In silico tools used for compound selection during target-based drug discovery and development

Bibliography

• Swinney DC, Anthony J. How were new medicines discovered? Nat Rev Drug Discov 2011;10(7):507–19
   PubMed Web of Science ®Google Scholar
• Eder J, Sedrani R, Wiesmann C. The discovery of first-in-class drugs: origins and evolution. Nat Rev Drug Discov 2014;13(8):577–87
   PubMed Web of Science ®Google Scholar
• Berry IR, Martin RP. The pharmaceutical regulatory process. In: Robert P, editor. Drugs and the pharmaceutical sciences. 2nd edition. Informa Healthcare; New York, NY: 2008
   Google Scholar
• Sustaining Better Health For Patients Worldwide. (2004, October) - International Federation of Pharmaceutical Manufacturers Associations (IFPMA). Available from: http://www.who.int/intellectualproperty/Pharmaceutical_innovation.pdf
   Google Scholar
• Munos B. Lessons for 60 years of pharmaceutical innovation. Nat Rev Drug Discov 2009;8(12):959–68
   PubMed Web of Science ®Google Scholar
• Crowley LV. Essentials of human disease. Jones & Bartlett Publishers; Burlington, (MA): 2011
   Google Scholar
• Nicolaou KC. Advancing the drug discovery and development process. Angewandte Chemie Int Ed 2014;53(35):9128–40
   PubMed Web of Science ®Google Scholar
• Scannell JW, Blanckley A, Boldon H, Warrington B. Diagnosing the decline in pharmaceutical R&D efficiency. Nat Rev Drug Discov 2012;11(3):191–200
   PubMed Web of Science ®Google Scholar
• Paul SM, Mytelka DS, Dunwiddie CT, et al. How to improve R&D productivity: the pharmaceutical industry’s grand challenge. Nat Rev Drug Discov 2010;9(3):203–14
   PubMed Web of Science ®Google Scholar
• Pammolli F, Magazzini L, Riccaboni M. The productivity crisis in pharmaceutical R&D. Nat Rev Drug Discov 2011;10(6):428–38
   PubMed Web of Science ®Google Scholar
• Dimitri N. An assessment of R&D productivity in the pharmaceutical industry. Trends Pharmacol Sci 2011;32(12):683–5
   PubMed Web of Science ®Google Scholar
• Bunnage ME. Getting pharmaceutical R&D back on target. Nat Chem Biol 2011;7:335–9
   PubMed Web of Science ®Google Scholar
• Khanna I. Drug discovery in pharmaceutical industry: productivity challenges and trends. Drug Discov Today 2012;17(19–20):1088–102
   PubMed Web of Science ®Google Scholar
• Ringel M, Tollman P, Hersch G, Schulze U. Does size matter in R&D productivity? If not, what does? Nat Rev Drug Discov 2013;12(10):901–2
   PubMed Web of Science ®Google Scholar
• Cumming JG, Finlay MRV, Giordanetto F, et al. Potential strategies for increasing drug-discovery productivity. Future Med Chem 2014;6(5):515–27
   PubMed Web of Science ®Google Scholar
• Schulze U, Baedeker M, Chen YT, Greber D. R&D productivity: on the comeback trail. Nat Rev Drug Discov 2014;13(4):331–2
   PubMed Web of Science ®Google Scholar
• Caldwell GW. Compound optimization in early and late phase drug discovery: acceptable pharmacokinetics properties utilizing combined physicochemical, in vitro and in vivo screens. Curr Opin Drug Discov Devel 2000;3:30–41
   PubMedGoogle Scholar
• Caldwell GW, Ritchie DM, Masucci JA, et al. The new pre-preclinical paradigm: compound optimization in early and late phase drug discovery. Curr Top Med Chem 2001;1(5):353–66
   PubMedGoogle Scholar
• Caldwell GW, Yan Z. Screening for reactive intermediates and toxicity assessment in drug discovery. Curr Opin Drug Discov Devel 2006;9(1):47–60
   PubMed Web of Science ®Google Scholar
