Medicinal Chemistry Research

, Volume 26, Issue 10, pp 2345–2356 | Cite as

De novo computational design of compounds virtually displaying potent antibacterial activity and desirable in vitro ADMET profiles

  • Alejandro Speck-Planche
  • M. Natália D. S. Cordeiro
Original Research


In this work, we introduce the first multitasking model for quantitative structure-biological effect relationships focused on the simultaneous exploration of antibacterial activity against Gram-negative pathogens and in vitro safety profiles related to absorption, distribution, metabolism, elimination, and toxicity (ADMET). The multitasking model for quantitative structure-biological effect relationships was created from a data set containing 46,229 cases, and it exhibited accuracy higher than 97% in both training and prediction (test) sets. Several molecular fragments present in the compounds of the data set were selected, and their contributions to multiple biological effects were calculated, providing useful insights toward the detection of 2D pharmacophores, toxicophores, etc. Here, we used a fragment-based philosophy known as puzzle approach, where different fragments with positive contributions against all the biological effects (antibacterial activity and ADMET properties) were assembled as pieces of a puzzle, leading to the creation of six new molecules. Such assembly was dictated by the physicochemical interpretations of the different molecular descriptors of the model. The new molecules were predicted to exhibit potent activity against Gram-negative bacteria, and desirable ADMET properties. The druglikeness of these new molecules was in agreement with the Lipinski’s rule of five, making them promising candidates for future biological testing in the framework of collaborative drug discovery.


ADMET Antibacterial Design Fragment Quantitative contribution mtk-QSBER 



The authors are grateful for the joint financial support given by the Portuguese Fundação para a Ciência e a Tecnologia (FCT/MEC) and FEDER (Projects No. UID/QUI/50006/2013 and POCI/01/0145/FEDER/007265).

Compliance with ethical standards

Conflict of interest

The authors declare that they have no competing interests.

