Skip to main content

Thank you for visiting nature.com. You are using a browser version with limited support for CSS. To obtain the best experience, we recommend you use a more up to date browser (or turn off compatibility mode in Internet Explorer). In the meantime, to ensure continued support, we are displaying the site without styles and JavaScript.

  • Review Article
  • Published:

Computational advances in combating colloidal aggregation in drug discovery

Nature Chemistry volume 11, pages 402–418 (2019)Cite this article

Subjects

Abstract

Small molecule effectors are essential for drug discovery. Specific molecular recognition, reversible binding and dose-dependency are usually key requirements to ensure utility of a novel chemical entity. However, artefactual frequent-hitter and assay interference compounds may divert lead optimization and screening programmes towards attrition-prone chemical matter. Colloidal aggregates are the prime source of false positive readouts, either through protein sequestration or protein-scaffold mimicry. Nevertheless, assessment of colloidal aggregation remains somewhat overlooked and under-appreciated. In this Review, we discuss the impact of aggregation in drug discovery by analysing select examples from the literature and publicly-available datasets. We also examine and comment on technologies used to experimentally identify these potentially problematic entities. We focus on evidence-based computational filters and machine learning algorithms that may be swiftly deployed to flag chemical matter and mitigate the impact of aggregates in discovery programmes. We highlight the tools that can be used to scrutinize libraries, and identify and eliminate these problematic compounds.

This is a preview of subscription content, access via your institution

Access options

Buy this article

  • Purchase on Springer Link
  • Instant access to full article PDF
Buy now

Prices may be subject to local taxes which are calculated during checkout

Fig. 1: Mechanisms of unspecific protein inhibition by colloidal aggregates.
Fig. 2: SCAMs are ubiquitous in drug discovery and impact network pharmacology.
Fig. 3: Historical evolution of confirmed SCAMs and their underlying scaffolds.
Fig. 4: Common filters do not detect SCAMs.
Fig. 5: Comparison of three different in silico methods for the automated identification of SCAMs.

Similar content being viewed by others

References

  1. Schurmann, M., Janning, P., Ziegler, S. & Waldmann, H. Small-molecule target engagement in cells. Cell Chem. Biol. 23, 435–441 (2016).

    PubMed  Google Scholar 

  2. Arrowsmith, C. H. et al. The promise and peril of chemical probes. Nat. Chem. Biol. 11, 536–541 (2015).

    CAS  PubMed  PubMed Central  Google Scholar 

  3. Garbaccio, R. M. & Parmee, E. R. The impact of chemical probes in drug discovery: a pharmaceutical industry perspective. Cell Chem. Biol. 23, 10–17 (2016).

    CAS  PubMed  Google Scholar 

  4. Sink, R., Gobec, S., Pecar, S. & Zega, A. False positives in the early stages of drug discovery. Curr. Med. Chem. 17, 4231–4255 (2010).

    CAS  PubMed  Google Scholar 

  5. Rishton, G. M. Reactive compounds and in vitro false positives in HTS. Drug Discov. Today 2, 382–384 (1997).

    CAS  Google Scholar 

  6. Roche, O. et al. Development of a virtual screening method for identification of “frequent hitters” in compound libraries. J. Med. Chem. 45, 137–142 (2002).

    CAS  PubMed  Google Scholar 

  7. Baell, J. B. & Holloway, G. A. New substructure filters for removal of pan assay interference compounds (PAINS) from screening libraries and for their exclusion in bioassays. J. Med. Chem. 53, 2719–2740 (2010).

    CAS  PubMed  Google Scholar 

  8. Devine, S. M. et al. Promiscuous 2-aminothiazoles (PrATs): a frequent hitting scaffold. J. Med. Chem. 58, 1205–1214 (2015).

    CAS  PubMed  Google Scholar 

  9. Huth, J. R. et al. ALARM NMR: a rapid and robust experimental method to detect reactive false positives in biochemical screens. J. Am. Chem. Soc. 127, 217–224 (2005).

    CAS  PubMed  Google Scholar 

  10. Hann, M. et al. Strategic pooling of compounds for high-throughput screening. J. Chem. Inform. Comput. Sci. 39, 897–902 (1999).