• Caldwell GW, Yan Z, Tang W, et al. ADME optimization and toxicity assessment in early- and late-phase drug discovery. Curr Top Med Chem 2009;9(11):965–80
   PubMed Web of Science ®Google Scholar
• Cook D, Brown D, Alexander R, et al. Lessons learned from the fate of AstraZeneca’s drug pipeline: a five-dimensional framework. Nat Rev Drug Discov 2014;13(6):419–31
   PubMed Web of Science ®Google Scholar
• Roberts RA, Kavanagh SL, Mellor HR, et al. Reducing attrition in drug development: smart loading preclinical safety assessment. Drug Discov Today 2014;19(3):341–7
   PubMed Web of Science ®Google Scholar
• Owens PK, Raddad E, Miller JW, et al. A decade of innovation in pharmaceutical R&D: the Chorus model. Nat Rev Drug Discov 2015;14(1):17–28
   PubMed Web of Science ®Google Scholar
• Southan C, Varkonyi P, Boppana K, et al. Tracking 20 Years of Compound-to-Target Output from Literature and Patents. PLoS One 2013;8(10):e77142
   PubMed Web of Science ®Google Scholar
• Chung TDY. Collaborative pre-competitive preclinical drug discovery with academics and pharma/biotech partners at sanford|burnham: infrastructure, capabilities & operational models. Comb Chem High Throughput Screen 2014;17(3):272–89
   PubMed Web of Science ®Google Scholar
• Houston JG, Banks MN, Binnie A. Case study: impact of technology investment on lead discovery at Bristol-Myers Squibb, 1998-2006. Drug Discov Today 2008;13(1-2):44–51
   PubMed Web of Science ®Google Scholar
• Janzen WP. Screening technologies for small molecule discovery: the state of the art. Chem Biol 2014;21(9):1162–70
   PubMed Web of Science ®Google Scholar
• Newman DJ, Cragg GM. Natural products as sources of new drugs over the 30 years from 1981 to 2010. J Nat Prod 2012;75(3):311–35
   PubMed Web of Science ®Google Scholar
• Lee JA, Uhlik MT, Moxham CM, et al. Modern phenotypic drug discovery is a viable, neoclassic pharma strategy. J Med Chem 2012;55(10):4527–38
   PubMed Web of Science ®Google Scholar
• Baumann A. Preclinical development of therapeutic biologics. Expert Opin Drug Discov 2008;3(3):289–97
   PubMed Web of Science ®Google Scholar
• Szekely-Klepser G, Kindt E. The role of biomarkers in drug discovery and development: enabling PK/PD. In: Lyubimov AV, Rodrigues AD, Sinz MA, editors. Encyclopedia of drug metabolism and interactions. John Wiley and Sons; Hoboken, (NJ): 2012. p. 577–98
   Google Scholar
• Azmi AS, Wang Z, Philip PA, et al. Proof of Concept: Network and Systems Biology Approaches Aid in the Discovery of Potent Anticancer Drug Combinations. Mol Cancer Ther 2010;9(12):3137–44
   PubMed Web of Science ®Google Scholar
• O’Brien PJ. High-content analysis in toxicology: screening substances for human toxicity potential, elucidating subcellular mechanisms and in vivo use as translational safety. Basic Clin Pharmacol Toxicol 2014;115(1):4–17
   PubMed Web of Science ®Google Scholar
• Mullane K, Winquist RJ, Williams M. Translational paradigms in pharmacology and drug discovery. Biochem Pharmacol 2014;87(1):189–210
   PubMed Web of Science ®Google Scholar
• Reddy AS, Zhang S. Polypharmacology: drug discovery for the future. Expert Rev Clin Pharmacol 2013;6(1):41–7
   PubMed Web of Science ®Google Scholar
• Patwardhan B, Vaidya ADB, Chorghade M, Joshi SP. Reverse pharmacology and systems approaches for drug discovery and development. Curr Bioactive Compounds 2008;4(4):201–12
   Google Scholar