Supplementary material

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  1. Alonso N, Caamano O, Romero-Duran FJ, Luan F, Cordeiro MNDS, Yanez M, Gonzalez-Diaz H, Garcia-Mera X (2013) Model for high-throughput screening of multitarget drugs in chemical neurosciences: synthesis, assay, and theoretic study of rasagiline carbamates. ACS Chem Neurosci 4(10):1393–1403CrossRefPubMedPubMedCentralGoogle Scholar
  2. Baskin II, Skvortsova MI, Stankevich IV, Zefirov NS (1995) On the basis of invariants of labeled molecular graphs. J Chem Inf Comput Sci 35(3):527–531CrossRefGoogle Scholar
  3. Borchardt RT, Kerns EH, Hageman MJ, Thakker DR, Stevens JL (eds) (2006) Optimizing the “Drug-Like” Properties of Leads in Drug Discovery, vol IV. Biotechnology: Pharmaceutical Aspects, Springer Science+Business Media, LLC, New York, NYGoogle Scholar
  4. Brachman PS, Abrutyn E (2009) Bacterial Infections of Humans: Epidemiology and Control. Springer Science+Business Media, LLC, New York, NYCrossRefGoogle Scholar
  5. CambridgeSoft (2003) ChemDraw Ultra.v8.0, PerkinElmer, Inc., Cambridge, MAGoogle Scholar
  6. Carloni P, Alber F (eds) (2003) Quantum Medicinal Chemistry, vol 17. Methods and Principles in Medicinal Chemistry. WILEY-VCH Verlag GmbH & Co. KGaA, WeinheimGoogle Scholar
  7. Carrio P, Pinto M, Ecker G, Sanz F, Pastor M (2014) Applicability Domain ANalysis (ADAN): a robust method for assessing the reliability of drug property predictions. J Chem Inf Model 54(5):1500–1511CrossRefPubMedGoogle Scholar
  8. Croes S, Koop AH, van Gils SA, Neef C (2012) Efficacy, nephrotoxicity and ototoxicity of aminoglycosides, mathematically modelled for modelling-supported therapeutic drug monitoring. Eur J Pharm Sci 45(1-2):90–100CrossRefPubMedGoogle Scholar
  9. ChemAxon (1998–2016) Standardizer (Tool for structure canonicalization and transformation), JChem.v15.11.16.0, ChemAxon, Budapest, HungaryGoogle Scholar
  10. Doucet JP, Weber J (1996) Computer-aided molecular design: Theory and applications. Academic Press, London, San Diego, New York, NY, Boston, Sidney, Tokyo, TorontoGoogle Scholar
  11. Estrada E (1999) Novel strategies in the search of topological indices. In: Devillers J, Balaban AT (eds) Topological Indices and Related Descriptors in QSAR and QSPR. Gordon and Breach Science Publishers, Amsterdam, p 403–453Google Scholar
  12. Estrada E, Peña A (2000) In silico studies for the rational discovery of anticonvulsant compounds. Bioorg Med Chem 8(12):2755–2770CrossRefPubMedGoogle Scholar
  13. Estrada E, Uriarte E, Montero A, Teijeira M, Santana L, De Clercq E (2000) A novel approach for the virtual screening and rational design of anticancer compounds. J Med Chem 43(10):1975–1985CrossRefPubMedGoogle Scholar
  14. Gaspar HA, Marcou G, Horvath D, Arault A, Lozano S, Vayer P, Varnek A (2013) Generative topographic mapping-based classification models and their applicability domain: application to the biopharmaceutics Drug Disposition Classification System (BDDCS). J Chem Inf Model 53(12):3318–3325CrossRefPubMedGoogle Scholar
  15. Gaulton A, Bellis LJ, Bento AP, Chambers J, Davies M, Hersey A, Light Y, McGlinchey S, Michalovich D, Al-Lazikani B, Overington JP (2012) ChEMBL: a large-scale bioactivity database for drug discovery. Nucleic Acids Res 40:D1100–1107. Database issueCrossRefPubMedGoogle Scholar
  16. Ghose AK, Viswanadhan VN, Wendoloski JJ (1999) A knowledge-based approach in designing combinatorial or medicinal chemistry libraries for drug discovery. 1. A qualitative and quantitative characterization of known drug databases. J Comb Chem 1(1):55–68CrossRefPubMedGoogle Scholar
  17. Gubernator K, James CA, Gubernator N (2005) eMolecules. Available at: Accessed 15 Dec 2016
  18. Hau J, Schapiro SJ (2011) Handbook of Laboratory Animal Science: Essential Principles and Practices. CRC Press, Taylor & Francis Group, LLC, Boca Raton, FLGoogle Scholar
  19. Hill T, Lewicki P (2006) STATISTICS methods and applications. A comprehensive reference for science, industry and data mining. StatSoft, TulsaGoogle Scholar
