Review

European Bioinformatics Institute, Wellcome Trust Genome Campus, Hinxton, Cambridge, CB10 1SD, UK.

Email the corresponding author at pedro.ballester@ebi.ac.uk

Published Online:23 Dec 2010https://doi.org/10.4155/fmc.10.280

Abstract

Molecular shape complementarity is widely recognized as a key indicator of biological activity. Unfortunately, efficient computation of shape similarity is challenging, which severely limits the potential of shape-based virtual screening. Ultrafast shape recognition (USR) is a recent shape similarity technique that is characterized by its extremely high speed of operation. Here we review important methodological aspects for the optimal application of USR as well as its first applications to medicinal chemistry problems. These applications already include several particularly successful prospective virtual screens, which shows the important role that USR can play in identifying bioactive molecules to be used as chemical probes and potentially as starting points for the drug-discovery process.

Papers of special note have been highlighted as: ▪ of interest ▪▪ of considerable interest

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Medline CASGoogle Scholar
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MedlineGoogle Scholar
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Medline CASGoogle Scholar
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Medline CASGoogle Scholar
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Medline CASGoogle Scholar
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Medline CASGoogle Scholar
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Medline CASGoogle Scholar
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MedlineGoogle Scholar
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Google Scholar
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Medline CASGoogle Scholar
24 Truchon J, Bayly CI. Evaluating virtual screening methods: good and bad metrics for the ‘early recognition’ problem. J. Chem. Inf. Model.47,488–508 (2007).
Medline CASGoogle Scholar
25 Kirchmair J, Distinto S, Markt P et al. How to optimize shape-based virtual screening: choosing the right query and including chemical information. J. Chem. Inf. Model.49,678–692(2009).▪ Study demonstrating the importance of using several active molecules as templates to improve ligand-based virtual screening performance.
Medline CASGoogle Scholar
26 Armstrong MS, Morris GM, Finn PW et al. ElectroShape: fast molecular similarity calculations incorporating shape, chirality and electrostatics. J. Comput. Aided Mol. Des.24(9),789–801 (2010).
Medline CASGoogle Scholar
27 Parker CN. McMaster University data-mining and docking competition: computational models on the catwalk. J. Biomol. Screen.10,647–648 (2005).
MedlineGoogle Scholar
28 Lang PT, Kuntz ID, Maggiora GM, Bajorath J. Evaluating the high-throughput screening computations. J. Biomol. Screen.10,649–652 (2005).
MedlineGoogle Scholar
29 Irwin J. Community benchmarks for virtual screening. J. Comput. Aided Mol. Des.22,193–199 (2008).
Medline CASGoogle Scholar
30 Rodrigues-Lima F, Dairou J, Busi F, Dupret J. Human arylamine N-acetyltransferase 1: a drug-metabolizing enzyme and a drug target? Curr. Drug Targets11,759–766 (2010).
Medline CASGoogle Scholar
31 Irwin JJ, Shoichet BK. ZINC – a free database of commercially available compounds for virtual screening. J. Chem. Inf. Model.45,177–182 (2005).▪▪ Presentation of the ZINC database, an extremely useful resource for prospective virtual screening.
Medline CASGoogle Scholar
32 Li H, Huang J, Chen L et al. Identification of novel falcipain-2 inhibitors as potential antimalarial agents through structure-based virtual screening. J. Med. Chem.52,4936–4940 (2009).
Medline CASGoogle Scholar
33 Ballester PJ, Mitchell JBO. A machine learning approach to predicting protein–ligand binding affinity with applications to molecular docking. Bioinformatics26,1169–1175 (2010).
Medline CASGoogle Scholar
34 Kaiser J. Industrial-style screening meets academic biology. Science321,764–766 (2008).
Medline CASGoogle Scholar
35 Oprea TI, Bologa CG, Boyer S et al. A crowdsourcing evaluation of the NIH chemical probes. Nat. Chem. Biol.5,441–447 (2009).
Medline CASGoogle Scholar
36 Vasudevan SR, Churchill GC. Mining free compound databases to identify candidates selected by virtual screening. Expert Opin. Drug Discov.4,901–906 (2009).▪ Perspective on how virtual screening is ready to power up academic hit identification.
Medline CASGoogle Scholar
37 Parker CN, Bajorath J. Towards unified compound screening strategies: a critical evaluation of error sources in experimental and virtual high-throughput screening. QSAR Comb. Sci.25(12),1153–1161 (2006).
CASGoogle Scholar
38 Dobson CM. Chemical space and biology. Nature432,824–828 (2004).
Medline CASGoogle Scholar
39 Sukumar N, Krein M, Breneman CM. Bioinformatics and cheminformatics: where do the twain meet? Curr. Opin. Drug Discov. Devel.11,311–319 (2008).
Medline CASGoogle Scholar
40 Morris RJ, Najmanovich RJ, Kahraman A, Thornton JM. Real spherical harmonic expansion coefficients as 3D shape descriptors for protein binding pocket and ligand comparisons. Bioinformatics21,2347–2355 (2005).
Medline CASGoogle Scholar
41 Hopkins AL. Network pharmacology: the next paradigm in drug discovery. Nat. Chem. Biol.4,682–690 (2008).▪▪ Simultaneously modulating several protein targets may be the key to drug efficacy, which makes the need of faster and more effective virtual screening techniques even more pressing.
Medline CASGoogle Scholar
101 MOE (The Molecular Operating Environment) Version 2006.08, software available from Chemical Computing Group Inc., 1010 Sherbrooke Street West, Suite 910, Montreal, Canada H3A 2R7. www.chemcomp.com
Google Scholar
102 OpenEye Scientific Software, Inc. www.eyesopen.com
Google Scholar
103 DUD: A Directry of Useful Decoys http://dud.docking.org/r2
Google Scholar
104 Molecular Libraries Initiative. http://mli.nih.gov/mli/
Google Scholar