• Bal-Price A, Jennings P. editors. In vitro toxicology systems. Humana Press; Totowa, (NJ): 2014
   Google Scholar
• Keohavong P, Grant SG. editors. Molecular toxicology protocols. Humana Press; Totowa, (NJ): 2014
   Google Scholar
• Caldwell GW, Yan Z. editors. Optimization in Drug discovery: in vitro methods 2. Humana Press; Totowa, (NJ): 2014
   Google Scholar
• Yan Z, Caldwell GW. editors. Optimization in drug discovery: in vitro methods 1. Humana Press; Totowa, (NJ): 2004
   Google Scholar
• Ou-Yang S-S, Lu J-Y, Kong X-Q, et al. Computational drug discovery. Acta Pharmacol Sin 2012;33(9):1131–40
   PubMed Web of Science ®Google Scholar
• Johnston SC, Hauser SL, Desmond-Hellmann S. Enhancing ties between academia and industry to improve health. Nat Med 2011;17(4):434–6
   PubMed Web of Science ®Google Scholar
• Karsdal MA, Henriksen K, Leeming DJ. Biochemical markers and the FDA Critical Path: How biomarkers may contribute to the understanding of pathophysiology and provide unique and necessary tools for drug development. Biomarkers 2009;14(3):181–202
   PubMed Web of Science ®Google Scholar
• Rogatko A, Babb JS, Tighiouart M, et al. New paradigm in dose-finding trials: patient-specific dosing and beyond phase I. Clin Cancer Res 2005;11(15):5342–6
   PubMed Web of Science ®Google Scholar
• Lindborg SR, Persinger CC, Sashegyi A, et al. Statistical refocusing in the design of Phase II trials offers promise of increased R&D productivity. Nat Rev Drug Discov 2014;13(8):638–40
   PubMed Web of Science ®Google Scholar
• Butler MS, Robertson AAB, Cooper MA. Natural product and natural product derived drugs in clinical trials. Nat Prod Rep 2014;31(11):1612–61
   PubMed Web of Science ®Google Scholar
• US Food and Drug Administration. Innovation or stagnation: challenge and opportunity on the critical path to new medical products. 2004. Available from: http://www.fda.gov/ScienceResearch/SpecialTopics/CriticalPathInitiative/CriticalPathOpportunitiesReports/ucm077262.htm
   Google Scholar
• Bamberger M, Moore N, Lechat P. How to improve the clinical development paradigm and its division into phases I, II and III. Therapie 2011;66(4):327–34
   Web of Science ®Google Scholar
• Grootjans-van Kampen I, Engelfriet PM, van Baal PHM. Disease prevention: saving lives or reducing health care costs? PLoS ONE 2014;9(8):e104469
   PubMed Web of Science ®Google Scholar
• Parker-Lue S, Santoro M, Koski G. The ethics and economics of pharmaceutical pricing. Annu Rev Pharmacol Toxicol 2015;55:191–206
   PubMed Web of Science ®Google Scholar
• Koenig J. Does process excellence handcuff drug development? Drug Discov Today 2011;16(9-10):377–81
   PubMed Web of Science ®Google Scholar
• Schumock GT, Li EC, Suda KJ, et al. National trends in prescription drug expenditures and projections for 2014. Am J Health Syst Pharm 2014;71(6):482–99
   PubMed Web of Science ®Google Scholar
• Kaplan RM, Babad YM. Balancing influence between actors in healthcare decision making. BMC Health Serv Res 2011;11(85):1–14
   PubMedGoogle Scholar
• Rutger D, Edith M, Maneesha M, et al. Pharmaceutical portfolio management: global disease burden and corporate performance metrics. Int Soc Pharmacoeconomics Outcomes Res 2014;17(6):732–8
   Web of Science ®Google Scholar
• LaMattina JL. The impact of mergers on pharmaceutical R&D. Nat Rev Drug Discov 2011;10(8):559–60
   PubMed Web of Science ®Google Scholar
