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Ultra-large library docking for discovering new chemotypes

Nature volume 566, pages 224–229 (2019)Cite this article

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Abstract

Despite intense interest in expanding chemical space, libraries containing hundreds-of-millions to billions of diverse molecules have remained inaccessible. Here we investigate structure-based docking of 170 million make-on-demand compounds from 130 well-characterized reactions. The resulting library is diverse, representing over 10.7 million scaffolds that are otherwise unavailable. For each compound in the library, docking against AmpC β-lactamase (AmpC) and the D4 dopamine receptor were simulated. From the top-ranking molecules, 44 and 549 compounds were synthesized and tested for interactions with AmpC and the D4 dopamine receptor, respectively. We found a phenolate inhibitor of AmpC, which revealed a group of inhibitors without known precedent. This molecule was optimized to 77 nM, which places it among the most potent non-covalent AmpC inhibitors known. Crystal structures of this and other AmpC inhibitors confirmed the docking predictions. Against the D4 dopamine receptor, hit rates fell almost monotonically with docking score, and a hit-rate versus score curve predicted that the library contained 453,000 ligands for the D4 dopamine receptor. Of 81 new chemotypes discovered, 30 showed submicromolar activity, including a 180-pM subtype-selective agonist of the D4 dopamine receptor.

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Fig. 1: Make-on-demand compounds are diverse and have increased exponentially.
Fig. 2: Structural fidelity between docked-predicted and crystallographically determined poses of the new β-lactamase inhibitors.
Fig. 3: Testing 549 molecules at different docking ranks against the D4 receptor.
Fig. 4: Estimating the number of active D4 receptor ligands in the 138 million compound library.

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Data availability

Active molecules reported here are available from B.K.S. or directly from Enamine. The four structures of AmpC determined with the new docking hits are available from the PDB with accession numbers 6DPZ, 6DPY, 6DPX and 6DPT. The compounds docked in this study are freely available from our ZINC lead-like make-on-demand library (http://zinc15.docking.org). All active compounds are available either from the authors or may be purchased from Enamine. Figures with associated raw data include: Fig. 2, for which electron density and reflection files are deposited with the PDB; Figs. 3, 4 and Extended Data Fig. 5, for which Source Data are available in the online version of the paper; Extended Data Fig. 1, for which the data are included in Supplementary Table 1; Extended Data Fig. 6, for which raw clustering or no-clustering rank numbers are included in Supplementary Tables 8, 9. Further data are provided in Supplementary Tables 3, 5 (aggregation assays for AmpC inhibitors and D4 ligands); Extended Data Table 1 (crystallographic data collection and refinement); Supplementary Tables 9, 10 and Supplementary Data 12–15 (chemical purity of active ligands and their spectra); Supplementary Data 11 and 14 (synthetic routes to compounds). All other data are available from the authors on request.

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Acknowledgements

This research was supported by GM71896 (to J.J.I.); R35 GM122481 and a UCSF PBBR New Frontier Award (to B.K.S.); R01 MH112205, U24DK1169195 and the NIMH Psychoactive Drug Screening Contract (to B.L.R.); Strategic Priority Research Program of the Chinese Academy of Sciences, grant number XDB19000000 (to S.W.). We thank R. Stein and I. Fish for help with AmpC preparation, H. Torosyan for aggregation assays, R. H. J. Olsen for developing the D4 receptor BRET assay, B. Wong and C. Dandarchuluun for computer support, and M. Korczynska and J. Pottel for reading this manuscript; ChemAxon for a license to JChem, OpenEye Scientific software for a license to OEChem and Omega2, Molecular Networks for a license to Corina, and Molinspriation for a license to Mitools.

Reviewer information

Nature thanks M. M. Babu, D. E. Gloriam and the other anonymous reviewer(s) for their contribution to the peer review of this work.

Author information

Author notes

  1. These authors contributed equally: Jiankun Lyu, Sheng Wang, Trent E. Balius, Isha Singh

Authors and Affiliations

  1. Department of Pharmaceutical Chemistry, University of California, San Francisco, San Francisco, CA, USA

    Jiankun Lyu, Trent E. Balius, Isha Singh, Anat Levit, Matthew J. O’Meara, Enkhjargal Algaa, Brian K. Shoichet & John J. Irwin

  2. State Key Laboratory of Bioreactor Engineering, Shanghai Key Laboratory of New Drug Design, School of Pharmacy, East China University of Science & Technology, Shanghai, China

    Jiankun Lyu

  3. State Key Laboratory of Molecular Biology, CAS Center for Excellence in Molecular Cell Science, Shanghai Institute of Biochemistry and Cell Biology, Chinese Academy of Sciences, University of Chinese Academy of Sciences, Shanghai, China

