modulation of CLA, IL-12R, CD40L, and IL-2Ralpha expression and inhibition of IL-12- and IL-23-induced cytokine secretion by CNTO 1275. Cell Immunol. 247, 1–11 (2007).

    Article  CAS  PubMed  Google Scholar 

  35. Xu, M. J., Johnson, D. E. & Grandis, J. R. EGFR-targeted therapies in the post-genomic era. Cancer Metastasis Rev. 36, 463–473 (2017).

    Article  PubMed  PubMed Central  Google Scholar 

  36. Masuelli, L. et al. Apigenin induces apoptosis and impairs head and neck carcinomas EGFR/ErbB2 signaling. Front. Biosci. 16, 1060–1068 (2011).

    Article  CAS  Google Scholar 

  37. Hu, W. J., Liu, J., Zhong, L. K. & Wang, J. Apigenin enhances the antitumor effects of cetuximab in nasopharyngeal carcinoma by inhibiting EGFR signaling. Biomed. Pharmacother. 102, 681–688 (2018).

    Article  CAS  PubMed  Google Scholar 

  38. Sawai, A. et al. Inhibition of Hsp90 down-regulates mutant epidermal growth factor receptor (EGFR) expression and sensitizes EGFR mutant tumors to paclitaxel. Cancer Res. 68, 589–596 (2008).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  39. Williams, A. J. et al. Open PHACTS: semantic interoperability for drug discovery. Drug Disc. Today 17, 1188–1198 (2012).

    Article  Google Scholar 

  40. Rodgers, G. et al. Glimmers in illuminating the druggable genome. Nat. Rev. Drug Disc. 17, 301–302 (2018).

    Article  CAS  Google Scholar 

  41. Wu, Z. et al. MoleculeNet: a benchmark for molecular machine learning. Chem. Sci. 9, 513–530 (2018).

    Article  CAS  PubMed  Google Scholar 

  42. Lee, Y. S. et al. A computational framework for genome-wide characterization of the human disease landscape. Cell Syst. 8, 152–162 (2019).

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  43. Mendez-Lucio, O., Baillif, B., Clevert, D. A., Rouquie, D. & Wichard, J. De novo generation of hit-like molecules from gene expression signatures using artificial intelligence. Nat. Commun. 11, 10 (2020).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  44. Reymond, J.-L. The Chemical Space Project. Acc. Chem. Res. 48, 722–730 (2015).

    Article  CAS  PubMed  Google Scholar 

  45. Irwin, J. J., Gaskins, G., Sterling, T., Mysinger, M. M. & Keiser, M. J. Predicted biological activity of purchasable chemical space. J. Chem. Info. Modeling 58, 148–164 (2018).

    Article  CAS  Google Scholar 

  46. Wang, B. et al. Similarity network fusion for aggregating data types on a genomic scale. Nat Methods 11, 333–337 (2014).

    Article  CAS  PubMed  Google Scholar 

  47. Bickerton, G. R., Paolini, G. V., Besnard, J., Muresan, S. & Hopkins, A. L. Quantifying the chemical beauty of drugs. Nat. Chem. 4, 90–98 (2012).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  48. Axen, S. D. et al. A Sisimple representation of three-dimensional molecular structure. J. Med. Chem. 60, 7393–7409 (2017).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  49. Bemis, G. W. & Murcko, M. A. The properties of known drugs. 1. Molecular frameworks. J. Med. Chem. 39, 2887–2893 (1996).

    Article  CAS  PubMed  Google Scholar 

  50. Durant, J. L., Leland, B. A., Henry, D. R. & Nourse, J. G. Reoptimization of MDL keys for use in drug discovery. J. Chem. Inf. Comput. Sci. 42, 1273–1280 (2002).

    Article  CAS  PubMed  Google Scholar 

  51. Lipinski, C. A. Lead- and drug-like compounds: the rule-of-five revolution. Drug Discov. Today Technol. 1, 337–341 (2004).