• Arrowsmith J. A decade of change. Nat Rev Drug Discov 2012;11(1):17–18
   PubMed Web of Science ®Google Scholar
• Watson RR. editor. In vitro methods of toxicology. CRC Press; Boca Raton, (FL): 1992
   Google Scholar
• O’Hare S, Atterwill CK. editors. In vitro toxicity testing protocols. Humana Press; Totowa, (NJ): 1995
   Google Scholar
• Borchart RT, Smith PL, Wilson G. editors. Models for assessing drug absorption and metabolism. Plenum Press; New York (NY): 1996
   Google Scholar
• Yalkowsky SH. editor. Solubility and solubilization in aqueous media. Oxford University Press; New York (NY): 1999
   Google Scholar
• Woolf TF. editor. Handbook of drug metabolism. Marcel Dekker; New York (NY): 1999
   Google Scholar
• Gad SC. editor. Preclinical development handbook; ADME and Biopharmaceutical Properties. John Wiley and Sons; Hoboken, (NJ): 2008
   Google Scholar
• van de Waterbeemd H, Testa B. editors. Drug bioavailability; estimation of solubility, permeability, absorption and bioavailability. Wiley-VCH Verlag GmbH & Co. KGaA; Weinheim (Germany): 2009
   Google Scholar
• Dressman JB, Reppas C. editors. Oral drug absorption: prediction and assessment. Informa Healthcare; New York, (NY): 2010
   Google Scholar
• Tihanyi K, Vastag M. editors. Solubility, delivery and ADME problems of drugs and drug candidates. United Arab Emirates Bentham Science Publishers; Sharjah: 2011
   Google Scholar
• Zhang D, Surapaneni S. editors. ADME-enabling technologies in drug design and development. John Wiley and Sons; Hoboken, (NJ): 2012
   Google Scholar
• Avdeef A. editor. Absorption and drug development: solubility, permeability, and charge state. John Wiley and Sons; Hoboken, (NJ): 2012
   Google Scholar
• Leeson PD, St-Gallay SA. The influence of the ’organizational factor’ on compound quality in drug discovery. Nat Rev Drug Discov 2011;10(10):749–65
   PubMed Web of Science ®Google Scholar
• Hartung T. Look back in anger - what clinical studies tell us about preclinical work. ALTEX 2013;30(3):275–91
   PubMedGoogle Scholar
• Hughes JP, Rees S, Kalindjian SB, Philpott KL. Principles of early drug discovery. Br J Pharmacol 2011;162(6):1239–49
   PubMed Web of Science ®Google Scholar
• Caldwell GW, Yan Z, Masucci JA, et al. Applied pharmacokinetics in drug development. Pharmaceut Devel Regul 2003;1(2):117–32
   Google Scholar
• Guido RVC, Oliva G, Andricopulo AD. Modern drug discovery technologies: opportunities and challenges in lead discovery. Comb Chem High Throughput Screen 2011;14(10):830–9
   PubMed Web of Science ®Google Scholar
• Titov DV, Liu JO. Identification and validation of protein targets of bioactive small molecules. Bioorgan Med Chem 2012;20(6):1902–9
   PubMed Web of Science ®Google Scholar
• Sharma N, Harikumar SL. Use of genomics and proteomics in pharmaceutical drug discovery and development: a review. Int J Pharm Pharmaceut Sci 2013;5(3):24–8
   Google Scholar
• Squarize CH, Castilho RM, Leopoldino AM. Drug target validation. Curr Methods Med Chem Biol Phy 2008;2:167–85
   Google Scholar
• Ekimler S, Sahin K. Computational methods for microRNA target prediction. Genes 2014;5(3):671–83
   Web of Science ®Google Scholar
• Taboureau O, Baell JB, Fernandez-Recio J, Villoutreix BO. Established and emerging trends in computational drug discovery in the structural genomics era. Chem Biol 2012;19(1):29–41
   PubMed Web of Science ®Google Scholar
