Advanced search
Publication Cover
Expert Opinion on Drug Discovery Volume 11, 2016 - Issue 2
Submit an article Journal homepage
1,937
Views
136
CrossRef citations to date
0
Altmetric
Reviews

An overview of molecular fingerprint similarity search in virtual screening

Ingo MueggeBoehringer Ingelheim Pharmaceuticals, Department of Small Molecule Discovery Research, Ridgefield, CT, USACorrespondence[email protected]
&
Prasenjit MukherjeeBoehringer Ingelheim Pharmaceuticals, Department of Small Molecule Discovery Research, Ridgefield, CT, USA
Pages 137-148 | Received 21 Sep 2015, Accepted 03 Nov 2015, Published online: 04 Dec 2015

Bibliography

  • • = of interest, •• = of considerable interest
  • Johnson MA, Maggiora G. Concepts and Applications of Molecular Similarity. Johnson MA, Maggiora G, editors. New York (NY): Wiley; 1990.
  • Bender A, Glen RC. Molecular similarity: a key technique in molecular informatics. Org Biomol Chem. 2004;2(22):3204–3218.
  • Roy K, Kar S, Das RN. Understanding the Basics of QSAR for Applications in Pharmaceutical Sciences and Risk Assessment. New York (NY): Academic Press; 2015.
  • Tropsha A, Golbraikh A. Predictive QSAR modeling workflow, model applicability domains, and virtual screening. Curr Pharm Des. 2007;13(34):3494–3504.
  • Varnek A, Tropsha A. Chemoinformatics approaches to virtual screening. Varnek A, Tropsha A, editors. Cambridge: RSC Publishing; 2008.
  • Willett P. Similarity-based approaches to virtual screening. Biochem Soc Trans. 2003;31(Pt 3):603–606.
  • Cereto-Massague A, Ojeda MJ, Valls C, et al. Molecular fingerprint similarity search in virtual screening. Methods. 2015;71:58–63.
  • Willett P. Similarity-based virtual screening using 2D fingerprints. Drug Discov Today. 2006;11(23–24):1046–1053.
  • Maggiora G, Vogt M, Stumpfe D, et al. Molecular similarity in medicinal chemistry. J Med Chem. 2014;57(8):3186–3204.
  • Walters WP, Stahl MT, Murcko MA. Virtual screening - an overview. Drug Discov Today. 1998;3(4):160–178.
  • Muegge I, Oloff S. Virtual Screening. In: Abraham DJ, Rotella DP, editors. Burger’s Medicinal Chemistry, Drug Discovery, and Development. 7th ed. Hoboken (NJ): Wiley; 2010. p. 1–46.
  • Klebe G. Virtual ligand screening: strategies, perspectives and limitations. Drug Discov Today. 2006;11(13–14):580–594.
  • Shoichet BK. Virtual screening of chemical libraries. Nature. 2004;432(7019):862–865.
  • Bajorath J. Integration of virtual and high-throughput screening. Nature Rev Drug Discov. 2002;1:882–894.
  • Baber JC, Shirley WA, Gao Y, et al. The use of consensus scoring in ligand-based virtual screening. J Chem Inf Model. 2006;46(1):277–288.
  • Zhu T, Cao S, Su PC, et al. Hit identification and optimization in virtual screening: practical recommendations based on a critical literature analysis. J Med Chem. 2013;56(17):6560–6572.
  • Kar S, Roy K. How far can virtual screening take us in drug discovery? Expert Opin Drug Discov. 2013;8(3):245–261.
  • Sheridan RP. Chemical similarity searches: when is complexity justified? Expert Opin Drug Discov. 2007;2(4):423–430.
  • Matter H, Potter T. Comparing 3D Pharmacophore Triplets and 2D Fingerprints for Selecting Diverse Compound Subsets. J Chem Inf Comput Sci. 1999;39:1211–1225.
  • Daylight fingerprints. Daylight Chemical Information Systems, Inc. Mission Viejo (CA); 2015 [cited 2015 Nov 19]. Available from: www.daylight.com.
  • Carhart RE, Smith DH, Venkataraghavan R. Atom Pairs as Molecular Features in Structure-Activity Studies: Definition and Application. J Chem Inf Comput Sci. 1985;25:64–73.
  • MACCS (Molecular ACCess System). 2002. MDL information Systems, San Leandro (CA).
  • Barnard JM, Downs GM, Von Scholley-Pfab A, et al. Use of Markush structure analysis techniques for descriptor generation and clustering of large combinatorial libraries. J Mol Graph Model. 2000;18(4–5):452–463.
  • PubChem Substructure Fingerprints. 2015 [cited 2015 Nov 19]. Available from: ftp://ftp.ncbi.nlm.nih.gov/pubchem/specifications/pubchem_fingerprints.txt.
  • Bender A, Mussa HY, Gill GS, et al. Molecular surface point environments for virtual screening and the elucidation of binding patterns (MOLPRINT 3D). J Med Chem. 2004;47(26):6569–6583.

