Nanoscale synthesis and affinity ranking

  • 1.

    Reymond, J. L. The chemical space project. Acc. Chem. Res. 48, 722–730 (2015).

  • 2.

    Murray, P. M., Tyler, S. N. G. Moseley, J. D. Beyond the numbers: charting chemical reaction space. Org. Process Res. Dev . 17, 40–46 (2013).

  • 3.

    Buitrago Santanilla, A. et al. Nanomole-scale high-throughput chemistry for the synthesis of complex molecules. Science 347, 49–53 (2015).

  • 4.

    Shevlin, M. Practical high-throughput experimentation for chemists. ACS Med. Chem. Lett. 8, 601–607 (2017).

  • 5.

    Collins, K. D., Gensch, T. Glorius, F. Contemporary screening approaches to reaction discovery and development. Nat. Chem. 6, 859–871 (2014).

  • 6.

    Troshin, K. Hartwig, J. F. Snap deconvolution: an informatics approach to high-throughput discovery of catalytic reactions. Science 357, 175–181 (2017).

  • 7.

    Kutchukian, P. S. et al. Chemistry informer libraries: a chemoinformatics enabled approach to evaluate and advance synthetic methods. Chem. Sci. 7, 2604–2613 (2016).

  • 8.

    Werner, M. et al. Seamless integration of dose-response screening and flow chemistry: efficient generation of structure–activity relationship data of β-secretase (BACE1) inhibitors. Angew. Chem. Int. Ed. 53, 1704–1708 (2014).

  • 9.

    Desai, B. et al. Rapid discovery of a novel series of Abl kinase inhibitors by application of an integrated microfluidic synthesis and screening platform. J. Med. Chem. 56, 3033–3047 (2013).

  • 10.

    Guetzoyan, L., Nikbin, N., Baxendale, I. R. Ley, S. V. Flow chemistry synthesis of zolpidem, alpidem and other GABAA agonists and their biological evaluation through the use of in-line frontal affinity chromatography. Chem. Sci. 4, 764–769 (2013).

  • 11.

    Karageorgis, G., Dow, M., Aimon, A., Warriner, S. Nelson, A. Activity-directed synthesis with intermolecular reactions: development of a fragment into a range of androgen receptor agonists. Angew. Chem. Int. Ed. 54, 13538–13544 (2015).

  • 12.

    Murray, J. B., Roughley, S. D., Matassova, N. Brough, P. A. Off-rate screening (ORS) by surface plasmon resonance. An efficient method to kinetically sample hit to lead chemical space from unpurified reaction products. J. Med. Chem. 57, 2845–2850 (2014).

  • 13.

    Baranczak, A. et al. Integrated platform for expedited synthesis–purification–testing of small molecule libraries. ACS Med. Chem. Lett. 8, 461–465 (2017).

  • 14.

    Price, A. K., MacConnell, A. B. Paegel, B. M. hνSABR: photochemical dose–response bead screening in droplets. Anal. Chem. 88, 2904–2911 (2016).

  • 15.

    Vastl, J., Wang, T., Trinh, T. B. Spiegel, D. A. Encoded silicon-chip-based platform for combinatorial synthesis and screening. ACS Comb. Sci. 19, 255–261 (2017).

  • 16.

    Goodnow, R. A. Jr, Dumelin, C. E. Keefe, A. D. DNA-encoded chemistry: enabling the deeper sampling of chemical space. Nat. Rev. Drug Discov. 16, 131–147 (2017).

  • 17.

    Annis, D. A. et al. An affinity selection–mass spectrometry method for the identification of small molecule ligands from self-encoded combinatorial libraries. Discovery of a novel antagonist of E. coli dihydrofolate reductase. Int. J. Mass Spectrom. 238, 77–83 (2004).

  • 18.

    Andrews, C. L., Ziebell, M. R., Nickbarg, E. Yang, X. in Protein and Peptide Mass Spectro
    metry in
    Drug Discovery (eds Gross, M. L. et al.) 253−286 (John Wiley Sons, Hoboken, 2012).

  • 19.

    O’Connell, T. N., Ramsay, J., Rieth, S. F., Shapiro, M. J. Stroh, J. G. Solution-based indirect affinity selection mass spectrometry – a general tool for high-throughput screening of pharmaceutical compound libraries. Anal. Chem. 86, 7413–7420 (2014).

  • 20.

    Annis, D. A. et al. A general technique to rank protein-ligand binding affinities and determine allosteric versus direct binding site competition in compound mixtures. J. Am. Chem. Soc. 126, 15495–15503 (2004).

  • 21.

    Cuozzo, J. W. et al. Discovery of a potent BTK inhibitor with a novel binding mode by using parallel selections with a DNA-encoded chemical library. ChemBioChem 18, 864–871 (2017).

  • 22.

    Schneider, M. et al. Big data from pharmaceutical patents: a computational analysis of medicinal chemists’ bread and butter. J. Med. Chem. 59, 4385–4402 (2016).

  • 23.

    Brown, D. G. Boström, J. Analysis of past and present synthetic methodologies on medicinal chemistry: where have all the new reactions gone? J. Med. Chem. 59, 4443–4458 (2016).

  • 24.

    Aronov, A. M. et al. Flipped out: structure-guided design of selective pyrazolylpyrrole ERK inhibitors. J. Med. Chem. 50, 1280–1287 (2007).

  • 25.

    Bruno, N. C., Tudge, M. T. Buchwald, S. L. Design and preparation of new palladium precatalysts for C–C and C–N cross-coupling reactions. Chem. Sci. 4, 916–920 (2013).

  • 26.

    Anderson, D. R. et al. Pyrrolopyridine inhibitors of mitogen-activated protein kinase-activated protein kinase 2 (MK-2). J. Med. Chem. 50, 2647–2654 (2007).

  • 27.

    Huang, X. et al. Structure-based design and optimization of 2-aminothiazole-4-carboxamide as a new class of CHK1 inhibitors. Bioorg. Med. Chem. Lett. 23, 2590–2594 (2013).

  • 28.

    Buitrago Santanilla, A. et al. P2Et phosphazene: a mild, functional group tolerant base for soluble, room temperature Pd-catalyzed C–N, C–O, and C–C cross-coupling reactions. Org. Lett. 17, 3370–3373 (2015).

  • 29.

    Schneider, P. Schneider, G. De novo design at the edge of chaos. J. Med. Chem. 59, 4077–4086 (2016).

  • 30.

    Ahneman, D. T., Estrada, J. G., Lin, S., Dreher, S. D. Doyle, A. G. Predicting reaction performance in C–N cross coupling using machine learning. Science 360, 186–190 (2018).

  • Leave a Reply

    Your email address will not be published. Required fields are marked *