• Liu H-F, Shen Q, Zhang J, Fu W. Evaluation of various inverse docking schemes in multiple targets identification. J Mol Graph Model 2010;29(3):326–30
   PubMed Web of Science ®Google Scholar
• Eric S, Ke S, Barata T, et al. Target fishing and docking studies of the novel derivatives of aryl-aminopyridines with potential anticancer activity. Bioorgan Med Chem 2012;20(17):5220–8
   PubMed Web of Science ®Google Scholar
• Koutsoukas A, Simms B, Kirchmair J, et al. From in silico target prediction to multi-target drug design: current databases, methods and applications. J Proteomics 2011;74(12):2554–257
   PubMed Web of Science ®Google Scholar
• Janzen WP. Screening technologies for small molecule discovery: the state of the art. Chem Biol 2014;21(9):1162–70
   PubMed Web of Science ®Google Scholar
• Macarron R, Banks MN, Bojanic D, et al. Impact of high-throughput screening in biomedical research. Nat Rev Drug Discov 2011;10(3):188–95
   PubMed Web of Science ®Google Scholar
• Cuozzo JW, Soutter H H. Overview of recent progress in protein-expression technologies for small-molecule screening. J Biomol Screen 2014;19(7):1000–13
   PubMed Web of Science ®Google Scholar
• Huggins DJ, Venkitaraman AR, Spring DR. Rational methods for the selection of diverse screening compounds. ACS Chem Biol 2011;6(3):208–17
   PubMed Web of Science ®Google Scholar
• Ramsay DL, Kwasnoski JD, Caldwell GW. Small molecule compound logistics outsourcing - going beyond the “thought experiment”. Curr Top Med Chem 2012;12(11):1298–308
   PubMed Web of Science ®Google Scholar
• Besnard J, Jones PS, Hopkins AL, Pannifer AD. The Joint European Compound Library: boosting precompetitive research. Drug Discov Today 2014; Ahead of Press
   PubMed Web of Science ®Google Scholar
• Fink T, Reymond J-L. Virtual exploration of the chemical universe up to 11 atoms of C, N, O, F: assembly of 26.4 million structures (110.9 million stereoisomers) and analysis for new ring systems, stereochemistry, physicochemical properties, compound classes, and drug discovery. J Chem Inf Mod 2007;47(2):342–53
   PubMed Web of Science ®Google Scholar
• Gubler H, Schopfer U, Jacoby E. Theoretical and experimental relationships between percent inhibition and IC50 data observed in high-throughput screening. J Biomol Screen 2013;18(1):1–13
   PubMed Web of Science ®Google Scholar
• Shun T Y, Lazo JS, Sharlow ER, Johnston PA. Identifying actives from HTS data sets: practical approaches for the selection of an appropriate HTS data-processing method and quality control review. J Biomol Screen 2011;16:1–14
   PubMed Web of Science ®Google Scholar
• Sink R, Gobec S, Pecar S, Zega A. False positives in the early stages of drug discovery. Curr Med Chem 2010;17(34):4231–55
   PubMed Web of Science ®Google Scholar
• Ortega SS, Cara LCL, Salvador MK. In silico pharmacology for a multidisciplinary drug discovery process. Drug Metabol Drug Interact 2012;27(4):199–207
   PubMedGoogle Scholar
• Leung K-, Liu L-J, Lin S, et al. Discovery of a small-molecule inhibitor of STAT3 by ligand-based pharmacophore screening. Methods 2015;71:38–43
   PubMed Web of Science ®Google Scholar
• Hoi PM, Li S, Vong CT, et al. Recent advances in structure-based drug design and virtual screening of VEGFR tyrosine kinase inhibitors. Methods 2015;71:85–91
   PubMed Web of Science ®Google Scholar
• Phatak SS, Stephan CC, Cavasotto CN. High-throughput and in silico screening in drug discovery. Expert Opin Drug Discov 2009;4(9):947–59
