Most drugs are developed through iterative rounds of chemical synthesis and biochemical testing to optimize the affinity of a particular compound for a protein target of therapeutic interest. This process is challenging because candidate molecules must be selected from a chemical space of more than 1060 drug-like possibilities1, and a single reaction used to synthesize each molecule has more than 107 plausible permutations of catalysts, ligands, additives and other parameters2. The merger of a method for high-throughput chemical synthesis with a biochemical assay would facilitate the exploration of this enormous search space and streamline the hunt for new drugs and chemical probes. Miniaturized high-throughput chemical synthesis3,4,5,6,7 has enabled rapid evaluation of reaction space, but so far the merger of such syntheses with bioassays has been achieved with only low-density reaction arrays, which analyse only a handful of analogues prepared under a single reaction condition8,9,10,11,12,13. High-density chemical synthesis approaches that have been coupled to bioassays, including on-bead14, on-surface15, on-DNA16 and mass-encoding technologies17, greatly reduce material requirements, but they require the covalent linkage of substrates to a potentially reactive support, must be performed under high dilution and must operate in a mixture format. These reaction attributes limit the application of transition-metal catalysts, which are easily poisoned by the many functional groups present in a complex mixture, and of transformations for which the kinetics require a high concentration of reactant. Here we couple high-throughput nanomole-scale synthesis with a label-free affinity-selection mass spectrometry bioassay. Each reaction is performed at a 0.1-molar concentration in a discrete well to enable transition-metal catalysis while consuming less than 0.05 milligrams of substrate per reaction. The affinity-selection mass spectrometry bioassay is then used to rank the affinity of the reaction products to target proteins, removing the need for time-intensive reaction purification. This method enables the primary synthesis and testing steps that are critical to the invention of protein inhibitors to be performed rapidly and with minimal consumption of starting materials.
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A deep learning framework for accurate reaction prediction and its application on high-throughput experimentation data
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We are grateful to L. Nogle, D. Smith and M. Pietrafitta (MSD) for assistance with compound purification, S. Bano and X. Bu (MSD) for measuring residual palladium concentrations, A. M. Norris (MSD) for assistance with graphics and Z. Gu (DFKZ German Cancer Research Center) for suggestions for data visualization. N.J.G. was supported by an MRL Postdoctoral Research Fellowship.
Nature thanks J. Janey, D. Young and the other anonymous reviewer(s) for their contribution to the peer review of this work.
The authors declare no competing interests.
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Extended data figures and tables
a, Reaction solutions are dosed using a TTP mosquito liquid-handling robot that handles increments of 20 nl. b, c, UPLC–MS analysis confirms the presence of products from crude reactions (b), which is visualized in heat maps (c). d, For the ASMS assay, productive reactions are mass-encoded, pooled and incubated with a protein of interest. e, f, Elution through a size-exclusion column separates protein-bound compounds from unbound compounds (e), and binders can be observed by high-performance liquid chromatography–mass spectrometry (HPLC–MS) following denaturation of the protein–ligand complex (f). g, Decreasing the concentration of protein in the ASMS assay increases the competition for binding so that ligands can be categorized as high, medium or low affinity. h, Scale-up of compounds and purification by using a traditional approach. i, Measurement of IC50 in a functional assay enables comparison of the new method to existing assay technology. See Supplementary Information for additional details.
a, A library of 19 unique ERK2 inhibitors (2, 3, A1–A17) was produced by coupling 1 to diverse amines under a single reaction condition (HATU, iPr2NEt, NMP; see Fig. 2a). b, Crude nanoscale reactions were submitted to the ASMS assay, with affinity ranking determined by reducing the concentration of ERK2 to increase the competition for binding. The lowest ERK2 concentration at which the mass signal of the compound was still observed is shown on the abscissa. The Ki value reported24 from purified product samples generated on a 500-times-larger reaction scale is shown on the ordinate. The colour shading groups compounds into low affinity (purple), modest affinity (magenta) or high affinity (yellow), as determined by the ASMS assay. rxn, reaction. c, Comparison of the results of the ASMS affinity-ranking assay (abscissa) to the reported ERK2 Ki values (ordinate). Both datasets are from purified product samples produced on a 50-μmol scale. d, Comparison of results of ASMS affinity ranking from crude reaction mixtures generated on a 100-nmol scale with those from purified product samples generated on a 50-μmol scale. Points are coloured by Ki, and jittering was applied to this categorical data to reveal overlapping data. e, Product structures with reaction conditions used, isolated yields and Ki values. See Supplementary Information for additional details.