    Article  CAS  PubMed  Google Scholar 

  52. Congreve, M., Carr, R., Murray, C. & Jhoti, H. A ‘rule of three’ for fragment-based lead discovery? Drug Discov. Today 8, 876–877 (2003).

    Article  PubMed  Google Scholar 

  53. Wishart, D. S. et al. DrugBank 5.0: a major update to the DrugBank database for 2018. Nucleic Acids Res. 46, D1074–D1082 (2018).

    Article  CAS  PubMed  Google Scholar 

  54. Cheng, H. et al. ECOD: an evolutionary classification of protein domains. PLoS Comput. Biol. 10, e1003926 (2014).

    Article  PubMed  PubMed Central  Google Scholar 

  55. Gilson, M. K. et al. BindingDB in 2015: a public database for medicinal chemistry, computational chemistry and systems pharmacology. Nucleic Acids Res. 44, D1045–D1053 (2016).

    Article  CAS  PubMed  Google Scholar 

  56. Hastings, J. et al. ChEBI in 2016: improved services and an expanding collection of metabolites. Nucleic Acids Res. 44, D1214–D1219 (2016).

    Article  CAS  PubMed  Google Scholar 

  57. Thiele, I. et al. A community-driven global reconstruction of human metabolism. Nat Biotechnol. 31, 419–425 (2013).

    Article  CAS  PubMed  Google Scholar 

  58. Cerami, E. G. et al. Pathway Commons, a web resource for biological pathway data. Nucleic Acids Res. 39, D685–D690 (2011).

    Article  CAS  PubMed  Google Scholar 

  59. Fabregat, A. et al. The Reactome Pathway Knowledgebase. Nucleic Acids Res. 46, D649–D655 (2018).

    Article  CAS  PubMed  Google Scholar 

  60. Pryszcz, L. P., Huerta-Cepas, J. & Gabaldon, T. MetaPhOrs: orthology and paralogy predictions from multiple phylogenetic evidence using a consistency-based confidence score. Nucleic Acids Res. 39, e32 (2011).

    Article  CAS  PubMed  Google Scholar 

  61. Kruger, F. A. & Overington, J. P. Global analysis of small molecule binding to related protein targets. PLoS Comput. Biol. 8, e1002333 (2012).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  62. Zwierzyna, M. & Overington, J. P. Classification and analysis of a large collection of in vivo bioassay descriptions. PLoS Comput. Biol. 13, e1005641 (2017).

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  63. Szklarczyk, D. et al. The STRING database in 2017: quality-controlled protein–protein association networks, made broadly accessible. Nucleic Acids Res. 45, D362–D368 (2017).

    Article  CAS  PubMed  Google Scholar 

  64. Li, T. et al. A scored human protein–protein interaction network to catalyze genomic interpretation. Nat. Methods 14, 61–64 (2017).

    Article  CAS  PubMed  Google Scholar 

  65. Kanehisa, M., Sato, Y., Kawashima, M., Furumichi, M. & Tanabe, M. KEGG as a reference resource for gene and protein annotation. Nucleic Acids Res. 44, D457–D462 (2016).

    Article  CAS  PubMed  Google Scholar 

  66. Kandasamy, K. et al. NetPath: a public resource of curated signal transduction pathways. Genome Biol. 11, R3 (2010).

    PubMed  PubMed Central  Google Scholar 

  67. Mi, H. et al. PANTHER version 11: expanded annotation data from Gene Ontology and Reactome pathways, and data analysis tool enhancements. Nucleic Acids Res. 45, D183–D189 (2017).

    Article  CAS  PubMed  Google Scholar 

  68. Kelder, T. et al. WikiPathways: building research communities on biological pathways. Nucleic Acids Res. 40, D1301–D1307 (2012).