   PubMed Web of Science ®Google Scholar
• Ripphausen P, Nisius B, Peltason L, Bajorath J. Quo vadis, virtual screening? a comprehensive survey of prospective applications. J Med Chem 2010;53(24):8461–7
   PubMed Web of Science ®Google Scholar
• Ripphausen P, Nisius B, Bajorath J. State-of-the-art in ligand based virtual screening. Drug Discov Today 2011;16:372–376
   PubMed Web of Science ®Google Scholar
• Stumpfe D, Ripphausen P, Bajorath J. Virtual compound screening in drug discovery. Future Med Chem 2012;4:593–602
   PubMed Web of Science ®Google Scholar
• Gu W-G, Liu B-N, Yuan J-F. Virtual-screening targeting Human Immunodeficiency Virus type 1 integrase-lens epithelium-derived growth factor/p75 interaction for drug development. J Drug Target 2015;23(2):134–9
   PubMed Web of Science ®Google Scholar
• Sun L, Qian H, Huang W. Virtual screening for cholesterol absorption inhibitors. Med Chem 2015;11(1):2–12
   Web of Science ®Google Scholar
• Leung C-H, Ma D-L. Recent advances in virtual screening for drug discovery. Methods 2015;71:1–3
   PubMed Web of Science ®Google Scholar
• Parentt MD, Rastelli G. Advances and applications of binding affinity predictions methods in drug discovery. Biotechnol Adv 2012;30:244–50
   PubMed Web of Science ®Google Scholar
• Zhu T, Cao S, Su P-C, et al. Hit identification and optimization in virtual screening: practical recommendations based on a critical literature analysis. J Med Chem 2013;56(17):6560–72
   PubMed Web of Science ®Google Scholar
• Shult MD. Improving the plausibility of success with inefficient metrics. Med Chem Lett 2013;5:2–5
   Web of Science ®Google Scholar
• Cho YS, Kwon HJ. Identification and validation of bioactive small molecule target through phenotypic screening. Bioorgan Med Chem 2012;20(6):1922–8
   PubMed Web of Science ®Google Scholar
• Lee JA, Uhlik MT, Moxham CM, et al. Modern phenotypic drug discovery is a viable, neoclassic pharma strategy. J Med Chem 2012;55(10):4527–38
   PubMed Web of Science ®Google Scholar
• Moffat JG, Rudolph J, Bailey D. Phenotypic screening in cancer drug discovery - past, present and future. Nat Rev Drug Discov 2014;13(8):588–602
   PubMed Web of Science ®Google Scholar
• Polyakov VR, Moorcroft ND, Drawid A. Enrichment analysis for discovering biological associations in phenotypic screens. J Chem Inf Model 2014;54(2):377–86
   PubMed Web of Science ®Google Scholar
• Keseru GM, Makara GM. The influence of lead discovery strategies on the properties of drug candidates. Nat Rev Drug Discov 2009;8(3):203–12
   PubMed Web of Science ®Google Scholar
• Balbach S, Korn C. Pharmaceutical evaluation of early development candidates “the 100 mg-approach”. Int J Pharm 2004;275(1-2):1–12
   PubMed Web of Science ®Google Scholar
• Hann MM, Keseru GM. Finding the sweet spot: the role of nature and nurture in medicinal chemistry. Nat Rev Drug Discov 2012;11:355–65
   PubMed Web of Science ®Google Scholar
• Lipkin MJ, Stevens AP, Livingstone DJ, Harris CJ. How large does a compound screening collection need to be? Comb Chem High Throughput Screen 2008;11(6):482–93
   PubMed Web of Science ®Google Scholar
• Caldwell GW, Yan Z, Lang W, Masucci J. The IC50 concept revisited. Curr Top Med Chem 2012;12(11):1282–90
   PubMed Web of Science ®Google Scholar
• Shultz MD. Setting expectations in molecular optimizations: Strengths and limitations of commonly used composite parameters. Bioorgan Med Chem Lett 2013;23(21):5980–91