    CAS  Google Scholar 

  11. Dahlin, J. L., Inglese, J. & Walters, M. A. Mitigating risk in academic preclinical drug discovery. Nat. Rev. Drug Discov. 14, 279–294 (2015).

    CAS  PubMed  PubMed Central  Google Scholar 

  12. Aldrich, C. et al. The ecstasy and agony of assay interference compounds. ACS Cent. Sci. 3, 143–147 (2017).

    CAS  PubMed  PubMed Central  Google Scholar 

  13. Ganesh, A. N., Donders, E. N., Shoichet, B. K. & Shoichet, M. S. Colloidal aggregation: from screening nuisance to formulation nuance. Nano Today 19, 188–200 (2018).

    CAS  PubMed  PubMed Central  Google Scholar 

  14. Young, R. J., Green, D. V., Luscombe, C. N. & Hill, A. P. Getting physical in drug discovery II: the impact of chromatographic hydrophobicity measurements and aromaticity. Drug Discov. Today 16, 822–830 (2011).

    CAS  PubMed  Google Scholar 

  15. Baell, J. & Walters, M. A. Chemistry: chemical con artists foil drug discovery. Nature 513, 481–483 (2014).

    CAS  PubMed  Google Scholar 

  16. Baell, J. B. Feeling nature’s PAINS: natural products, natural product drugs, and pan assay interference Compounds (PAINS). J. Nat. Prod. 79, 616–628 (2016).

    CAS  PubMed  Google Scholar 

  17. McGovern, S. L., Caselli, E., Grigorieff, N. & Shoichet, B. K. A common mechanism underlying promiscuous inhibitors from virtual and high-throughput screening. J. Med. Chem. 45, 1712–1722 (2002).

    CAS  PubMed  Google Scholar 

  18. McGovern, S. L., Helfand, B. T., Feng, B. & Shoichet, B. K. A specific mechanism of nonspecific inhibition. J. Med. Chem. 46, 4265–4272 (2003).

    CAS  PubMed  Google Scholar 

  19. Jadhav, A. et al. Quantitative analyses of aggregation, autofluorescence, and reactivity artifacts in a screen for inhibitors of a thiol protease. J. Med. Chem. 53, 37–51 (2010).

    CAS  PubMed  PubMed Central  Google Scholar 

  20. Pohjala, L. & Tammela, P. Aggregating behavior of phenolic compounds—a source of false bioassay results? Molecules 17, 10774–10790 (2012).

    CAS  PubMed  PubMed Central  Google Scholar 

  21. Coan, K. E. D. & Shoichet, B. K. Stoichiometry and physical chemistry of promiscuous aggregate-based inhibitors. J. Am. Chem. Soc. 130, 9606–9612 (2008).

    CAS  PubMed  PubMed Central  Google Scholar 

  22. Duan, D. et al. Internal structure and preferential protein binding of colloidal aggregates. ACS Chem. Biol. 12, 282–290 (2017).

    CAS  PubMed  Google Scholar 

  23. Coan, K. E. D., Maltby, D. A., Burlingame, A. L. & Shoichet, B. K. Promiscuous aggregate-based inhibitors promote enzyme unfolding. J. Med. Chem. 52, 2067–2075 (2009).

    CAS  PubMed  PubMed Central  Google Scholar 

  24. Shoichet, B. K. Interpreting steep dose-response curves in early inhibitor discovery. J. Med. Chem. 49, 7274–7277 (2006).

    CAS  PubMed  Google Scholar 

  25. Blevitt, J. M. et al. Structural basis of small-molecule aggregate induced inhibition of a protein−protein interaction. J. Med. Chem. 60, 3511–3517 (2017).

    CAS  PubMed  Google Scholar 

  26. Owen, S. C. et al. Colloidal drug formulations can explain “bell-shaped” concentration-response curves. ACS Chem. Biol. 9, 777–784 (2014).

    CAS  PubMed  PubMed Central  Google Scholar 

  27. Sassano, M. F., Doak, A. K., Roth, B. L. & Shoichet, B. K. Colloidal aggregation causes inhibition of G protein-coupled receptors. J. Med. Chem. 56, 2406–2414 (2013).

    CAS  PubMed  PubMed Central  Google Scholar 

  28. Doak, A. K., Wille, H., Prusiner, S. B. & Shoichet, B. K. Colloid formation by drugs in simulated intestinal fluid. J. Med. Chem. 53, 4259–4265 (2010).

