[1]
Hou, T. ADME Evaluation in drug discovery. 8. The prediction of human intestinal absorption by a support vector machine. J. Chem. Inf. Model., 2007, 47(6), 2408-2415.
[2]
Selick, H.E.; Beresford, A.P.; Tarbit, M.H. The emerging importance of predictive ADME simulation in drug discovery. Drug Discov. Today, 2002, 7(2), 109-116.
[3]
Kubinyi, H. Drug research: Myths, hype and reality. Nat. Rev. Drug Discov., 2003, 2(8), 665-668.
[4]
Lyne, P.D. Structure-based virtual screening: An overview. Drug Discov. Today, 2002, 7(20), 1047-1055.
[5]
Oprea, T.I.; Davis, A.M.; Teague, S.J.; Leeson, P.D. Is there a difference between leads and drugs? A historical perspective. J. Chem. Inf. Comput. Sci., 2001, 41(5), 1308-1315.
[6]
Teague, S.J.; Davis, A.M.; Leeson, P.D.; Oprea, T. The design of leadlike combinatorial libraries. Angew. Chem. Int. Ed. Engl., 1999, 38(24), 3743-3748.
[7]
Beresford, A.P.; Segall, M.; Tarbit, M.H. In silico prediction of ADME properties: Are we making progress? Curr. Opin. Drug Discov. Devel., 2004, 7(1), 36-42.
[8]
Meyer, E.F.; Swanson, S.M.; Williams, J.A. Molecular modeling and drug design. Pharmacol. Ther., 2000, 85, 113-121.
[9]
Shinde, S.P.; Banerjee, A.K.; Arora, N.; Murty, U.S.; Sripathi, V.R.; Pal-Bhadra, M.; Bhadra, U. Computational approach for elucidating interactions of cross-species miRNAs and their targets in Flaviviruses. J. Vector Borne, 2015, 52, 11-22.
[10]
Albert, J.S.; Blomberg, N.; Breeze, A.L.; Brown, A.J.; Burrows, J.N.; Edwards, P.D.; Folmer, R.H.; Geschwindner, S.; Griffen, E.J.; Kenny, P.W.; Nowak, T.; Olsson, L.L.; Sanganee, H.; Shapiro, A.B. An integrated approach to fragment-based lead generation: Philosophy, strategy and case studies from AstraZeneca’s drug discovery programmes. Curr. Top. Med. Chem., 2007, 7(16), 1600-29.
[11]
Banerjee, A.K.; Ravi, V.; Murty, U.S.; Shanbhag, A.P.; Prasanna, V.L. Keratin protein property based classification of mammals and non-mammals using machine learning techniques. Comput. Biol. Med., 2013, 43, 889-899.
[12]
Banerjee, A.K.; Ravi, V.; Murty, U.S.; Sengupta, N.; Karuna, B. Application of intelligent techniques for classification of bacteria using protein sequence derived features. Appl. Biochem. Biotechnol., 2013, 170, 1263-1281.
[13]
Hileman, B. Accounting for R&D, many doubt the $800 million pharmaceutical price tag. Chemical. Eng. News, 2006, 84, 50-51.
[14]
Lesk, A.J.M. Introduction to bioinformatics; Oxford University Press Inc.: New York, 2002.
[15]
Baldi, A. Computational approaches for drug design and discovery: An overview. Sys. Rev. Pharm., 2010, 1, 99-105.
[17]
Balakumar, C.; Ramesh, M.; Tham, C.L.; Khathi, S.P.; Kozielski, F.; Srinivasulu, C.; Hampannavar, G.A.; Sayyad, N.; Soliman, M.E.; Karpoormath, R. Ligand- and structure-based in silico studies to identify kinesin spindle protein (KSP) inhibitors as potential anticancer agents. J. Biomol. Str. Dyn., 2017, 29, 1-18.
[18]
Lipinski, C.A.; Lombardo, F.; Dominy, B.W.; Feeney, P.J. Experimental and computational approaches to estimate solubility and permeability in drug discovery and development settings. Adv. Drug Deliv. Rev., 2001, 46(1-3), 3-26.
[20]
Gao, X.; Qian, J.; Zheng, S.; Changyi, Y.; Zhang, J.; Ju, S.; Zhu, J.; Li, C. Overcoming the blood-brain barrier for delivering drugs into the brain by using adenosine receptor nanoagonist. ACS Nano, 2014, 8(4), 3678-3689.
[21]
Levin, V.A. Relationship of octanol/water partition coefficient and
molecular weight to rat brain capillary permeability. J. Med.
Chem., 1980, 23, 682-684.
[22]
Kansy, M.; van de Waterbeemd, H. Hydrogen bonding capacity and brain penetration. Chimia, 1992, 46, 299-303.
