Abstract
It is challenging to handle a large volume of molecular data without appropriate tools. Here, we describe the need and the approaches for the development of focussed virtual libraries to design efficient molecules and optimize them for lead generation. The experimental chemists and biologists are more interested in properties of chemicals and their response to biological system in both beneficial and adverse effects context rather than just their structures. In this chapter, the focus is to relate newly designed chemical structures to their predicted activity, property or toxicity. Property prediction tools save time, money and lives of experimental animals. They come in handy while taking informed decisions especially in certain cases involving pharmacodynamic studies of drug molecules in humans where there are inevitable ethical and safety concerns. Property prediction is an important component in virtual screening which is at the heart of drug design and the most important step where chemoinformatics plays a major role. The other fields where structure–activity relation-based principles hold good for virtual screening are agrochemicals and environmental science, specifically the toxicity and biodegradability prediction of pollutant molecules. In this chapter, we will show how to design software tools to handle generation of focussed virtual libraries from a given set of molecules with common features, fragments or bioactivity spectrum.
This is a preview of subscription content, log in via an institution.
Buying options
Tax calculation will be finalised at checkout
Purchases are for personal use only
Learn about institutional subscriptionsNotes
- 1.
Interested readers are encouraged to download the supporting materials related to ChemStar application (JCIM’ 2008, ACS).
References
Leo A, Hansch C, Church C (1969) Comparison of parameters currently used in the study of structure-activity relationships. J Med Chem 12:766–771
Admason GW, Bawdon D (1976) An empirical method of structure-activity correlation for polysubstituted cyclic compounds using wiswesser line notation. J Chem Inf Comput Sci 16(3):161–165
Choplin, F (1990) Computers and the medicinal chemist. In: Hansch C, Sammes PG, Taylor JB (eds) Comprehensive Medicinal Chemistry Pergamon Press, UK 4:33–58
Tropsha A, Gramatica P, Gombar V (2003) The importance of being earnest: validation is the absolute essential for successful application and interpretation of QSPR models. Mol Inform 22(1):69–77
Seybold PG, May M, Bagel UA (1987) Molecular structure property relationships. J Chem Educ 64(7):575
Todeschini R, Consonni V (2009) Molecular descriptors for chemoinformatics, vol 2. Wiley-VCH
Karelson M (2000) Molecular descriptors in QSAR/QSPR. Wiley
http://www.codessa-ro.com/descriptors/electrostatic/index.htm
Balaban AT (1997) From chemical topology to three dimensional geometry. Plenum Press, New York, 1–24
Karelson M, Lobanov V, Katritzky AR (1996) Quantum chemical descriptors in QSAR/QSPR studies. Chem Rev 96:1027–1043
Enoch SJ (2010)The use of quantum mechanics derived descriptors in computational toxicology. In: Puzyn T et al (ed) Challenges and advances in computational chemistry and physics, vol 8. Springer Science pp 24–27
Stanton D (1999) Evaluation and use of BCUT descriptors in QSAR and QSPR studies. J Chem Inf Comput Sci 39(1):11–20
Ma SL, Joung JY, Lee S, Cho KH, No KT (2012) PXR ligand classification model with SFED weighted WHIM and CoMMA descriptors. SAR QSAR Environ Res 23(5–6):485–504
Todeschini R, Bettiol C, Giurin G, Gramatica P, Miana P, Argese E (1996) Modeling and prediction by using WHIM descriptors in QSAR studies: submitochondrial particles(SMP) as toxicity biosensors of chlorophenols. Chemosphere 33:71–79
Hinselmann G, Rosenbaum L, Jahn A, Fechner N, Zell AJ (2011) Compound Mapper: an open source JAVA library and command line tool for chemical fingerprints. J Chemoinformatics 3:3
Rogers D, Mathew H(2010) Extended connectivity fingerprints. J Chem Inf Model 50(5):742–754
Bender A, Hamse Y, Mussa HY, Glen C (2010) Similarity searching of chemical databases using atom environment descriptors (Molprint 2D) evaluation of performance. J Chem Inf Comput Sci 44:1708–1718
Deursen R, Blum Lorenz CB, Reymond JL (2010) A searchable map of PubChem. J Chem Inf Model 50(11):1924–1934
Chemscreener unpublished results
Jorgenson WL, Duffy EM (2002) Prediction of drug solubility from structure. Adv Drug Deliv Rev 54:355–366
Livingstone DJ, Waterbeemd VD, Han I (2009) In silico prediction of human oral bioavailability. Method Prin Med Chem 40:433–451
Persson LC, Porter CJ, Charman WN, Bergstrom CA (2013) Computational prediction of drug solubility in lipid based formulation excipients. Pharm Res PMID:23771564
Faller B, Ertl P (2007) Computational approaches to determine drug solubility. Adv Drug Deliv Rev 59:533–545