    CAS  PubMed  PubMed Central  Google Scholar 

  29. Duan, D., Doak, A. K., Nedyalkova, L. & Shoichet, B. K. Colloidal aggregation and the in vitro activity of traditional chinese medicines. ACS Chem. Biol. 10, 978–988 (2015).

    CAS  PubMed  PubMed Central  Google Scholar 

  30. Seidler, J., McGovern, S. L., Doman, T. N. & Shoichet, B. K. Identification and prediction of promiscuous aggregating inhibitors among known drugs. J. Med. Chem. 46, 4477–4486 (2003).

    CAS  PubMed  Google Scholar 

  31. Ferreira, R. S. et al. Divergent modes of enzyme inhibition in a homologous structure-activity series. J. Med. Chem. 52, 5005–5008 (2009).

    CAS  PubMed  PubMed Central  Google Scholar 

  32. Feng, B. Y., Shelat, A., Doman, T. N., Guy, R. K. & Shoichet, B. K. High-throughput assays for promiscuous inhibitors. Nat. Chem. Biol. 1, 146–148 (2005).

    CAS  PubMed  Google Scholar 

  33. Feng, B. Y. et al. A high-throughput screen for aggregation-based inhibition in a large compound library. J. Med. Chem. 50, 2385–2390 (2007).

    CAS  PubMed  Google Scholar 

  34. Ngo, T. et al. Orphan receptor ligand discovery by pickpocketing pharmacological neighbors. Nat. Chem. Biol. 13, 235–242 (2017).

    CAS  PubMed  Google Scholar 

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

    CAS  PubMed  Google Scholar 

  36. van Hattum, H. & Waldmann, H. Biology-oriented synthesis: harnessing the power of evolution. J. Am. Chem. Soc. 136, 11853–11859 (2014).

    PubMed  Google Scholar 

  37. Tannert, R. et al. Synthesis and structure–activity correlation of natural-product inspired cyclodepsipeptides stabilizing F-actin. J. Am. Chem. Soc. 132, 3063–3077 (2010).

    CAS  PubMed  Google Scholar 

  38. Takayama, H. et al. Discovery of inhibitors of the Wnt and Hedgehog signaling pathways through the catalytic enantioselective synthesis of an iridoid-inspired compound collection. Angew. Chem. Int. Ed. 52, 12404–12408 (2013).

    CAS  Google Scholar 

  39. Nelson, K. M. et al. The essential medicinal chemistry of curcumin. J. Med. Chem. 60, 1620–1637 (2017).

    CAS  PubMed  PubMed Central  Google Scholar 

  40. Baker, M. Deceptive curcumin offers cautionary tale for chemists. Nature 541, 144–145 (2017).

    CAS  PubMed  Google Scholar 

  41. Reker, D. et al. Revealing the macromolecular targets of complex natural products. Nat. Chem. 6, 1072–1078 (2014).

    CAS  PubMed  Google Scholar 

  42. Rodrigues, T. et al. Unveiling (-)-englerin A as a modulator of L-type calcium channels. Angew. Chem. Int. Ed. 55, 11077–11081 (2016).

    CAS  Google Scholar 

  43. Rodrigues, T., Reker, D., Kunze, J., Schneider, P. & Schneider, G. Revealing the macromolecular targets of fragment-like natural products. Angew. Chem. Int. Ed. 54, 10516–10520 (2015).

    CAS  Google Scholar 

  44. Rodrigues, T. et al. Machine intelligence decrypts β-lapachone as an allosteric 5-lipoxygenase inhibitor. Chem. Sci. 9, 6899–8903 (2018).

    CAS  PubMed  PubMed Central  Google Scholar 

  45. Matter, W. F., Brown, R. F. & Vlahos, C. J. The inhibition of phosphatidylinositol 3-kinase by quercetin and analogs. Biochem. Biophys. Res. Commun. 186, 624–631 (1992).

    CAS  PubMed  Google Scholar 

  46. Fabre, S., Prudhomme, M. & Rapp, M. Protein kinase C inhibitors; structure-activity relationships in K252c-related compounds. Bioorg. Med. Chem. 1, 193–196 (1993).