• introduction of circular fingerprint concept.

  • Rogers D, Hahn M. Extended-connectivity fingerprints. J Chem Inf Model. 2010;50(5):742–754.
  • Schneider G, Neidhart W, Giller T, et al. “Scaffold-Hopping” by Topological Pharmacophore Search: A Contribution to Virtual Screening. Angew Chem Int Ed Engl. 1999;38(19):2894–2896.
  • McGregor MJ, Muskal SM. Pharmacophore Fingerprinting. 1. Application to QSAR and Focused Library Design. J Chem Inf Comput Sci. 1999;39:569–574.
  • McGregor MJ, Muskal SM. Pharmacophore Fingerprinting. 2. Application to Primary Library Design. J Chem Inf Comput Sci. 2000;40:117–125.
  • Mason JS, Cheney DL. Library design and virtual screening using multiple 4-point pharmacophore fingerprints. Pac Symp Biocomput. 2000;5:576–587.
  • Unity 2D fingerprints. Certara USA Inc; 2015 [cited 2015 Nov 19]. Available from: https://www.certara.com/.
  • Schwartz J, Awale M, Reymond JL. SMIfp (SMILES fingerprint) chemical space for virtual screening and visualization of large databases of organic molecules. J Chem Inf Model. 2013;53(8):1979–1989.
  • Deng Z, Chuaqui C, Singh J. Structural interaction fingerprint (SIFt): a novel method for analyzing three-dimensional protein-ligand binding interactions. J Med Chem. 2004;47(2):337–344.
  • Molecular Operating Environment (MOE), 2013.08; Chemical Computing Group Inc., 1010 Sherbooke St. West, Suite #910, Montreal, QC, Canada, H3A 2R7, 2015. 2015.
  • Rarey M, Dixon JS. Feature Trees: A new molecular similarity measure based on tree matching. J Comput Aided Mol Des. 1998;12:471–490.
  • Lesnik S, Stular T, Brus B, et al. LiSiCA: A Software for Ligand-Based Virtual Screening and Its Application for the Discovery of Butyrylcholinesterase Inhibitors. J Chem Inf Model. 2015;55(8):1521–1528.
  • McGaughey GB, Sheridan RP, Bayly CI, et al. Comparison of topological, shape, and docking methods in virtual screening. J Chem Inf Model. 2007;47(4):1504–1519.
  • Muegge I. Synergies of virtual screening approaches. Mini Rev Med Chem. 2008;8(9):927–933.
  • Sheridan RP, Kearsley SK. Why do we need so many chemical similarity search methods? Drug Discov Today. 2002;7(17):903–911.
  • Zhang Q, Muegge I. Scaffold hopping through virtual screening using 2D and 3D similarity descriptors: ranking, voting, and consensus scoring. J Med Chem. 2006;49(5):1536–1548.

• comparison of fingerprint similarities and docking in VS.

  • Awale M, Reymond JL. Atom pair 2D-fingerprints perceive 3D-molecular shape and pharmacophores for very fast virtual screening of ZINC and GDB-17. J Chem Inf Model. 2014;54(7):1892–1907.
  • Huang N, Shoichet BK, Irwin JJ. Benchmarking Sets for Molecular Docking. J Med Chem. 2006;49:6789–6801.
  • Mysinger MM, Carchia M, Irwin JJ, et al. Directory of useful decoys, enhanced (DUD-E): better ligands and decoys for better benchmarking. J Med Chem. 2012;55(14):6582–6594.
  • Awale M, Jin X, Reymond JL. Stereoselective virtual screening of the ZINC database using atom pair 3D-fingerprints. J Cheminform. 2015;7:3.
  • Horvath D, Marcou G, Varnek A. Do not hesitate to use Tversky-and other hints for successful active analogue searches with feature count descriptors. J Chem Inf Model. 2013;53(7):1543–1562.
  • Patterson DE, Cramer RD, Ferguson AM, et al. Neighborhood behavior: a useful concept for validation of “molecular diversity” descriptors. J Med Chem. 1996;39(16):3049–3059.