Papers of special note have been highlighted as: ▪ of interest ▪▪ of considerable interest

Bibliography

1 Drewry DH, Macarron R. Enhancements of screening collections to address areas of unmet medical need: an industry perspective. Curr. Opin. Chem. Biol.14,289–298 (2010).
Medline CASGoogle Scholar
2 Peakman T, Franks S, White C, Beggs M. Delivering the power of discovery in large pharmaceutical organizations. Drug Discov. Today8,203–211 (2003).
MedlineGoogle Scholar
3 Schneider G. Virtual screening: an endless staircase? Nat. Rev. Drug Discov.9,273–276 (2010).▪ Perspective on the current role of virtual screening and its expected future development.
Medline CASGoogle Scholar
4 Zauhar RJ, Moyna G, Tian L, Li Z, Welsh WJ. Shape signatures, a new approach to computer-aided ligand- and receptor-based drug design. J. Med. Chem.46,5674–5690 (2003).
Medline CASGoogle Scholar
5 Kortagere S, Krasowski MD, Ekins S. The importance of discerning shape in molecular pharmacology. Trends Pharmacol. Sci.30,138–147 (2009).
Medline CASGoogle Scholar
6 Ebalunode JO, Zheng W. Molecular shape technologies in drug discovery: methods and applications. Curr. Top. Med. Chem.10,669–679 (2010).▪ Comprehensive review of molecular shape similarity techniques applied to drug discovery.
Medline CASGoogle Scholar
7 Ballester PJ, Richards WG. Ultrafast shape recognition to search compound databases for similar molecular shapes. J. Comput. Chem.28,1711–1723 (2007).
Medline CASGoogle Scholar
8 Ballester PJ, Richards WG. Ultrafast shape recognition for similarity search in molecular databases. Proc. R. Soc. A463,1307–1321 (2007).▪ Description of the original ultrafast shape recognition (USR) method addressed to a multi-disciplinary audience.
CASGoogle Scholar
9 Ballester PJ, Westwood I, Laurieri N, Sim E, Richards WG. Prospective virtual screening with ultrafast shape recognition: the identification of novel inhibitors of arylamine N-acetyltransferases. J. R. Soc. Interface7(43),335–342 (2010).▪▪ First prospective application of USR, which led to an outstanding proportion of novel inhibitors with diverse chemical structure.
Medline CASGoogle Scholar
10 Grant JA, Pickup BT. A Gaussian description of molecular shape. J. Phys. Chem.99,3503–3510 (1995).▪ The introduction of the ROCS method.
CASGoogle Scholar
11 Cannon EO, Nigsch F, Mitchell JBO. A novel hybrid ultrafast shape descriptor method for use in virtual screening. Chem. Cent. J.2(3) (2008).
MedlineGoogle Scholar
12 Nicholls A, McGaughey GB, Sheridan RP et al. Molecular shape and medicinal chemistry: a perspective. J. Med. Chem.53,3862–3886 (2010).
Medline CASGoogle Scholar
13 Armstrong MS, Morris GM, Finn PW, Sharma R, Richards WG. Molecular similarity including chirality. J. Mol. Graph. Model.28,368–370 (2009).
Medline CASGoogle Scholar
14 Zhou T, Lafleur K, Caflisch A. Complementing ultrafast shape recognition with an optical isomerism descriptor. J. Mol. Graph. Model.29(3),443–449 (2010).
Medline CASGoogle Scholar
15 Lafleur K, Huang D, Zhou T, Caflisch A, Nevado C. Structure-based optimization of potent and selective inhibitors of the tyrosine kinase erythropoietin producing human hepatocellular carcinoma receptor b4 (EphB4). J. Med. Chem.52,6433–6446 (2009).
Medline CASGoogle Scholar
16 Nguyen QC, Ong YS, Soh H, Kuo JL. Multiscale approach to explore the potential energy surface of water clusters (H₂O)nn ≤ 8. J. Phys. Chem. A112,6257–6261(2008).
Medline CASGoogle Scholar
17 Soh H, Ong Y-S, Nguyen QC. Discovering unique, low-energy pure water isomers: memetic exploration, optimization, and landscape analysis. IEEE Trans. Evol. Computat.14(3),419–437 (2010).
Google Scholar
18 Schreyer A, Blundell TL. CREDO: a protein–ligand interaction database for drug discovery. Chem. Biol. Drug Des.73,157–167 (2009).
Medline CASGoogle Scholar
19 Lee S, Blundell TL. BIPA: a database for protein–nucleic acid interaction in 3D structures. Bioinformatics25,1559–1560 (2009).
Medline CASGoogle Scholar
20 Guha R, Gilbert K, Fox G, Pierce M, Wild D, Yuan H. Advances in cheminformatics methodologies and infrastructure to support the data mining of large, heterogeneous chemical datasets. Curr. Comput. Aided Drug Des.6,50–67 (2010).▪ Pilot study demonstrating the suitability of USR to allow interactive 3D shape similarity searches in PubChem.
Medline CASGoogle Scholar
21 Zhu Q, Lajiness MS, Ding Y, Wild DJ. WENDI: a tool for finding non-obvious relationships between compounds and biological properties, genes, diseases and scholarly publications. J. Cheminform.2 (6) (2010).