   PubMed Web of Science ®Google Scholar
• Perola E. An analysis of the binding efficiencies of drugs and their leads in successful drug discovery programs. J Med Chem 2010;53(7):2986–97
   PubMed Web of Science ®Google Scholar
• Walters WP, Green J, Weiss JR, Murcko MA. What do chemists actually make? A 50-year retrospective. J Med Chem 2011;54(19):6405–16
   PubMed Web of Science ®Google Scholar
• Lajiness MS, Maggiora GM, Shanmugasundaram V. Assessment of the consistency of medicinal chemists in reviewing sets of compounds. J Med Chem 2004;47(20):4891–6
   PubMed Web of Science ®Google Scholar
• Kutchukian PS, Vasilyeva NY, Xu J, et al. Inside the mind of a medicinal chemist: the role of human bias in compound prioritization during drug discovery. PLoS One 2012;7(11):e48476
   PubMed Web of Science ®Google Scholar
• Bemis GW, Murcko MA. The properties of known drugs.1. Molecular frameworks. J Med Chem 1996;39(15):2887–93
   PubMed Web of Science ®Google Scholar
• Hopkins AL, Keseru GM, Leeson D, et al. The role of ligand efficiency metrics in drug discovery. Nat Rev Drug Discov 2014;13(2):105–21
   PubMed Web of Science ®Google Scholar
• Johnson AT. A prospective method to guide small molecule drug design. J Chem Educ 2014;10.1021/ed5002653
   Web of Science ®Google Scholar
• Tarcsay A, Nyiri K, Keseru GM. Impact of lipophilic efficiency on compound quality. J Med Chem 2012;55(3):1252–60
   PubMed Web of Science ®Google Scholar
• Arnott JA, Kumar R, Planey SL. Lipophilicity indices for drug development. J Appl Biopharm Pharmacokinet 2013;1(1):31–6
   Google Scholar
• Bickerton GR, Paolini GV, Besnard J, et al. Quantifying the chemical beauty of drugs. Nat Chem 2012;4(2):90–8
   PubMed Web of Science ®Google Scholar
• Dobchev DA, Pillai GG, Karelson M. In silico machine learning methods in drug development. Curr Top Med Chem 2014;14(16):1913–22
   PubMed Web of Science ®Google Scholar
• Cramer RD. The inevitable QSAR renaissance. J Comput Aided Mol Des 2012;26(1):35–8
   PubMed Web of Science ®Google Scholar
• Cumming JG, Davis AM, Muresan S, et al. Chemical predictive modelling to improve compound quality. Nat Rev Drug Discov 2013;12(12):948–62
   PubMed Web of Science ®Google Scholar
• Davis AM, Wood DJ. Quantitative structure-activity relationship models that stand the test of time. Mol Pharm 2013;10(4):1183–90
   PubMed Web of Science ®Google Scholar
• Dearden JC. Expert systems for toxicity prediction. In: Cronin MTD, Madden JC, editors. In silico toxicology. RSC Publishing; Cambridge (UK): 2010. p. 478–507
   Google Scholar
• Arnott JA, Planey SL. The influence of lipophilicity in drug discovery and design. Expert Opin Drug Discov 2012;7(10):863–75
   PubMed Web of Science ®Google Scholar
• Ortwine DF, Aliagas I. Physicochemical and DMPK in silico models: facilitating their use by medicinal chemists. Mol Pharm 2013;10(4):1153–61
   PubMed Web of Science ®Google Scholar
• Cherkasov A, Muratov EN, Fourches D, et al. QSAR modeling: where have you been? where are you going. J Med Chem 2014;57(12):4977–5010
   PubMed Web of Science ®Google Scholar
• Wang L, Wu Y, Deng Y, et al. Accurate and reliable prediction of relative ligand binding potency in prospective drug discovery by way of a modern free-energy calculation protocol and force field. J Am Chem Soc 2015;137(7):2695–703
   PubMed Web of Science ®Google Scholar