    CAS  PubMed  Google Scholar 

  47. McGovern, S. L. & Shoichet, B. K. Kinase inhibitors: not just for kinases anymore. J. Med. Chem. 46, 1478–1483 (2003).

    CAS  PubMed  Google Scholar 

  48. Wermuth, C. G. Selective optimization of side activities: the SOSA approach. Drug Discov. Today 11, 160–164 (2006).

    CAS  PubMed  Google Scholar 

  49. Hopkins, A. L. Network pharmacology: the next paradigm in drug discovery. Nat. Chem. Biol. 4, 682–690 (2008).

    CAS  PubMed  Google Scholar 

  50. Hopkins, A. L. Network pharmacology. Nat. Biotechnol. 25, 1110–1111 (2007).

    CAS  PubMed  Google Scholar 

  51. Irwin, J. J. et al. An aggregation advisor for ligand discovery. J. Med. Chem. 58, 7076–7087 (2015).

    CAS  PubMed  PubMed Central  Google Scholar 

  52. Mysinger, M. M. et al. Structure-based ligand discovery for the protein–protein interface of chemokine receptor CXCR4. Proc. Natl Acad. Sci. USA 109, 5517–5522 (2012).

    CAS  PubMed  PubMed Central  Google Scholar 

  53. Prinz, H. Hill coefficients, dose-response curves and allosteric mechanisms. J. Chem. Biol. 3, 37–44 (2010).

    PubMed  Google Scholar 

  54. Owen, S. C., Doak, A. K., Wassam, P., Shoichet, M. S. & Shoichet, B. K. Colloidal aggregation affects the efficacy of anticancer drugs in cell culture. ACS Chem. Biol. 7, 1429–1435 (2012).

    CAS  PubMed  PubMed Central  Google Scholar 

  55. Irwin, J. J. & Shoichet, B. K. Docking screens for novel ligands conferring new biology. J. Med. Chem. 59, 4103–4120 (2016).

    CAS  PubMed  PubMed Central  Google Scholar 

  56. Ryan, A. J., Gray, N. M., Lowe, P. N. & Chung, C. W. Effect of detergent on “promiscuous” inhibitors. J. Med. Chem. 46, 3448–3451 (2003).

    CAS  PubMed  Google Scholar 

  57. Feng, B. Y. & Shoichet, B. K. A detergent-based assay for the detection of promiscuous inhibitors. Nat. Protoc. 1, 550–553 (2006).

    CAS  PubMed  PubMed Central  Google Scholar 

  58. Coan, K. E. & Shoichet, B. K. Stability and equilibria of promiscuous aggregates in high protein milieus. Mol. BioSyst. 3, 208–213 (2007).

    CAS  PubMed  Google Scholar 

  59. Tomohara, K., Ito, T., Onikata, S., Kato, A. & Adachi, I. Discovery of hyaluronidase inhibitors from natural products and their mechanistic characterization under DMSO-perturbed assay conditions. Bioorg. Med. Chem. Lett. 27, 1620–1623 (2017).

    CAS  PubMed  Google Scholar 

  60. Rodrigues, T. et al. Multidimensional de novo design reveals 5-HT2B receptor-selective ligands. Angew. Chem. Int. Ed. 54, 1551–1555 (2015).

    CAS  Google Scholar 

  61. Chan, L. L. et al. A method for identifying small-molecule aggregators using photonic crystal biosensor microplates. JALA 14, 348–359 (2009).

    CAS  PubMed  Google Scholar 

  62. Rausch, K., Reuter, A., Fischer, K. & Schmidt, M. Evaluation of nanoparticle aggregation in human blood serum. Biomacromolecules 11, 2836–2839 (2010).

    CAS  PubMed  Google Scholar 

  63. Ganesh, A. N., McLaughlin, C. K., Duan, D., Shoichet, B. K. & Shoichet, M. S. A new spin on antibody–drug conjugates: trastuzumab–fulvestrant colloidal drug aggregates target HER2-positive cells. ACS Appl. Mater. Interfaces 9, 12195–12202 (2017).

    CAS  PubMed  PubMed Central  Google Scholar 

  64. Lifeng, C. & Gochin, M. Colloidal aggregate detection by rapid fluorescence measurement of liquid surface curvature changes in multiwell plates. J. Biomol. Screen. 12, 966–971 (2007).