  20. Irwin JJ, Shoichet BK (2005) ZINC–a free database of commercially available compounds for virtual screening. J Chem Inf Model 45(1):177–182CrossRefPubMedPubMedCentralGoogle Scholar
  21. Jahnke W, Erlanson DA (2006) Fragment-based approaches in drug discovery. Wiley-VCH Verlag GmbH & Co. KGaA, Weinheim, GermanyCrossRefGoogle Scholar
  22. Kaiser J (2005) Science resources. Chemists want NIH to curtail database. Science 308(5723):774CrossRefPubMedGoogle Scholar
  23. Kaye KS, Pogue JM (2015) Infections caused by resistant Gram-negative bacteria: Epidemiology and management. Pharmacotherapy 35(10):949–962CrossRefPubMedGoogle Scholar
  24. Klein CD, Hopfinger AJ (1998) Pharmacological activity and membrane interactions of antiarrhythmics: 4D-QSAR/QSPR analysis. Pharm Res 15(2):303–311CrossRefPubMedGoogle Scholar
  25. Kubinyi H, Folkers G, Martin YC (eds) (2002) 3D QSAR in Drug Design: Recent Advances, vol 3. Kluwer Academic Publishers, New York, NY, Boston, Dordrecht, London, MoscowGoogle Scholar
  26. Lipinski CA, Lombardo F, Dominy BW, Feeney PJ (2001) Experimental and computational approaches to estimate solubility and permeability in drug discovery and development settings. Adv Drug Deliv Rev 46(1-3):3–26CrossRefPubMedGoogle Scholar
  27. Luan F, Cordeiro MNDS, Alonso N, Garcia-Mera X, Caamano O, Romero-Duran FJ, Yanez M, Gonzalez-Diaz H (2013) TOPS-MODE model of multiplexing neuroprotective effects of drugs and experimental-theoretic study of new 1,3-rasagiline derivatives potentially useful in neurodegenerative diseases. Bioorg Med Chem 21(7):1870–1879CrossRefPubMedGoogle Scholar
  28. Mok NY, Brenk R (2011) Mining the ChEMBL database: an efficient chemoinformatics workflow for assembling an ion channel-focused screening library. J Chem Inf Model 51(10):2449–2454CrossRefPubMedPubMedCentralGoogle Scholar
  29. Moriguchi I, Hirono S, Liu Q, Nakagome I, Matsushita Y (1992) Simple method of calculating octanol/water partition coefficient. Chem Pharm Bull 40(1):127–130CrossRefGoogle Scholar
  30. Overington J (2009) ChEMBL. An interview with John Overington, team leader, chemogenomics at the European bioinformatics institute outstation of the European molecular biology laboratory (EMBL-EBI). Interview by Wendy A. Warr. J Comput Aided Mol Des 23(4):195–198CrossRefPubMedGoogle Scholar
  31. Paul SM, Mytelka DS, Dunwiddie CT, Persinger CC, Munos BH, Lindborg SR, Schacht AL (2010) How to improve R&D productivity: the pharmaceutical industry’s grand challenge. Nat Rev Drug Discov 9(3):203–214PubMedGoogle Scholar
  32. Pearson K (1895) Notes on regression and inheritance in the case of two parents. Proc R Soc Lond 58:240–242CrossRefGoogle Scholar
  33. Prado-Prado FJ, Garcia-Mera X, Gonzalez-Diaz H (2010) Multi-target spectral moment QSAR versus ANN for antiparasitic drugs against different parasite species. Bioorg Med Chem 18(6):2225–2231CrossRefPubMedGoogle Scholar
  34. Romero-Duran FJ, Alonso N, Yanez M, Caamano O, Garcia-Mera X, Gonzalez-Diaz H (2016) Brain-inspired cheminformatics of drug-target brain interactome, synthesis, and assay of TVP1022 derivatives. Neuropharmacology 103:270–278CrossRefPubMedGoogle Scholar
  35. Romero Duran FJ, Alonso N, Caamano O, Garcia-Mera X, Yanez M, Prado-Prado FJ, Gonzalez-Diaz H (2014) Prediction of multi-target networks of neuroprotective compounds with entropy indices and synthesis, assay, and theoretical study of new asymmetric 1,2-rasagiline carbamates. Int J Mol Sci 15(9):17035–17064CrossRefPubMedPubMedCentralGoogle Scholar
  36. Ryan KJ, Ray CG (2004) Sherris Medical Microbiology. An Introduction to infectious diseases. McGraw-Hill Companies, Inc, ArizonaGoogle Scholar
  37. Sahigara F, Mansouri K, Ballabio D, Mauri A, Consonni V, Todeschini R (2012) Comparison of different approaches to define the applicability domain of QSAR models. Molecules 17(5):4791–4810CrossRefPubMedGoogle Scholar
  38. Speck-Planche A, Cordeiro MNDS (2013) Simultaneous modeling of antimycobacterial activities and ADMET profiles: a chemoinformatic approach to medicinal chemistry. Curr Top Med Chem 13(14):1656–1665CrossRefPubMedGoogle Scholar