    Sheng Wang

  4. Department of Pharmacology, University of North Carolina at Chapel Hill School of Medicine, Chapel Hill, NC, USA

    Sheng Wang, Tao Che & Bryan L. Roth

  5. National Taras Shevchenko University of Kiev, Kiev, Ukraine

    Yurii S. Moroz

  6. Chemspace, Riga, Latvia

    Yurii S. Moroz

  7. Enamine, Kiev, Ukraine

    Kateryna Tolmachova & Andrey A. Tolmachev

  8. Division of Chemical Biology and Medicinal Chemistry, Eshelman School of Pharmacy, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA

    Bryan L. Roth

  9. National Institute of Mental Health Psychoactive Drug Screening Program (NIMH PDSP), School of Medicine, University of North Carolina at Chapel Hill School of Medicine, Chapel Hill, NC, USA

    Bryan L. Roth

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Contributions

J.J.I. and B.K.S. conceived the study. J.J.I. created the enlarged docking libraries. J.L., T.E.B. and A.L. performed the docking. S.W., T.C. and B.L.R. performed D4 receptor assays and analysis. I.S. performed all crystallography and assays for AmpC. Y.S.M., K.T. and A.A.T. directed the compound synthesis, purification and characterization. M.J.O. performed Bayesian modelling. E.A., T.E.B. and J.L. contributed enabling computer code. B.K.S., J.L., J.J.I., B.L.R., T.E.B., I.S., A.L., S.W., T.C. and M.J.O. wrote the paper.

Corresponding authors

Correspondence to Brian K. Shoichet, Bryan L. Roth or John J. Irwin.

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Competing interests

B.K.S. and J.J.I. are founders of a company, BlueDolphin LLC, that works in the area of molecular docking. All other authors declare no competing interests.

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Extended data figures and tables

Extended Data Fig. 1 Simulating the effect of library size on ligand enrichment among the top 1,000 docked molecules.

a, b, The energy distribution of ligands (a) and decoys (b) from docking enrichment calculations against AmpC. The skewed normal fitting curves are plotted in red lines. The fitting parameters (shape (α), location (loc) and scale values) are shown. c, Heat maps of the number of active molecules in the top 1,000 docked molecules for 6 targets. The number of ligands in the top 1,000 docked molecules for a given library size and the ratio between ligands and decoys is coloured using a log10(number of ligands) scale ranging from 1 (blue) to 1,000 (red). Cells with zero ligands are shown in white. d, Large-library docking screens of AmpC (top, n = 99 million molecules) and D4 (bottom, n = 138 million molecules). Molecules that are known to bind to AmpC and D4, as well as close analogues, are treated as ligands and the rest of the molecules are treated as decoys. Left, the energy distributions of decoys (grey), ligands defined by ECFP4 Tc similarity ≥0.5 (blue), 0.6 (green) and 0.7 (orange) to ligands from ChEMBL. Middle, heat maps of the number of ligands in the top 1,000 docked molecules based on fit to full-library docking with the ligands (AmpC, Tc ≥ 0.5, green; D4, Tc ≥ 0.6, orange) and decoys (grey) distributions. Right, number of ligands in the top 1,000 docked molecules as the library grows based on actual distributions plotted in the left panel. Data are mean ± s.d. of 20 samples. See Supplementary Table 1 for retrospective performance on three more targets.

Extended Data Fig. 2 Initial hits and selected analogues against AmpC.

The five initial hits are shown in the left column. Under each compound, the first row includes the ZINC identifier; the second row is the cluster rank (position in cluster head list sorted by DOCK score) with global rank (position in unclustered hit list sorted by DOCK score) shown in brackets; the third row is the Tc value (Tanimoto coefficient to known AmpC inhibitors in ChEMBL); the fourth row is the Ki value. Five selected analogues for the corresponding hits are shown in the right column. Under each compound, the first row includes the ZINC identifier; the second row is the Tc value; and the third row is the Ki value.

Extended Data Fig. 3 Lineweaver–Burk plot and Ki analysis for analogues of each of the five series of AmpC inhibitors.

a–f, Lineweaver–Burk plots for ZINC776666294 (a), 275579920 (b), ZINC548592534 (c), ZINC1187516987 (d), 339204163 (e) and 549719643 (f), indicating competitive inhibition. IC50 values were determined by nonlinear regression fit in GraphPad Prism, and Ki values calculated by a replot of the slope of each Lineweaver–Burk plot versus the corresponding inhibitor concentration.

Extended Data Fig. 4 Electron density maps for AmpC–inhibitor complexes.

The initial Fo − Fc electron density map contoured at 2.5σ around the inhibitor (density in cyan) with refined 2Fo − Fc electron density contoured at 1σ for enzyme residues for the complexes with the following compounds. a, 547933290. b, 275579920. c, 339204163. d, 549719643. Inhibitor carbons are shown in cyan and enzyme carbons are shown in grey, oxygens in red, nitrogens in blue, sulfurs in yellow and chlorides in green.