    Google Scholar 

  65. LaPlante, S. R. et al. Compound aggregation in drug discovery: implementing a practical NMR assay for medicinal chemists. J. Med. Chem. 56, 5142–5150 (2013).

    CAS  PubMed  Google Scholar 

  66. Zega, A. NMR methods for identification of false positives in biochemical screens. J. Med. Chem. 60, 9437–9447 (2017).

    CAS  PubMed  Google Scholar 

  67. Giannetti, A. M., Koch, B. D. & Browner, M. F. Surface plasmon resonance based assay for the detection and characterization of promiscuous inhibitors. J. Med. Chem. 51, 574–580 (2008).

    CAS  PubMed  Google Scholar 

  68. Merk, D., Friedrich, L., Grisoni, F. & Schneider, G. De novo design of bioactive small molecules by artificial intelligence. Mol. Inform. 37, 1700153 (2018).

    PubMed Central  Google Scholar 

  69. Frenkel, Y. V. et al. Concentration and pH dependent aggregation of hydrophobic drug molecules and relevance to oral bioavailability. J. Med. Chem. 48, 1974–1983 (2005).

    CAS  PubMed  Google Scholar 

  70. Walters, W. P., Murcko, A. A. & Murcko, M. A. Recognizing molecules with drug-like properties. Curr. Opin. Chem. Biol. 3, 384–387 (1999).

    CAS  PubMed  Google Scholar 

  71. Walters, W. P. & Namchuk, M. Designing screens: how to make your hits a hit. Nat. Rev. Drug Discov. 2, 259–266 (2003).

    CAS  PubMed  Google Scholar 

  72. Walters, W. P., Stahl, M. T. & Murcko, M. A. Virtual screening—an overview. Drug Discov. Today 3, 160–178 (1998).

    CAS  Google Scholar 

  73. Olson, M. E. et al. Oxidative reactivities of 2-furylquinolines: ubiquitous scaffolds in common high-throughput screening libraries. J. Med. Chem. 58, 7419–7430 (2015).

    CAS  PubMed  PubMed Central  Google Scholar 

  74. Lipinski, C. A. Drug-like properties and the causes of poor solubility and poor permeability. J. Pharmacol. Tox. Methods 44, 235–249 (2000).

    CAS  Google Scholar 

  75. Brenk, R. et al. Lessons learnt from assembling screening libraries for drug discovery for neglected diseases. ChemMedChem 3, 435–444 (2008).

    CAS  PubMed  Google Scholar 

  76. Baell, J. B. & Nissink, J. W. M. Seven year itch: pan-assay interference compounds (PAINS) in 2017-utility and limitations. ACS Chem. Biol. 13, 36–44 (2018).

    CAS  PubMed  Google Scholar 

  77. Sadowski, J. & Kubinyi, H. A scoring scheme for discriminating between drugs and nondrugs. J. Med. Chem. 41, 3325–3329 (1998).

    CAS  PubMed  Google Scholar 

  78. Schneider, P., Rothlisberger, M., Reker, D. & Schneider, G. Spotting and designing promiscuous ligands for drug discovery. Chem. Commun. 52, 1135–1138 (2016).

    CAS  Google Scholar 

  79. Yang, J. J. et al. Badapple: promiscuity patterns from noisy evidence. J. Cheminformatics 8, 29 (2016).

    CAS  Google Scholar 

  80. Stork, C. et al. Hit dexter: a machine-learning model for the prediction of frequent hitters. ChemMedChem 13, 564–571 (2018).

    CAS  PubMed  Google Scholar 

  81. Rao, H. et al. Identification of small molecule aggregators from large compound libraries by support vector machines. J. Comput. Chem. 31, 752–763 (2010).

    CAS  PubMed  Google Scholar 

  82. 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).

    CAS  PubMed  PubMed Central  Google Scholar 

  83. Chen, H., Engkvist, O., Wang, Y., Olivecrona, M. & Blaschke, T. The rise of deep learning in drug discovery. Drug Discov. Today (2018).

  84. Reker, D., Schneider, P. & Schneider, G. Multi-objective active machine learning rapidly improves structure–activity models and reveals new protein–protein interaction inhibitors. Chem. Sci. 7, 3919–3927 (2016).