• introduction of neighborhood behavior principle.

  • Papadatos G, Cooper AW, Kadirkamanathan V, et al. Analysis of neighborhood behavior in lead optimization and array design. J Chem Inf Model. 2009;49(2):195–208.
  • Brown RD, Martin YC. Use of Structure-Activity Data To Compare Structure-Based Clustering Methods and Descriptors for Use in Compound Selection. J Chem Inf Comput Sci. 1996;36:572–584.
  • Brown RD, Martin YC. The information content of 2D and 3D structural descriptors relevant to ligand-receptor binding. J Chem Inf Comput Sci. 1997;37:1–9.
  • Martin YC, Kofron JL, Traphagen LM. Do structurally similar molecules have similar biological activity? J Med Chem. 2002;45(19):4350–4358.

•• an analysis of 150 HTS screens affords a quantitative correlation of molecular similarity and probability of activity.

• highlights the dependency of VS outcomes on databases searched against.

  • Muegge I, Oloff S. Advances in virtual screening. Drug Discov Today Technol. 2006;3:405–411.
  • Muegge I, Zhang Q. 3D virtual screening of large combinatorial spaces. Methods. 2015;71:14–20.
  • Hessler G, Zimmermann M, Matter H, et al. Multiple-ligand-based virtual screening: methods and applications of the MTree approach. J Med Chem. 2005;48(21):6575–6584.
  • Yap CW. PaDEL-descriptor: an open source software to calculate molecular descriptors and fingerprints. J Comput Chem. 2011;32(7):1466–1474.
  • Klekota J, Roth FP. Chemical substructures that enrich for biological activity. Bioinformatics. 2008;24(21):2518–2525.
  • Steinbeck C, Han Y, Kuhn S, et al. The Chemistry Development Kit (CDK): an open-source Java library for Chemo- and Bioinformatics. J Chem Inf Comput Sci. 2003;43(2):493–500.
  • Ewing T, Baber JC, Feher M. Novel 2D fingerprints for ligand-based virtual screening. J Chem Inf Model. 2006;46(6):2423–2431.
  • Smusz S, Kurczab R, Satala G, et al. Fingerprint-based consensus virtual screening towards structurally new 5-HT(6)R ligands. Bioorg Med Chem Lett. 2015;25(9):1827–1830.
  • Hamza A, Wei NN, Zhan CG. Ligand-based virtual screening approach using a new scoring function. J Chem Inf Model. 2012;52(4):963–974.
  • Hamza A, Wagner JM, Evans TJ, et al. Novel mycosin protease MycP(1) inhibitors identified by virtual screening and 4D fingerprints. J Chem Inf Model. 2014;54(4):1166–1173.
  • JChem version 5.3.1, ChemAxon. 2015 [cited 2015 Nov 19]. Available from: http://www.chemaxon.com.
  • Gabrielsen M, Kurczab R, Siwek A, et al. Identification of novel serotonin transporter compounds by virtual screening. J Chem Inf Model. 2014;54(3):933–943.
  • Marcou G, Rognan D. Optimizing fragment and scaffold docking by use of molecular interaction fingerprints. J Chem Inf Model. 2007;47(1):195–207.
  • Irwin JJ, Shoichet BK. ZINC–a free database of commercially available compounds for virtual screening. J Chem Inf Model. 2005;45(1):177–182.
  • Jansen C, Wang H, Kooistra AJ, et al. Discovery of novel Trypanosoma brucei phosphodiesterase B1 inhibitors by virtual screening against the unliganded TbrPDEB1 crystal structure. J Med Chem. 2013;56(5):2087–2096.
  • Tsigelny IF, Kouznetsova VL, Biswas N, et al. Development of a pharmacophore model for the catecholamine release-inhibitory peptide catestatin: virtual screening and functional testing identify novel small molecule therapeutics of hypertension. Bioorg Med Chem. 2013;21(18):5855–5869.
  • Distinto S, Esposito F, Kirchmair J, et al. Identification of HIV-1 reverse transcriptase dual inhibitors by a combined shape-, 2D-fingerprint- and pharmacophore-based virtual screening approach. Eur J Med Chem. 2012;50:216–229.
  • Hert J, Willett P, Wilton DJ, et al. Comparison of topological descriptors for similarity-based virtual screening using multiple bioactive reference structures. Org Biomol Chem. 2004;2(22):3256–3266.