[23]
Abraham, M.H.; Chadha, H.S.; Mitchell, R.C. Hydrogen bonding. 33. Factors that influence the distribution of solutes between blood and brain. J. Pharm. Sci., 1994, 83, 1257-1268.
[24]
Bala, N.; Raj, J.S.; Kandakatla, N. In silico studies of new Indazole derivatives as GSK-3β inhibitors. Int. J. Pharm. Pharma. Sci., 2015, 7(3), 295-299.
[25]
Veber, D.F.; Johnson, S.R.; Cheng, H.Y.; Smith, B.R.; Ward, K.W.; Kopple, K.D. Molecular properties that influence the oral bioavailability of drug candidates. J. Med. Chem., 2002, 45(12), 2615-2623.
[26]
Navia, M.A.; Chaturvedi, P.R. Design principles for orally bioavailable drugs. Drug Discovery. Today, 1996, 1, 179-189.
[27]
Smith, A.B.; Hirschmann, R.; Pasternak, A.; Yao, W.; Sprengler, P.A.; Halloway, M.K.; Kuo, L.C.; Chen, Z.; Darke, P.L.; Schleif, W.A. An orally bioavailable pyrrolinone inhibitor of hiv-1 protease: computational analysis and X-ray crystal structure of the enzyme complex. J. Med. Chem., 1997, 40, 2440-2444.
[28]
Palm, K.; Stenberg, P.; Luthman, K.; Artursson, P. Polar molecular surface properties predict the intestinal absorption of drugs in humans. Pharm. Res., 1997, 14, 568-571.
[29]
Hung-Yuan, C.; Smith, B.R.; Ward, K.W.; Kenneth, D. Kopple molecular properties that influence the oral bioavailability of drug candidates daniel F. Veber, Stephen R. Johnson. J. Med. Chem., 2002, 45, 2615-2623.
[30]
Palm, K.; Stenberg, P.; Luthman, K.; Artursson, P. Polar molecular surface properties predict the intestinal absorption of drugs in humans. Pharm. Res., 1997, 14, 568-571.
[31]
Azad, I.; Nasibullah, M.; Khan, T.; Hassan, F.; Akhter, Y. Exploring the novel heterocyclic derivatives as lead molecules for design and development of potent anticancer agents. J. Mol. Gra. Mod., 2018, 81, 211-228.
[32]
Clark, D.E. Rapid calculation of polar molecular surface area and its application to the prediction of transport phenomena. Prediction of intestinal absorption. J. Pharm. Sci., 1999, 88, 807-814.
[33]
Ertl, P.; Rohde, B.; Selzer, P. Fast calculation of molecular polar surface area as a sum of fragment-based contributions and its application to the prediction of drug transport properties. J. Med. Chem., 2000, 43, 3714-3717.
[34]
Bytheway, I.; Darley, M.G.; Popelier, P.L. The calculation of polar surface area from first principles: An application of quantum chemical topology to drug design. ChemMedChem, 2008, 3(3), 445-453.
[35]
Ghose, A.K.; Crippen, G.M. 1987 Atomic physicochemical parameters for three-dimensional-structure-directed quantitative structure-activity relationships. 2. Modeling dispersive and hydrophobic interactions. J. Chem. Inf. Comput. Sci., 1987, 27, 21-35.
[36]
Chapman, N. Correlation Analysis in Chemistry; Wiley: New York, 1978.
[37]
Hansch, C. Rockwell, S.D.; Jow, P.Y.C.; Leo, A.; Steller, E.E. Substituent Constants for Correlation Analysis. J. Med. Chem., 1977, 20(2), 304-306.
[38]
Khan, T.; Ahmad, R.; Azad, I.; Raza, S.; Joshi, S.; Khan, A.R. Computer-aided drug design and virtual screening of targeted combinatorial libraries of mixed-ligand transition metal complexes of 2-butanone thiosemicarbazone. Comput. Biol. Chem., 2018, 75, 178-195.
[39]
Viswanadhan, V.N.; Ghose, A.K.; Hanna, N.B.; Matsumoto, S.S.; Avery, T.L.; Revankar, G.R.; Robins, R.K. Analysis of the in vitro antitumor activity of novel purine-6-sulfenamide, -sulfinamide, and -sulfonamide nucleosides and certain related compounds using a computer-aided receptor modeling procedure. J. Med. Chem., 1991, 34, 526-532.
[40]
Ghose, A.K.; Vellarkad, N. Viswanadhan, John, J.; Wendoloski, J.A.; Knowledge-Based approach in designing combinatorial or medicinal chemistry libraries for drug discovery. 1. a qualitative and quantitative characterization of known drug databasescomb. Chemistry, 1999, 1, 55-68.