    CAS  PubMed  PubMed Central  Google Scholar 

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

    CAS  PubMed  Google Scholar 

  86. Capuzzi, S. J., Muratov, E. N. & Tropsha, A. Phantom PAINS: problems with the utility of alerts for pan-assay interference compoundS. J. Chem. Inf. Model. 57, 417–427 (2017).

    CAS  PubMed  PubMed Central  Google Scholar 

  87. Kenny, P. W. Comment on the ecstasy and agony of assay interference compounds. J. Chem. Inf. Model. 57, 2640–2645 (2017).

    CAS  PubMed  Google Scholar 

  88. Jasial, S., Hu, Y. & Bajorath, J. How frequently are pan-assay interference compounds active? Large-scale analysis of screening data reveals diverse activity profiles, low global hit frequency, and many consistently inactive compounds. J. Med. Chem. 60, 3879–3886 (2017).

    CAS  PubMed  Google Scholar 

  89. Gilberg, E., Stumpfe, D. & Bajorath, J. Activity profiles of analog series containing pan assay interference compounds. RSC Adv. 7, 35638–35647 (2017).

    CAS  Google Scholar 

  90. Vidler, L. R., Watson, I. A., Margolis, B. J., Cummins, D. J. & Brunavs, M. Investigating the behavior of published PAINS alerts using a pharmaceutical company dataset. ACS Med. Chem. Lett. 9, 792–796 (2018).

    CAS  PubMed  PubMed Central  Google Scholar 

  91. Senger, M. R., Fraga, C. A., Dantas, R. F. & Silva, F. P. Jr. Filtering promiscuous compounds in early drug discovery: is it a good idea? Drug Discov. Today 21, 868–872 (2016).

    CAS  PubMed  Google Scholar 

  92. Gilberg, E., Jasial, S., Stumpfe, D., Dimova, D. & Bajorath, J. Highly promiscuous small molecules from biological screening assays include many pan-assay interference compounds but also candidates for polypharmacology. J. Med. Chem. 59, 10285–10290 (2016).

    CAS  PubMed  Google Scholar 

  93. Perna, A. M. et al. Fragment-based de novo design reveals a small-molecule inhibitor of helicobacter pylori HtrA. Angew. Chem. Int. Ed. 54, 10244–10248 (2015).

    CAS  Google Scholar 

  94. Mike, L. A. et al. Activation of heme biosynthesis by a small molecule that is toxic to fermenting staphylococcus aureus. Proc. Natl Acad. Sci. USA 110, 8206–8211 (2013).

    CAS  PubMed  PubMed Central  Google Scholar 

  95. Lavecchia, A. Machine-learning approaches in drug discovery: methods and applications. Drug Discov. Today 20, 318–331 (2015).

    PubMed  Google Scholar 

  96. Schwaighofer, A. et al. Accurate solubility prediction with error bars for electrolytes: a machine learning approach. J. Chem. Inf. Model. 47, 407–424 (2007).

    CAS  PubMed  Google Scholar 

  97. Rodrigues, T. et al. De novo fragment design for drug discovery and chemical biology. Angew. Chem. Int. Ed. 54, 15079–15083 (2015).

    CAS  Google Scholar 

  98. Cheng, J., Tegge, A. N. & Baldi, P. Machine learning methods for protein structure prediction. IEEE Rev. Biomed. Eng. 1, 41–49 (2008).

    PubMed  Google Scholar 

  99. Wernick, M. N., Yang, Y., Brankov, J. G., Yourganov, G. & Strother, S. C. Machine learning in medical imaging. IEEE Signal Process. Mag. 27, 25–38 (2010).

    PubMed  PubMed Central  Google Scholar 

  100. Zhang, L., Tan, J., Han, D. & Zhu, H. From machine learning to deep learning: progress in machine intelligence for rational drug discovery. Drug Discov. Today 22, 1680–1685 (2017).

    PubMed  Google Scholar 

  101. Segler, M. H. S., Preuss, M. & Waller, M. P. Planning chemical syntheses with deep neural networks and symbolic AI. Nature 555, 604–610 (2018).

    CAS  PubMed  Google Scholar 

  102. Reker, D. & Schneider, G. Active-learning strategies in computer-assisted drug discovery. Drug Discov. Today 20, 458–465 (2015).