Extended Data Fig. 5 ASMS comparison plots for different reaction workup protocols and residual levels of palladium from the CHK1 library.
Reactions with different workup protocols were submitted to the ASMS assay, with affinity ranking determined by reducing the concentration of CHK1 to increase the competition for binding. The axes of the plots are as in Extended Data Fig. 4b, c. The reaction and workup protocols are as follows: a, nanoscale reactions submitted to the affinity-ranking assay with no purification; b, nanoscale reactions submitted to the affinity-ranking assay following treatment with SiliaMetS dimercaptotriazine (DMT) resin; and c, 50-mmol-scale reactions in which the product samples were purified by reverse-phase HPLC before submission to the affinity-ranking assay. See Supplementary Information for additional details.
See Fig. 3. a, Diverse model nucleophiles (D1–D17) were screened against combinations of catalysts (11–26) and bases (27–34) in NMP, DMSO or DMF solvent. b, The details beside the heat maps show the mapping of nucleophiles, solvents, catalysts and bases. NA, no catalyst used. Reactions were run on a 100-nmol scale and analysed by UPLC–MS for conversion to products of the form of 10 compared to a biphenyl internal standard. Productive reaction conditions from this screen were selected for use in the subsequent library synthesis campaign. aD1 is compound C2 in Fig. 2c; bD2 is compound C3 in Fig. 2c and 43 in Fig. 4; cD3 is compound C5 in Fig. 2c; dD4 is compound C6 in Fig. 2c; eD5 is compound 8 in Fig. 2c; fprepared from the boronic acid. See Supplementary Information for additional details.
See Fig. 3. Structures for 62 diverse coupling products (of the form of 10) are shown, which were selected from the 345 productive reactions identified in a library synthesis campaign targeting 384 products (Fig. 3). Diverse nucleophiles were coupled to 7 using the four reaction conditions selected in Fig. 3 and Extended Data Fig. 6. See Supplementary Information for additional details.
Extended Data Fig. 8 Comparison of the results of affinity ranking with IC50 values for inhibition of CHK1 functional activity.
The 62 exemplary compounds (Extended Data Fig. 6) were selected from 345 that were submitted to affinity ranking (Fig. 3). Crude nanoscale reactions were submitted to the ASMS assay, with affinity ranking determined by reducing the concentration of CHK1 to increase the competition for binding. The axes of the plots are as in Extended Data Fig. 4b, c. Points are coloured by the IC50 value for inhibition of CHK1 function (as in Extended Data Fig. 4d). See Supplementary Information for additional details.
The compounds shown are 35–41 and 43. The inhibition of CHK1 functional activity by diverse coupling products (10) from the reaction of 7 with nucleophiles under various reaction conditions is shown. Isolated yields were achieved when the reaction conditions shown were used. The dose response curves and IC50 values shown were measured on purified product samples generated on a 50-μmol scale and could be predicted from affinity-ranking results of crude nanoscale reactions as shown in Extended Data Fig. 8. See Supplementary Information for dose response curves of additional compounds.
a, Histogram of reaction performance for 384 diverse coupling reactions with bromide 7 using a single reaction condition (10 mol% tBuXPhos Pd G3, P2Et, NMP). Only 158 of the 384 targeted products were observed by mass spectrometry and the majority of the reactions failed (0% conversion to product). b, Histogram of reaction performance for 384 diverse coupling reactions with bromide 7 using the best of four reaction conditions, as described in Extended Data Figs. 6 and 7. Of the 384 targeted products, 345 were observed by mass spectrometry, with a more even distribution of reaction yields and the majority of reactions succeeding (100% conversion to product). c, Principal-component (PC) analysis of chemical ‘dark’ space, with each dot representing a compound that was not formed under a single reaction condition (0% yield) but that had affinity to CHK1. By using the best of four reaction conditions, 187 additional products were produced and assayed that bound to CHK1; the colour of the dots reflects the affinity ranking of the compounds. The boundaries of this space are identical to those depicted in Fig. 4a, in which purple shading highlights additional regions where the majority of reactions failed (0% yield). Areas where dots are shown in purple shaded regions depict products with affinity to CHK1 that were formed in 0% yield in a but in more than 1% yield in b. d, Potent CHK1 inhibitors that were produced in 0% yield under a single condition (10 mol% tBuXPhos Pd G3, P2Et, NMP), but in modest to good yields following reaction-condition screening.
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Gesmundo, N.J., Sauvagnat, B., Curran, P. et al. Nanoscale synthesis and affinity ranking. Nature 557, 228–232 (2018). https://doi.org/10.1038/s41586-018-0056-8
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