Extended Data Fig. 5 Selected D4 hits from docking 138 million make-on-demand molecules.

Six ligands with docked poses (first column), cAMP Gαi/o activities (second column), Tango β-arrestin activities (third column) and 3H-N-methylspiperone displacement and chemical drawing (fourth column) are shown. The receptor structure is in grey and ligand carbons are in teal. Ballesteros–Weinstein residue numbers are included as superscripts. Functional assays represent normalized concentration–response curves of the ligands in cloned human D4-mediated activation of Gαi/o and β-arrestin translocation. Data are mean ± s.e.m. of three assays. The first row shows an example of an antagonist identified among the D4 hits. Both agonist (teal curve) and antagonist (purple curve) modes are shown for ZINC130532671 in the third panel; the concentration of quinpirole in the antagonist mode was 100 nM.

Source Data

Extended Data Fig. 6 Pre-clustering the docking library yields much worse scores of scaffold representatives compared to full library docking.

a, b, Comparison of energy distributions of scaffold representatives between full library docking (orange) and pre-clustered library docking for D4 (a) and AmpC (b) using four strategies: the closest member to the centroid of molecular masses and clogP (blue), the closest member to the centroid of molecular masses (pink), the member with the largest molecular masses (magenta) and the member with the smallest molecular masses (green). The inset shows the ratio of the number of molecules at a given docking score for full library docking divided by the number at that score when only cluster representatives are docked (coloured by clustering method). For each target, two examples illustrate the effect on our experimentally active scaffold families. c, D4. d, AmpC. The scaffold for each molecule is highlighted in red. The ZINC identifier, post-cluster rank and pre-cluster rank are labelled for each pair. The arrow colour is as for the pre-clustering methods in a and b.

Extended Data Fig. 7 Comparison of hit rates achieved by combined docking score and human prioritization compared to the rates achieved by docking score alone.

a, The hit rates for selecting compounds at different scoring ranges by each strategy: human prioritization and docking score (orange), or docking score alone (blue). Hit rate is the ratio of active compounds/tested compounds; the raw numbers appear at the top of each bar. b, Distribution of the binding affinity level among the hits from a. There are 32 hits from human prioritization and docking score, and 26 hits from the docking score alone. These are divided into three affinity ranges: <100 nM (pale blue); 100 nM–1 μM (blue); 1–10 μM (dark blue). c, Functional activity distribution among the hits from b. There are 22 molecules from human prioritization and docking score, and 7 molecules from the docking score alone. These are divided into five activity ranges: <10 nM (pale green); 10 nM–1 μM (light green); 1–10 μM (olive); 10–50 μM (forest green); and not determined (dark green).

Extended Data Fig. 8 Bayesian prior modelling for balancing information gain and ligand discovery in molecule-selection design and error estimation.

a, Sigmoid functional form for the hit-rate model. b–d, Marginal Bayesian prior (teal) and posterior (red) distributions (n = 200,000) for each model parameter. b, Top. c, Dock50. d, Slope. e, Estimated hit rate based on evaluation by the authors of the docked poses before any molecules were tested. Brown, mean ± s.d.; n = 200, 220, 230, 230, 285, 235, 210, 230 and 200 compounds; n = 5, 4, 4, 4, 4, 4, 4, 4 and 4 experts. The prior mean (green) and samples (n = 200) from the prior (blue) are shown. f, Candidate (blue) and chosen (orange) experimental designs (inset, designs 1–6), with expected number of hits and information gain for each design. g, Expected number of active scaffolds (orange, mean; grey, posterior draws n = 200,000) superimposed on the total number of scaffold cluster heads (black). h, i, Marginal distribution of the number of active compounds (h) and scaffolds (i) over the posterior distributions (n = 200,000).

Extended Data Table 1 Data collection and refinement statistics of AmpC inhibitors
Full size table
Extended Data Table 2 The highest-affinity direct docking hits for the D4 receptor
Full size table

Supplementary information

Supplementary Information

This file contains Supplementary Tables and Data 1-15, except Supplementary Tables 2, 4, 7 and 8, which are provided as separate files.

Reporting Summary

Supplementary Table

This file contains Supplementary Table 2: Molecules tested against β-lactamase AmpC. We report the zinc id, smiles, and indicator of binding or non-binding. (supplied as a separate file. See Extended Data Fig. 2 for affinities and chemical drawings of potent binders).

Supplementary Table

This file contains Supplementary Table 4: All molecules tested against D4.

Supplementary Table

This file contains Supplementary Table 7: Full-library vs pre-clustering library docking for D4.

Supplementary Table

This file contains Supplementary Table 8: Full-library vs pre-clustering library docking for AmpC.

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Lyu, J., Wang, S., Balius, T.E. et al. Ultra-large library docking for discovering new chemotypes. Nature 566, 224–229 (2019). https://doi.org/10.1038/s41586-019-0917-9

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