MedlineGoogle Scholar
22 Ballester PJ, Finn PW, Richards WG. Ultrafast shape recognition: evaluating a new ligand-based virtual screening technology. J. Mol. Graph. Model.27,836–845 (2009).
Google Scholar
23 Huang N, Shoichet B, Irwin J. Benchmarking sets for molecular docking. J. Med. Chem.49,6789–6801 (2006).
Medline CASGoogle Scholar
24 Truchon J, Bayly CI. Evaluating virtual screening methods: good and bad metrics for the ‘early recognition’ problem. J. Chem. Inf. Model.47,488–508 (2007).
Medline CASGoogle Scholar
25 Kirchmair J, Distinto S, Markt P et al. How to optimize shape-based virtual screening: choosing the right query and including chemical information. J. Chem. Inf. Model.49,678–692(2009).▪ Study demonstrating the importance of using several active molecules as templates to improve ligand-based virtual screening performance.
Medline CASGoogle Scholar
26 Armstrong MS, Morris GM, Finn PW et al. ElectroShape: fast molecular similarity calculations incorporating shape, chirality and electrostatics. J. Comput. Aided Mol. Des.24(9),789–801 (2010).
Medline CASGoogle Scholar
27 Parker CN. McMaster University data-mining and docking competition: computational models on the catwalk. J. Biomol. Screen.10,647–648 (2005).
MedlineGoogle Scholar
28 Lang PT, Kuntz ID, Maggiora GM, Bajorath J. Evaluating the high-throughput screening computations. J. Biomol. Screen.10,649–652 (2005).
MedlineGoogle Scholar
29 Irwin J. Community benchmarks for virtual screening. J. Comput. Aided Mol. Des.22,193–199 (2008).
Medline CASGoogle Scholar
30 Rodrigues-Lima F, Dairou J, Busi F, Dupret J. Human arylamine N-acetyltransferase 1: a drug-metabolizing enzyme and a drug target? Curr. Drug Targets11,759–766 (2010).
Medline CASGoogle Scholar
31 Irwin JJ, Shoichet BK. ZINC – a free database of commercially available compounds for virtual screening. J. Chem. Inf. Model.45,177–182 (2005).▪▪ Presentation of the ZINC database, an extremely useful resource for prospective virtual screening.
Medline CASGoogle Scholar
32 Li H, Huang J, Chen L et al. Identification of novel falcipain-2 inhibitors as potential antimalarial agents through structure-based virtual screening. J. Med. Chem.52,4936–4940 (2009).
Medline CASGoogle Scholar
33 Ballester PJ, Mitchell JBO. A machine learning approach to predicting protein–ligand binding affinity with applications to molecular docking. Bioinformatics26,1169–1175 (2010).
Medline CASGoogle Scholar
34 Kaiser J. Industrial-style screening meets academic biology. Science321,764–766 (2008).
Medline CASGoogle Scholar
35 Oprea TI, Bologa CG, Boyer S et al. A crowdsourcing evaluation of the NIH chemical probes. Nat. Chem. Biol.5,441–447 (2009).
Medline CASGoogle Scholar
36 Vasudevan SR, Churchill GC. Mining free compound databases to identify candidates selected by virtual screening. Expert Opin. Drug Discov.4,901–906 (2009).▪ Perspective on how virtual screening is ready to power up academic hit identification.
Medline CASGoogle Scholar
37 Parker CN, Bajorath J. Towards unified compound screening strategies: a critical evaluation of error sources in experimental and virtual high-throughput screening. QSAR Comb. Sci.25(12),1153–1161 (2006).
CASGoogle Scholar
38 Dobson CM. Chemical space and biology. Nature432,824–828 (2004).
Medline CASGoogle Scholar
39 Sukumar N, Krein M, Breneman CM. Bioinformatics and cheminformatics: where do the twain meet? Curr. Opin. Drug Discov. Devel.11,311–319 (2008).
Medline CASGoogle Scholar
40 Morris RJ, Najmanovich RJ, Kahraman A, Thornton JM. Real spherical harmonic expansion coefficients as 3D shape descriptors for protein binding pocket and ligand comparisons. Bioinformatics21,2347–2355 (2005).
Medline CASGoogle Scholar
41 Hopkins AL. Network pharmacology: the next paradigm in drug discovery. Nat. Chem. Biol.4,682–690 (2008).▪▪ Simultaneously modulating several protein targets may be the key to drug efficacy, which makes the need of faster and more effective virtual screening techniques even more pressing.
Medline CASGoogle Scholar
101 MOE (The Molecular Operating Environment) Version 2006.08, software available from Chemical Computing Group Inc., 1010 Sherbrooke Street West, Suite 910, Montreal, Canada H3A 2R7. www.chemcomp.com
Google Scholar
102 OpenEye Scientific Software, Inc. www.eyesopen.com
Google Scholar
103 DUD: A Directry of Useful Decoys http://dud.docking.org/r2
Google Scholar
104 Molecular Libraries Initiative. http://mli.nih.gov/mli/
Google Scholar

