Tuning artificial intelligence on the de novo design of natural-product-inspired retinoid X receptor modulators

Communications Chemistry

Volumn 1, Issue 1, 2018, Pages

  Merk, Daniel ^a   Grisoni, Francesca ^a,b   Friedrich, Lukas ^a   Schneider, Gisbert ^a  

^a ETH ZURICH   (Switzerland)

^b UNIVERSITY OF MILANO BICOCCA   (Italy)

Author keywords

[No Author keywords available]

Indexed keywords

EID: 85061969204     PISSN: None     EISSN: 23993669     Source Type: Journal    
DOI: 10.1038/s42004-018-0068-1     Document Type: Article

References (35)

1. Rodrigues, T., Reker, D., Schneider, P. & Schneider, G. Counting on natural products for drug design. Nat. Chem. 8, 531–541 (2016).
2. Chen, Y., De Bruyn Kops, C. & Kirchmair, J. Data resources for the computer-guided discovery of bioactive natural products. J. Chem. Inf. Model. 57, 2099–2111 (2017).
3. Schneider, P. & Schneider, G. Privileged structures revisited. Angew. Chem., Int. Ed. 56, 7971–7974 (2017).
4. Merk, D., Grisoni, F., Friedrich, L., Gelzinyte, E. & Schneider, G. Computer-assisted discovery of retinoid X receptor modulating natural products and isofunctional mimetics. J. Med. Chem. 61, 5442–5447 (2018).
5. Schneider, P. & Schneider, G. De novo design at the edge of chaos. J. Med. Chem. 59, 4077–4086 (2016).
6. Schneider, G., Funatsu, K., Okuno, Y. & Winkler, D. De novo drug design – ye olde scoring problem revisited. Mol. Inf. 36, 1681031 (2017).
7. Hartenfeller, M. & Schneider, G. De novo drug design. Methods Mol. Biol. 672, 299–323 (2010).
8. Proschak, E., Heitel, P., Kalinowsky, L. & Merk, D. Opportunities and challenges for fatty acid mimetics in drug discovery. J. Med. Chem. 60, 5235–5266 (2017).
9. Germain, P. et al. International union of pharmacology. LXIII. Retinoid X receptors. Pharmacol. Rev. 58, 760–772 (2006).
10. Michalik, L. et al. International union of pharmacology. LXI. Peroxisome proliferator-activated receptors. Pharmacol. Rev. 58, 726–741 (2006).
11. Merk, D., Friedrich, L., Grisoni, F. & Schneider, G. De novo design of bioactive small molecules by artificial intelligence. Mol. Inf. 37, 1700153 (2018).
12. Gupta, A. et al. Generative recurrent networks for de novo drug design. Mol. Inf. 37, 1700111 (2018).
13. Weininger, D. SMILES, a chemical language and information system: 1: introduction to methodology and encoding rules. J. Chem. Inf. Comput. Sci. 28, 31–36 (1988).
14. Merk, D., Grisoni, F., Friedrich, L., Gelzinyte, E. & Schneider, G. Scaffold hopping from synthetic RXR modulators by virtual screening and de novo design. Med. Chem. Commun. 9, 1289–1292 (2018).
15. de Lera, A. R., Bourguet, W., Altucci, L. & Gronemeyer, H. Design of selective nuclear receptor modulators: RAR and RXR as a case study. Nat. Rev. Drug Discov. 6, 811–820 (2007).
16. Vaz, B. & de Lera, Á. Advances in drug design with RXR modulators. Expert Opin. Drug Discov. 7, 1003–1016 (2012).
17. Nakashima, K.-I., Murakami, T., Tanabe, H. & Inoue, M. Identification of a naturally occurring retinoid X receptor agonist from Brazilian green propolis. Biochim. Biophys. Acta 1840, 3034–3041 (2014).
18. Kotani, H., Tanabe, H., Mizukami, H., Makishima, M. & Inoue, M. Identification of a naturally occurring rexinoid, honokiol, that activates the retinoid X receptor. J. Nat. Prod. 73, 1332–1336 (2010).
19. Zhang, H. et al. Structure basis of bigelovin as a selective RXR agonist with a distinct binding mode. J. Mol. Biol. 407, 13–20 (2011).
20. Reker, D., Rodrigues, T., Schneider, P. & Schneider, G. Identifying the macromolecular targets of de novo-designed chemical entities through self-organizing map consensus. Proc. Natl Acad. Sci. USA 111, 4067–4072 (2014).
21. Grisoni, F. et al. Scaffold hopping from natural products to synthetic mimetics by holistic molecular similarity. Commun. Chem. 1, 44 (2018).
22. Ertl, P., Roggo, S. & Schuffenhauer, A. Natural product-likeness score and its application for prioritization of compound libraries. J. Chem. Inf. Model. 48, 68–74 (2008).
23. Dictionary of natural products. (Taylor & Francis Group and CRC Press: Boca Raton, FL, U.S. 2011).
24. Lam, P. Y. et al. New aryl/heteroaryl C-N bond cross-coupling reactions via arylboronic acid/cupric acetate arylation. Tetrahedron Lett. 39, 2941–2944 (1998).
25. Schmidt, J. et al. A dual modulator of farnesoid X receptor and soluble epoxide hydrolase to counter nonalcoholic steatohepatitis. J. Med. Chem. 60, 7703–7724 (2017).
26. Flesch, D. et al. Non-acidic farnesoid X receptor modulators. J. Med. Chem. 60, 7199–7205 (2017).
27. Grisoni, F. et al. Matrix-based molecular descriptors for prospective virtual compound screening. Mol. Inf. 36, 1600091 (2017).
28. Carhart, R. E., Smith, D. H. & Venkataraghavan, R. Atom pairs as molecular features in structure-activity studies: definition and applications. J. Chem. Inf. Comput. Sci. 25, 64–73 (1985).
29. Morgan, H. The generation of a unique machine description for chemical structures-a technique developed at chemical abstracts service. J. Chem. Doc. 5, 107–113 (1965).
30. MACCS-II, MDL Information Systems Inc, San Leandro, CA, USA, 1987.
31. Halgren, T. A. Merck molecular force field. I. Basis, form, scope, parameterization, and performance of MMFF94. J. Comput. Chem. 17, 490–519 (1996).
32. Gasteiger, J. & Marsili, M. Iterative partial equalization of orbital electronegativity—a rapid access to atomic charges. Tetrahedron 36, 3219–3228 (1980).
33. Chen, B., Mueller, C. & Willett, P. Combination rules for group fusion in similarity-based virtual screening. Mol. Inf. 29, 533–541 (2010).
34. Reutlinger, M. et al. Chemically advanced template search (CATS) for scaffold-hopping and prospective target prediction for ‘orphan’ molecules. Mol. Inf. 32, 133–138 (2013).
35. Berthold, M. R. et al. KNIME - the Konstanz information miner: version 2.0 and beyond. SIGKDD Explor Newsl. 11, 26–31 (2009).