  • Hert J, Willett P, Wilton DJ, et al. Comparison of fingerprint-based methods for virtual screening using multiple bioactive reference structures. J Chem Inf Comput Sci. 2004;44(3):1177–1185.
  • Hert J, Willett P, Wilton DJ, et al. Enhancing the effectiveness of similarity-based virtual screening using nearest-neighbor information. J Med Chem. 2005;48(22):7049–7054.
  • Hert J, Willett P, Wilton DJ, et al. New methods for ligand-based virtual screening: use of data fusion and machine learning to enhance the effectiveness of similarity searching. J Chem Inf Model. 2006;46(2):462–470.
  • Heikamp K, Bajorath J. How do 2D fingerprints detect structurally diverse active compounds? Revealing compound subset-specific fingerprint features through systematic selection. J Chem Inf Model. 2011;51(9):2254–2265.
  • Bender A, Jenkins JL, Scheiber J, et al. How similar are similarity searching methods? A principal component analysis of molecular descriptor space. J Chem Inf Model. 2009;49(1):108–119.
  • Duan J, Dixon SL, Lowrie JF, et al. Analysis and comparison of 2D fingerprints: insights into database screening performance using eight fingerprint methods. J Mol Graph Model. 2010;29(2):157–170.
  • Riniker S, Landrum GA. Open-source platform to benchmark fingerprints for ligand-based virtual screening. J Cheminform. 2013;5(1):26.
  • Whittle M, Gillet VJ, Willett P, et al. Enhancing the effectiveness of virtual screening by fusing nearest neighbor lists: a comparison of similarity coefficients. J Chem Inf Comput Sci. 2004;44(5):1840–1848.
  • Whittle M, Gillet VJ, Willett P, et al. Analysis of data fusion methods in virtual screening: theoretical model. J Chem Inf Model. 2006;46(6):2193–2205.
  • Duesbury E, Holliday J, Willett P. Maximum common substructure-based data fusion in similarity searching. J Chem Inf Model. 2015;55(2):222–230.
  • Riniker S, Fechner N, Landrum GA. Heterogeneous classifier fusion for ligand-based virtual screening: or, how decision making by committee can be a good thing. J Chem Inf Model. 2013;53(11):2829–2836.
  • Whittle M, Gillet VJ, Willett P, et al. Analysis of data fusion methods in virtual screening: similarity and group fusion. J Chem Inf Model. 2006;46(6):2206–2219.
  • Muegge I. Selection Criteria for Drug-Like Compounds. Med Res Rev. 2003;23:302–321.
  • Wassermann AM, Geppert H, Bajorath J. Searching for target-selective compounds using different combinations of multiclass support vector machine ranking methods, kernel functions, and fingerprint descriptors. J Chem Inf Model. 2009;49(3):582–592.
  • Zang Q, Rotroff DM, Judson RS. Binary classification of a large collection of environmental chemicals from estrogen receptor assays by quantitative structure-activity relationship and machine learning methods. J Chem Inf Model. 2013;53(12):3244–3261.
  • Liu R, Wallqvist A. Merging applicability domains for in silico assessment of chemical mutagenicity. J Chem Inf Model. 2014;54(3):793–800.
  • Zhou D, Alelyunas Y, Liu R. Scores of extended connectivity fingerprint as descriptors in QSPR study of melting point and aqueous solubility. J Chem Inf Model. 2008;48(5):981–987.
  • Clark AM, Ekins S. Open Source Bayesian Models. 2. Mining a “Big Dataset” To Create and Validate Models with ChEMBL. J Chem Inf Model. 2015;55(6):1246–1260.
  • Clark AM, Dole K, Coulon-Spektor A, et al. Open Source Bayesian Models. 1. Application to ADME/Tox and Drug Discovery Datasets. J Chem Inf Model. 2015;55(6):1231–1245.
  • Ajay, Walters WP, Murcko MA. Can we learn to distinguish between ‘drug-like’ and ‘nondrug-like’ molecules? J Med Chem. 1998;41:3314–3324.
  • Podlogar BL, Muegge I. “Holistic” In Silico Methods to Estimate the Synthetic and CNS Bioavailabilities of Potential Chemotherapeutic Agents. Curr Top Med Chem. 2001;1:257–275.