[41]
Riadh, H.; Salah, B.; Aicha, K.; Salima, B. Structure activity/property relationships of pyrazole derivatives by MPO and QSAR methods for drug design. Ser. J. Pharm. Biol. Chem. Sci., 2015, 6(4), 923-935.
[42]
Teague, S.J.; Davis, A.M.; Paul, D. Leeson, Oprea, T.; Angew. The design of lead like combinatorial libraries. Chem. Int. ed.,
1999, 38(24), 3743-3748.
[43]
Rohs, R.; Bloch, I.; Sklenar, H.; Shakked, Z. Molecular flexibility in ab-initio drug docking to DNA: Binding-site and binding-mode transitions in all-atom Monte Carlo simulations. Nucl Acids Res., 2005, 33, 7048-7057.
[44]
Guedes, I.A.; de Magalhães, C.S.; Dardenne, L.E. Receptor-ligand molecular docking. Bio. Rev, 2014, 6, 75-87.
[45]
López-Vallejo, F.; Caulfield, T.; Martínez-Mayorga, K.; Giulianotti, M.A.; Houghten, R.A.; Nefzi, A.; Medina-Franco, J.L. Integrating virtual screening and combinatorial chemistry for accelerated drug discovery. Comb. Chem. High Throughput Screen., 2011, 14, 475-487.
[46]
Agarwal, S.; Chadha, D.; Mehrotra, R. Molecular modeling and spectroscopic studies of semustine binding with DNA and its comparison with lomustine-DNA adduct formation. J. Biomol. Stru. Dyn., 2015, 33, 1653-1668.
[47]
Foloppe, N.; Hubbard, R. Towards predictive ligand design with free-energy based computational methods? Curr. Med. Chem., 2006, 13, 3583-3608.
[48]
Jain, A.N. Scoring functions for protein-ligand docking. Curr. Protein Pept. Sci., 2006, 7, 407-420.
[49]
Seeliger, D.; de Groot, B.L. Ligand docking and binding site analysis with PyMOL and Autodock/Vina. J. Comput. Aided Mol. Des., 2010, 24, 417-422.
[50]
Kapetanovic, I.M. Computer-aided drug discovery and development (CADDD): In silico-chemicobiological approach. Chem. Biol. Interact., 2008, 171, 165-176.
[51]
Lamb, M.L.; Jorgensen, W.L. Computational approaches to molecular recognition. Curr. Opin. Chem. Biol., 1997, 1, 449-457.
[52]
Gschwend, D.A.; Good, A.C.; Kuntz, I.D. Molecular docking towards drug discovery. J. Mol. Recognit., 1996, 9, 175-186.
[53]
Carlsson, L.; Spjuth, O.; Adams, S.; Glen, R.C.; Boyer, S. Use of historic metabolic biotransformation data as a means of anticipating metabolic sites using MetaPrint2D and Bioclipse. BMC Bioinformatics, 2010, 11, 362-367.
[56]
Meng, X-U.; Zhang, H-X.; Mezei, M.; Cui, M. Molecular docking: A powerful approach for structure-based drug discovery. Curr. Comput. Aided Drug Des., 2011, 7(2), 146-157.
[57]
Adam, C. Chamberlin; Levitt, D.G.; Cramer, C.J.; Truhlar, D.G. Modeling free energies of solvation in olive oil. Mol. Pharmceutic, 2008, 5(6), 1064-1079.
[58]
Kola, I.; Landis, J. Can the pharmaceutical industry reduce attrition rates? Nat. Rev. Drug. Dis., 2004, 3, 711-715.
[59]
Hou, T.; Wang, J. Structure-ADME relationship: Still a long way to go? Expert Opin. Drug Metab. Toxicol., 2008, 4, 759-770.
[60]
Kramer, J.A.; Sagartz, J.E.; Morris, D.L. The application of discovery toxicology and pathology towards the design of safer pharmaceutical lead candidates. Nat. Rev. Drug Discov., 2007, 6, 636-649.
[61]
Ames, B.N.; McCann, J.; Yamasaki, E. Methods for detecting carcinogens and mutagens with the Salmonella/mammalian-microsome mutagenicity test. Mutat. Res., 1975, 31, 347-364.
[62]
Iurii Sushko, S.N.; Igor, V.T. Applicability domain for in silico models to achieve accuracy of experimental measurements. J. Chemometr., 2010, 24, 202-208.
[63]
Cronin, T.D.M. Predicting Chemical Toxicity and Fate. 1st Taylor
& Francis Group CRC Press: Ohio, 2004.
[64]
Raymond, S.T.; Tirrell, M. Bottom-up design of biomimetic assemblies. Adv. Drug Deliv. Rev., 2004, 56(11), 1537-1563.
[65]
Benigni, R.; Giuliani, A. Computer-assisted analysis of interlaboratory Ames test variability. J. Toxicol. Environ. Health, 1988, 25, 135-148.