  39. Speck-Planche A, Cordeiro MNDS (2014a) Chemoinformatics for medicinal chemistry: in silico model to enable the discovery of potent and safer anti-cocci agents. Future Med Chem 6(18):2013–2028CrossRefPubMedGoogle Scholar
  40. Speck-Planche A, Cordeiro MNDS (2014b) Simultaneous virtual prediction of anti-Escherichia coli activities and ADMET profiles: A chemoinformatic complementary approach for high-throughput screening. ACS Comb Sci 16(2):78–84CrossRefPubMedGoogle Scholar
  41. Speck-Planche A, Kleandrova VV, Cordeiro MNDS (2013a) New insights toward the discovery of antibacterial agents: Multi-tasking QSBER model for the simultaneous prediction of anti-tuberculosis activity and toxicological profiles of drugs. Eur J Pharm Sci 48(4-5):812–818CrossRefPubMedGoogle Scholar
  42. Speck-Planche A, Kleandrova VV, Luan F, Cordeiro MNDS (2013b) Unified multi-target approach for the rational in silico design of anti-bladder cancer agents. Anticancer Agents Med Chem 13(5):791–800CrossRefPubMedGoogle Scholar
  43. Speck-Planche A, Kleandrova VV, Ruso JM, Cordeiro MNDS (2016) First multitarget chemo-bioinformatic model to enable the discovery of antibacterial peptides against multiple Gram-positive pathogens. J Chem Inf Model 56(3):588–598CrossRefPubMedGoogle Scholar
  44. Statsoft-Team (2001) STATISTICA. Data analysis software system.v6.0, TulsaGoogle Scholar
  45. Talete-srl (2015) DRAGON (Software for Molecular Descriptor Calculation).v6.0,
  46. Tenorio-Borroto E, Penuelas-Rivas CG, Vasquez-Chagoyan JC, Castanedo N, Prado-Prado FJ, Garcia-Mera X, Gonzalez-Diaz H (2014) Model for high-throughput screening of drug immunotoxicity - Study of the anti-microbial G1 over peritoneal macrophages using flow cytometry. Eur J Med Chem 72:206–220CrossRefPubMedGoogle Scholar
  47. Tenorio-Borroto E, Penuelas Rivas CG, Vasquez Chagoyan JC, Castanedo N, Prado-Prado FJ, Garcia-Mera X, Gonzalez-Diaz H (2012) ANN multiplexing model of drugs effect on macrophages; theoretical and flow cytometry study on the cytotoxicity of the anti-microbial drug G1 in spleen. Bioorg Med Chem 20(20):6181–6194CrossRefPubMedGoogle Scholar
  48. Toplak M, Mocnik R, Polajnar M, Bosnic Z, Carlsson L, Hasselgren C, Demsar J, Boyer S, Zupan B, Stalring J (2014) Assessment of machine learning reliability methods for quantifying the applicability domain of QSAR regression models. J Chem Inf Model 54(2):431–441CrossRefPubMedGoogle Scholar
  49. Valdés-Martini JR, García-Jacas CR, Marrero-Ponce Y, Silveira Vaz ‘d Almeida Y, Morell C (2012) QUBILs-MAS: Free software for molecular descriptors calculator from quadratic, bilinear and linear maps based on graph-theoretic electronic-density matrices and atomic weightings.v1.0, Villa Clara,
  50. Vasoo S, Barreto JN, Tosh PK (2015) Emerging issues in gram-negative bacterial resistance: an update for the practicing clinician. Mayo Clin Proc 90(3):395–403CrossRefPubMedGoogle Scholar
  51. Veber DF, Johnson SR, Cheng HY, Smith BR, Ward KW, Kopple KD (2002) Molecular properties that influence the oral bioavailability of drug candidates. J Med Chem 45(12):2615–2623CrossRefPubMedGoogle Scholar
  52. Vedani A, Dobler M (2002) 5D-QSAR: the key for simulating induced fit? J Med Chem 45(11):2139–2149CrossRefPubMedGoogle Scholar
  53. Vedani A, Dobler M, Lill MA (2005) Combining protein modeling and 6D-QSAR. Simulating the binding of structurally diverse ligands to the estrogen receptor. J Med Chem 48(11):3700–3703CrossRefPubMedGoogle Scholar
  54. Williams AJ (2011) Chemspider: a platform for crowdsourced collaboration to curate data derived from public compound databases. In: Ekins S, Hupcey MAZ, Williams AJ (eds) Collaborative computational technologies for biomedical research. John Wiley & Sons, Inc., Hoboken, NJ, p 363–386CrossRefGoogle Scholar

Copyright information

© Springer Science+Business Media New York 2017

Authors and Affiliations

  1. 1.LAQV@REQUIMTE/Department of Chemistry and BiochemistryUniversity of PortoPortoPortugal

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