    Article  CAS  PubMed  Google Scholar 

  69. Mosca, R., Ceol, A. & Aloy, P. Interactome3D: adding structural details to protein networks. Nat. Methods 10, 47–53 (2013).

    Article  CAS  PubMed  Google Scholar 

  70. Leiserson, M. D. et al. Pan-cancer network analysis identifies combinations of rare somatic mutations across pathways and protein complexes. Nat. Genet. 47, 106–114 (2015).

    Article  CAS  PubMed  Google Scholar 

  71. Iorio, F. et al. Discovery of drug mode of action and drug repositioning from transcriptional responses. Proc. Natl Acad. Sci. USA 107, 14621–14626 (2010).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  72. Barretina, J. et al. The Cancer Cell Line Encyclopedia enables predictive modelling of anticancer drug sensitivity. Nature 483, 603–607 (2012).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  73. Basu, A. et al. An interactive resource to identify cancer genetic and lineage dependencies targeted by small molecules. Cell 154, 1151–1161 (2013).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  74. Chabner, B. A. NCI-60 cell line screening: a radical departure in its time. J. Natl Cancer Inst. 108, djv388 (2016).

    Article  PubMed  CAS  Google Scholar 

  75. Azur, M. J., Stuart, E. A., Frangakis, C. & Leaf, P. J. Multiple imputation by chained equations: what is it and how does it work? Int. J. Meth. Psychiatr. Res. 20, 40–49 (2011).

    Article  Google Scholar 

  76. Nelson, J. et al. MOSAIC: a chemical-genetic interaction data repository and web resource for exploring chemical modes of action. Bioinformatics 34, 1251–1252 (2017).

    Article  PubMed Central  CAS  Google Scholar 

  77. Wawer, M. J. et al. Toward performance-diverse small-molecule libraries for cell-based phenotypic screening using multiplexed high-dimensional profiling. Proc. Natl Acad. Sci. USA 111, 10911–10916 (2014).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  78. Brown, A. S. & Patel, C. J. A standard database for drug repositioning. Sci. Data 4, 170029 (2017).

    Article  PubMed  PubMed Central  Google Scholar 

  79. Piñero, J. et al. DisGeNET: a comprehensive platform integrating information on human disease-associated genes and variants. Nucleic Acids Res. 45, D833–D839 (2017).

    Article  CAS  PubMed  Google Scholar 

  80. Kuhn, M., Letunic, I., Jensen, L. J. & Bork, P. The SIDER database of drugs and side effects. Nucleic Acids Res. 44, D1075–1079 (2016).

    Article  CAS  PubMed  Google Scholar 

  81. Kuhn, M. et al. Systematic identification of proteins that elicit drug side effects. Mol. Syst. Biol. 9, 663 (2013).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  82. Duran-Frigola, M. & Aloy, P. Analysis of chemical and biological features yields mechanistic insights into drug side effects. Chem. Biol. 20, 594–603 (2013).

    Article  CAS  PubMed  Google Scholar 

  83. Davis, A. P. et al. The Comparative Toxicogenomics Database: update 2017. Nucleic Acids Res. 45, D972–D978 (2017).

    Article  CAS  PubMed  Google Scholar 

  84. Ryu, J. Y., Kim, H. W. & Lee, S. Y. Deep learning improves prediction of drug–drug and drug–food interactions. Proc. Natl Acad. Sci. USA 115, 4304–4311 (2018).

    Article  CAS  Google Scholar 

  85. Grover, A. & Leskovec, J. node2vec: scalable feature learning for networks. Preprint at https://arxiv.org/abs/1607.00653 (2016).

  86. Matsui, Y. O., Yamasaki, K. & Aizawa, T. K PQk-means: billion-scale clustering for product-quantized codes. Preprint at https://arxiv.org/abs/1709.03708 (2017).

  87. Maaten, L. v. d. Barnes–Hut-SNE. Preprint at https://arxiv.org/abs/1301.3342 (2013).

  88. McInnes, L. & Healy, J. Accelerated hierarchical density based clustering. Proc. 2017 IEEE International Conference on Data Mining Workshops (IEEE, 2017).