[66]
Cheng, F.; Weihua, Li.; Zhou, Y.; Jie, S.; Zengrui, W.; Guixia, L.; Philip, W.; Lee, Y. admetSAR: A comprehensive source and free tool for assessment of chemical ADMET properties. J. Chem. Inf. Model., 2012, 52, 3099-3105.
[67]
Cheng, F.; Li, W.; Zhou, Y.; Shen, J.; Wu, Z.; Liu, G.; Lee, P.W.; Tang, Y. admetSAR: A comprehensive source and free tool for assessment of chemical ADMET properties. J. Chem. Inf. Model., 2012, 52(11), 3099-3105.
[68]
Van Breemen, R.B.; Li, Y. Caco-2 cell permeability assays to measure drug absorption. Expert Opin. Drug Metab. Toxicol., 2005, 1(2), 175-185.
[69]
Aniyery, R.B.; Sharma, A.; Gupta, A. Molecular docking studies and in silico pharmacokinetic property study of synthesized organotin complex of (1r, 2s, 5r)-2-isopropyl-5- methylcyclohexanol. J. Chem. Pharm. Sci, 2015, 9(4), 2656-2663.
[70]
Ames, B.N. The metabolic tune-up: Metabolic harmony and disease prevention. J. Nutr., 2003, 94, 1544S-15448S.
[71]
Maritim, A.C.; Sanders, R.A.; Watkins, J.B. 3rd Diabetes, oxidative stress, and antioxidants: A review. J. Biochem. Mol. Toxicol., 2003, 17(2), 24-38.
[72]
B.N., Ames ACS Symposium Series, 2003.
[73]
Gunasekar, P.G.; Rogers, J.V.; Kabbur, M.B.; Garrett, C.M.; Brinkley, W.W.; McDougal, J.N. J. Biochem. Mol. Toxicol., 2003, 17(2), 92.
[74]
Puratchikody, A.; Doble, M.; Ramalakshmi, N. Toxicity risk assessment of some novel quinoxalines. RASAYAN J. Chem., 2011, 4(3), 636-639.
[75]
Segel, I.H. Biochemical calculations: How to solve mathematical problems in general biochemistry, 2nd ed; Wiley Publications: New York, 1976.
[76]
Appling, D.R.; Anthony-Cahill, S.J.; Mathews, C.K. Biochemistry: concepts and connection Biochemical genetics. 2nd ed; , 2018.
[78]
Verma, A. Lead finding from Phyllanthus debelis with hepatoprotective potentials. Asian Pac. J. Trop. Biomed., 2012, S1735-S1737.
[79]
Azad, I.; Jafri, A.; Khan, T.; Akhter, Y.; Arshad, M.; Hassan, F.; Ahmad, N.; Khan, A.R.; Nasibullah, M. Evaluation of pyrrole-2,3-dicarboxylate derivatives: Synthesis, DFT analysis, molecular docking, virtual screening and in vitro anti-hepatic cancer study. J. Mol. Str., 2019, 1176, 314-334.
[80]
Ertl, P.; Rohde, B.; Selzer, P. Fast calculation of molecular polar surface area as a sum of fragment-based contributions and its application to the prediction of drug transport properties. J. Med. Chem., 2000, 43, 3714-3717.
[81]
Veber, D.F.; Johnson, S.R.; Cheng, H.Y.; Smith, B.R.; Ward, K.W.; Kopple, K.D. Molecular properties that influence the oral bioavailability of drug candidates. J. Med. Chem., 2002, 45, 2615-2623.
[82]
Liu, H.; Wang, L.; Mingliang, L.M.; Pei, R.; Li, P.; Pei, Z.; Wang, Y.; Su, W.; Xie, X. AlzPlatform: An alzheimer’s disease domain-specific chemogenomics knowledgebase for polypharmacology and target identification research. J. Chem. Inf. Model., 2014, 54, 1050-1060.
[83]
Wager, T.T.; Hou, X.; Verhoest, P.R.; Villalobos, A. Central nervous system multiparameter optimization desirability: Application in drug discovery. ACS Chem. Neurosci., 2016, 7, 767-775.
[84]
Jensen, F. Introduction to computational chemistry; Jonh Wiley & Sons Ltd.: New York, 2007.
[85]
Govender, K.; Gao, J.; Naidoo, K.J. AM1/d-CB1: A semiempirical model for QM/MM simulations of chemical glycobiology systems. J. Chem. Theory Comput., 2014, 10, 4694-4707.
[86]
Gupta, S.; Kesarla, R.; Omri, A. Formulation strategies to improve the bioavailability of poorly absorbed drugs with special emphasis on self-emulsifying systems. ISRN Pharm., 2013, 2013, 16.