Cortes-Cabrera A, Morris GM, Finn PW, Morreale A, Gago F (2013) Comparison of ultra fast 2D and 3D descriptors for side effect prediction and network analysis in polypharmacology. Br J Pharmacol. doi:10.1111/bph.12294
Rice BM, Byrd EF (2013) Evaluation of electrostatic descriptors for crystalline density. Langmuir
Garcia EJ, Pellitero PJ, Jallut C, Pirngruber GD (2013) Modeling adsorption properties on the basis of microscopic, molecular structural descriptors for non polar adsorbents. J Chem Inf Model
Wegner JK, Zell A (2003) Prediction of aqueous solubility and partition coefficient optimized by genetic algorithm based descriptors selection method. J Chem Inf Comput Sci 43(3):1077–1084
Steinbeck C, Hoppe C, Kuhn S, Matteo F, Guha R, Willighagen EL (2006) Recent development of the CDK (Chemistry Development Kit) an open source JAVA library for chemo and bioinformatics. Curr Pharm Design 12(17):2111–2120
Steinbeck C (2008) Open toolkits and applications for chemoinformatics teaching Abstracts of Papers, 235th ACS National Meeting, New Orleans, LA, United States, April 6–10
Yap CW (2011) Padel descriptor an open source software to calculate molecular descriptors and fingerprints. J Comput Chem 32(7):1466–1474
Liu K, Feng J, Young SS (2005) A software environment for molecular viewing, descriptor generation, data analysis and hit evaluation. J Chem Inf Model 45(2):515–522
http://www.chemaxon.com/marvin/help/calculations/calculator-plugins.html
Xueliang L, Yongtang S, Wang L (2012) On a relation between randic index and algebraic connectivity. Match 68(3):843–839
Ivanciuc O, Ivanciuc T, Douglas KJ, William SA, Balaban T (2001) Wiener index extension by counting even/odd graph distances. J Chem Inf Model 41(3):536–549
Benet LZ, Broccatelli F, Oprea TI (2011) BDDCS applied to over 900 drugs. AAPS J 13(4):519–547
Lu D, Chambers P, Wipf P, Xie X-Q, Englert D, Weber S (2012) Lipophilicity screening of novel drug like compounds and comparison to clogp. J Chromatogr A 1258:161–167
QikProp (2012) version 3.5, Schrödinger, LLC, New York
Kerns E, Li D (2010) Drug like properties, concepts, structure design and methods. Academic Press
LigPrep (2012) version 2.5, Schrödinger, LLC, New York
Molecular Operating Environment (MOE) (2012)10; Chemical Computing Group Inc., 1010 Montreal, QC, Canada, H3A 2R7, 2012
Gerardo CMM, Yovani MP, Khan MTH, Arjumand A, Khan KM, Torrens F, Rotondo R (2007) Dragon method for finding novel tyrosinase inhibitors biosilico identification and experimental in vitro assays. Eur J Med Chem 42(11–12):1370–1381
Karthikeyan M, Krishnan S, Pandey AK, Bender A, Tropsha A (2008) Distributed chemical computing using ChemStar: An open source java remote method invocation architecture applied to large scale molecular data from pubchem. J Chem Inf Model 48(4):691–703
Lusci A, Pollastri G, Baldi P (2013) Deep architectures and deep learning in Chemoinformatics: the prediction of aqueous solubility for drug like molecules. J Chem Inf Model 53(7):1563–1575
Sorana BD, Lorentz J (2011) Predictivity approach for quantitative structure prediction models: application for blood barrier permeation for diverse drug like compounds. Int J Mol Sci 12(7):4348–4386
Ulrich A, Koch C, Speitling M, Hansske FG (2002) Modern methods to produce natural-product libraries. Curr Opin Chem Biol 6(4):453–458
Bemis GW, Murcko MA (1999) Properties of known drugs, 2: Side chains. J Med Chem 42(25):5095–5099
Wetzel S, Karsten K, Renner S, Rauh D, Oprea TI, Mutzel P, Waldmann H (2009) Interactive exploration of chemical space with scaffold hunter. Nat Chem Biol 5(9):696
Van Drie JH (2009) ReCore. J Am Chem Soc 131(4):1617
Core Hopping (2011), version 1.1, Schrödinger, LLC, New York
Schuller A, Hahnke V, Schneider G (2007) SmiLib v2.0: A Java-Based Tool for Rapid Combinatorial Library Enumeration. QSAR Comb Sci 3:407–410
http://www.chemcomp.com/MOE-Cheminformatics_and_QSAR.htm#CombinatorialLibraryDesign
Tropsha A (2008) Integrated chemo and bioinformatics approaches to virtual screening. In: Tropsha A, Varnek A (ed) Chemoinformatics approaches to virtual screening. SC Publishing, pp 295–325
Perola E, Xu K, Kollmeyer TM, Kaufmann SH, Prendergast FG, Pang Y-P (2000) Successful virtual screening of a chemical database for farnesyl transferase inhibitor leads. J Med Chem 43(3):401–408
Oprea TI (2002) Virtual screening in lead discovery a viewpoint. Molecules 7:51–62
Unpublished results
Author information
Authors and Affiliations
Corresponding author
Rights and permissions
Copyright information
© 2014 Springer India
About this chapter
Cite this chapter
Karthikeyan, M., Vyas, R. (2014). Chemoinformatics Approach for the Design and Screening of Focused Virtual Libraries. In: Practical Chemoinformatics. Springer, New Delhi. https://doi.org/10.1007/978-81-322-1780-0_2
Download citation
DOI: https://doi.org/10.1007/978-81-322-1780-0_2
Published:
Publisher Name: Springer, New Delhi
Print ISBN: 978-81-322-1779-4
Online ISBN: 978-81-322-1780-0
eBook Packages: Chemistry and Materials ScienceChemistry and Material Science (R0)