  • Balfer J, Bajorath J. Introduction of a methodology for visualization and graphical interpretation of Bayesian classification models. J Chem Inf Model. 2014;54(9):2451–2468.
  • Sutherland JJ, Higgs RE, Watson I, et al. Chemical fragments as foundations for understanding target space and activity prediction. J Med Chem. 2008;51(9):2689–2700.
  • Vieth M, Erickson J, Wang J, et al. Kinase inhibitor data modeling and de novo inhibitor design with fragment approaches. J Med Chem. 2009;52(20):6456–6466.
  • Bender A, Jenkins JL, Glick M, et al. “Bayes affinity fingerprints” improve retrieval rates in virtual screening and define orthogonal bioactivity space: when are multitarget drugs a feasible concept? J Chem Inf Model. 2006;46(6):2445–2456.
  • Olah M, Rad R, Ostopovici L, et al. WOMBAT and WOMBAT-PK: bioactivity databases for lead and drug discovery. In: Schreiber KL, Kapoor TM, Wess G, editors. Chemical Biology: From Small Molecules to Systems Biology and Drug Design. Weinheim (Germany): Wiley-VCH Verlag GmbH; 2008. p. 760–786.
  • Martin E, Mukherjee P, Sullivan D, et al. Profile-QSAR: a novel meta-QSAR method that combines activities across the kinase family to accurately predict affinity, selectivity, and cellular activity. J Chem Inf Model. 2011;51(8):1942–1956.
  • Mukherjee P, Martin E. Profile-QSAR and Surrogate AutoShim protein-family modeling of proteases. J Chem Inf Model. 2012;52(9):2430–2440.
  • Gaulton A, Bellis LJ, Bento AP, et al. ChEMBL: a large-scale bioactivity database for drug discovery. Nucleic Acids Res. 2012;40(Database issue):D1100–D1107.
  • O’Boyle NM, Banck M, James CA, et al. Open Babel: an open chemical toolbox. J Cheminform. 2011;3:33.
  • Steinbeck C, Hoppe C, Kuhn S, et al. Recent developments of the chemistry development kit (CDK) - an open-source java library for chemo- and bioinformatics. Curr Pharm Des. 2006;12(17):2111–2120.
  • Steinbeck C, Han Y, Kuhn S, et al. The Chemistry Development Kit (CDK): an open-source Java library for Chemo- and Bioinformatics. J Chem Inf Comput Sci. 2003;43(2):493–500.
  • Indigo. 2015 [cited 2015 Nov 19]. Available from: http://lifescience.opensource.epam.com/indigo/index.html
  • RDKit: 2015 [cited 2015 Nov 19]. Available from: http://www.rdkit.org.
  • Ambure A, Aher RB, Roy K. Recent Advances in the Open Access Cheminformatics Toolkits, Software Tools, Workflow Environments, and Databases. In: James KY, editors. Methods in Pharmacology and Toxicology. New York (NY): Springer Science+Business Media New York; 2014. p. 1–40.
  • Petrone PM, Wassermann AM, Lounkine E, et al. Biodiversity of small molecules–a new perspective in screening set selection. Drug Discov Today. 2013;18(13–14):674–680.

Reprints and Corporate Permissions

Please note: Selecting permissions does not provide access to the full text of the article, please see our help page How do I view content?

To request a reprint or corporate permissions for this article, please click on the relevant link below:

Order Reprints Request Corporate Permissions

Academic Permissions

Please note: Selecting permissions does not provide access to the full text of the article, please see our help page How do I view content?

Obtain permissions instantly via Rightslink by clicking on the button below:

Request Academic Permissions

If you are unable to obtain permissions via Rightslink, please complete and submit this Permissions form. For more information, please visit our Permissions help page.

Related research

People also read lists articles that other readers of this article have read.

Recommended articles lists articles that we recommend and is powered by our AI driven recommendation engine.

Cited by lists all citing articles based on Crossref citations.
Articles with the Crossref icon will open in a new tab.