Vol. 3, No. 1

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Published online 23 December 2010

Published in print January 2011

Information

Acknowledgements

The author would like to thank Prof. Graham Richards from the Physical & Theoretical Chemistry Laboratory, Oxford University (UK), for mentorship and giving him complete freedom to investigate new approaches to molecular shape similarity during his first postdoctoral position. Thanks also to Prof. David Gavaghan from the Computing Laboratory, Oxford University (UK), for helpful feedback and strong support; and Dr Adrian Schreyer from the Department of Biochemistry, Cambridge University (UK), for providing the multiconformer version of the DUD data sets. The valuable feedback on the original manuscript by Dr John Mitchell from Biomolecular Sciences, University of St Andrews (UK), is also thankfully acknowledged.

Disclosure

Ballester PJ (2007): ‘Shape Recognition Methods and Systems for Searching Molecular Databases’. US Patent No. 12/127559. Free academic licenses of the USR software are available through www.isis-innovation.com/licensing/2932.html

Financial & competing interests disclosure

The author gratefully acknowledges financial support from the US National Foundation of Cancer Research, Oxford University’s Bridging the Gap scheme and the Medical Research Council (MRC) Methodology Research Fellowship programme. The authors have no other relevant affiliations or financial involvement with any organization or entity with a financial interest in or financial conflict with the subject matter or materials discussed in the manuscript apart from those disclosed.

No writing assistance was utilized in the production of this manuscript.

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Ultrafast shape recognition: method and applications

Abstract

Bibliography

Bibliography