    PubMed  Google Scholar 

  103. Reker, D., Schneider, P., Schneider, G. & Brown, J. B. Active learning for computational chemogenomics. Future Med. Chem. 9, 381–402 (2017).

    CAS  PubMed  Google Scholar 

  104. Kohonen, T. Self-organized formation of topologically correct feature maps. Biol. Cybern. 43, 59–69 (1982).

    Google Scholar 

  105. Cortes, C. & Vapnik, V. Support-vector networks. Mach. Learn. 20, 273–297 (1995).

    Google Scholar 

  106. Weston, J. et al. in Advances in Neural Information Processing Systems 13 (eds T. K. Leen, T. G. Dietterich, & V. Tresp) 668–674 (MIT Press, 2001).

  107. Ekins, S. et al. Analysis and hit filtering of a very large library of compounds screened against mycobacterium tuberculosis. Mol. BioSyst. 6, 2316–2324 (2010).

    CAS  PubMed  Google Scholar 

Download references

Acknowledgements

D.R. is a Swiss National Science Foundation Fellow (Grants P2EZP3_168827 and P300P2_177833). G.J.L.B. is a Royal Society URF (UF110046 and URF/R/180019), an iFCT Investigator (IF/00624/2015), and the recipient of an ERC StG (TagIt, Grant Agreement 676832). T.R. and G.J.L.B. acknowledge Marie Sklodowska-Curie ITN Protein Conjugates (Grant Agreement 675007) for funding. T.R. is a Marie Curie Fellow (Grant Agreement 743640). T.R. acknowledges the H2020 (TWINN-2017 ACORN, Grant Agreement 807281) and POR Lisboa 2020/FEDER (02/SAICT/2017, Grant Agreement Lisboa-01-0145-FEDER-028333) for funding. D.R. acknowledges the MIT-IBM Watson AI Lab and the MIT SenseTime coalition for funding.

Author information

Author notes

  1. These authors contributed equally: Daniel Reker and Tiago Rodrigues.

Authors and Affiliations

  1. Koch Institute for Integrative Cancer Research, Massachusetts Institute of Technology, Cambridge, MA, USA

    Daniel Reker

  2. Division of Gastroenterology, Hepatology and Endoscopy, Department of Medicine, Brigham and Women’s Hospital, Harvard Medical School, Boston, MA, USA

    Daniel Reker

  3. MIT-IBM Watson AI Lab, Massachusetts Institute of Technology, Cambridge, MA, USA

    Daniel Reker

  4. Department of Chemistry, University of Cambridge, Lensfield Road, Cambridge, UK

    Gonçalo J. L. Bernardes

  5. Instituto de Medicina Molecular, Faculdade de Medicina da Universidade de Lisboa, Lisboa, Portugal

    Gonçalo J. L. Bernardes & Tiago Rodrigues

Authors
  1. Daniel Reker
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Gonçalo J. L. Bernardes
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Tiago Rodrigues
    View author publications

    You can also search for this author in PubMed Google Scholar

Contributions

D.R. and T.R. conceived the study, performed literature and data analyses. D.R., G.J.L.B. and T.R. wrote the manuscript. All authors approved the submitted version of the manuscript.

Corresponding authors

Correspondence to Daniel Reker or Tiago Rodrigues.

Ethics declarations

Competing interests

The authors of this article declare that V. Cantrill is employed by G. Bernardes as a Research Coordinator at the University of Cambridge. V. Cantrill was not involved in the preparation, writing or editing of this Review, but is married to S. Cantrill, who is the Chief Editor of Nature Chemistry. The editorial team of Nature Chemistry declares that S. Cantrill has not been involved in the editorial handling of this Review.

Additional information

Publisher’s note: Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Reker, D., Bernardes, G.J.L. & Rodrigues, T. Computational advances in combating colloidal aggregation in drug discovery. Nat. Chem. 11, 402–418 (2019). https://doi.org/10.1038/s41557-019-0234-9

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1038/s41557-019-0234-9

This article is cited by

Search

Advanced search

Quick links

Nature Briefing

Sign up for the Nature Briefing newsletter — what matters in science, free to your inbox daily.

Get the most important science stories of the day, free in your inbox. Sign up for Nature Briefing