  89. Webber, W., Moffat, A. & Zobel, J. A similarity measure for indefinite rankings. ACM Trans. Inf. Syst. 28, 1–38 (2010).

    Article  Google Scholar 

  90. Lo, Y. C. et al. Large-scale chemical similarity networks for target profiling of compounds identified in cell-based chemical screens. PLoS Comput. Biol. 11, e1004153 (2015).

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  91. Rennie, J. D. M., Shih, L., Teevan, J. & Karger, D. R. Tackling the poor assumptions of naive Bayes text classifiers. Proc. International Conference on International Conference on Machine Learning 616–623 (AAAI Press, 2003).

  92. Irwin, J. J. & Shoichet, B. K. ZINC–a free database of commercially available compounds for virtual screening. J. Chem. Inf. Model 45, 177–182 (2005).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  93. Fernandez-Torras, A., Duran-Frigola, M. & Aloy, P. Encircling the regions of the pharmacogenomic landscape that determine drug response. Genome Med. 11, 17 (2019).

  94. Badia, R. et al. SAMHD1 is active in cycling cells permissive to HIV-1 infection. Antiviral Res. 142, 123–135 (2017).

    Article  CAS  PubMed  Google Scholar 

  95. Saxena, V., Orgill, D. & Kohane, I. Absolute enrichment: gene set enrichment analysis for homeostatic systems. Nucleic Acids Res. 34, e151 (2006).

    Article  PubMed  PubMed Central  CAS  Google Scholar 

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Acknowledgements

We thank the SB&NB laboratory members for their support and helpful discussions. We are grateful to the Broad Institute and National Center for Advancing Translational Sciences (NCATS-NIH) for providing compounds on request, and J. Duran-Frigola for the website design. We also thank the IRB Barcelona Biostatistics and Bioinformatics Unit and the IRB Functional Genomics Facility. P.A. acknowledges the support of the Spanish Ministerio de Economía y Competitividad (grant no. BIO2016-77038-R), the INB/ELIXIR-ES (grant no. PT17/0009/0007), the European Research Council (SysPharmAD, grant no. 614944) and ‘La Caixa’ BioMedTec (grant no. CTEC_15).

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  1. These authors contributed equally: Miquel Duran-Frigola, Eduardo Pauls.

Authors and Affiliations

  1. Joint IRB-BSC-CRG Program in Computational Biology, Institute for Research in Biomedicine (IRB Barcelona), The Barcelona Institute of Science and Technology, Barcelona, Spain

    Miquel Duran-Frigola, Eduardo Pauls, Oriol Guitart-Pla, Martino Bertoni, Víctor Alcalde, David Amat, Teresa Juan-Blanco & Patrick Aloy

  2. Institució Catalana de Recerca i Estudis Avançats (ICREA), Barcelona, Spain

    Patrick Aloy

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Contributions

M.D.-F., E.P. and P.A. designed the study, analyzed the results and wrote the manuscript. M.D.-F. did the computational analysis, together with M.B., T.J.-B. and D.A. O.G.-P. implemented the web server. E.P. and V.A. carried out the experimental validations. All authors have read and approved the manuscript.

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Correspondence to Miquel Duran-Frigola or Patrick Aloy.

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Supplementary Information

Supplementary Data 1 and 2 legends, Supplementary Figs. 1–17 and Supplementary Tables 1–3.

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Supplementary Data 1

Reversion of transcriptional signatures of fAD mutations.

Supplementary Data 2

Small-molecule analogs of biologics.

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Duran-Frigola, M., Pauls, E., Guitart-Pla, O. et al. Extending the small-molecule similarity principle to all levels of biology with the Chemical Checker. Nat Biotechnol 38, 1087–1096 (2020). https://doi.org/10.1038/s41587-020-0502-7

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