• Mishra M, Mishra VK, Senger P, et al. Exploring QSAR studies on 4-substituted quinazoline derivatives as antimalarial compounds for the development of predictive models. Med Chem Res 2014;23(3):1397–405
   Web of Science ®Google Scholar
• Dearden JC. In silico prediction of aqueous solubility. Expert Opin Drug Discov 2006;1(1):31–52
   PubMed Web of Science ®Google Scholar
• Hewitt M, Cronin MTD, Enoch S.J, et al. In silico prediction of aqueous solubility: the solubility challenge. J Chem Inf Model 2009;49(11):2572–87
   PubMed Web of Science ®Google Scholar
• Chevillard F, Lagorce D, Reynes C, et al. In silico prediction of aqueous solubility: a multimodel protocol based on chemical similarity. Mol Pharm 2012;9(11):3127–35
   PubMed Web of Science ®Google Scholar
• Wenlock MC, Carlsson LA. How experimental errors influence drug metabolism and pharmacokinetic QSAR/QSPR models. J Chem Inf Model 2015;55(1):125–34
   PubMed Web of Science ®Google Scholar
• Beck B, Geppert T. Industrial applications of in silico ADMET. J Mol Model 2014;20(7):1–15
   Web of Science ®Google Scholar
• Honorio KM, Moda TL, Andricopulo AD. Pharmacokinetic properties and in silico ADME modeling in drug discovery. Med Chem 2013;9(2):163–76
   PubMed Web of Science ®Google Scholar
• Cheng F, Li W, Liu G, Tang Y. In silico ADMET prediction: recent advances, current challenges and future trends. Curr Top Med Chem 2013;13(11):1273–89
   PubMed Web of Science ®Google Scholar
• Gleeson MP, Hersey A, Hannongbua S. In-silico ADME models: a general assessment of their utility in drug discovery applications. Curr Top Med Chem 2011;11(4):358–81
   PubMed Web of Science ®Google Scholar
• Yan Z, Caldwell GW. The current status of time dependent CYP inhibition assay and in silico drug drug interaction predictions. Curr Top Med Chem 2012;12(11):1291–7
   PubMed Web of Science ®Google Scholar
• Gleeson MP, Montanari D. Strategies for the generation, validation and application of in silico ADMET models in lead generation and optimization. Expert Opin Drug Metabol Toxicol 2012;8(11):1435–46
   PubMed Web of Science ®Google Scholar
• Jones HM, Mayawala K, Poulin P. Dose selection based on physiologically based pharmacokinetic (PBPK) approaches. AAPS J 2013;15(2):377–87
   PubMed Web of Science ®Google Scholar
• Shelia AP. editor. Physiologically-based pharmacokinetic (PBPK) modeling and simulations principles, methods, and applications in the pharmaceutical industry. Nature Publishing Group; London, UK: 2013
   Google Scholar
• Lipscomb JC, Haddad S, Poet T, Krishnan K. Physiologically-based pharmacokinetic (PBPK) models in toxicity testing and risk assessment. Adv Exp Med Biol 2012;745:76–95
   PubMed Web of Science ®Google Scholar
• Jones HM, Dickins M, Youdim K, et al. Application of PBPK modelling in drug discovery and development at Pfizer. Xenobiotica 2012;42(1):94–106
   PubMed Web of Science ®Google Scholar
• Wunberg T, Hendrix M, Hillisch A, et al. Improving the hit-to-lead process: data-driven assessment of drug-like and lead-like screening hits. Drug Discov Today 2006;11(3-4):175–80
   PubMed Web of Science ®Google Scholar
• Keseru GM, Makara GM. Hit discovery and hit-to-lead approaches. Drug Discov Today 2006;11(15-16):741–8
   PubMed Web of Science ®Google Scholar
• Manly CJ, Chandrasekhar J, Ochterski JW, et al. Strategies and tactics for optimizing the Hit-to-Lead process and beyond--a computational chemistry perspective. Drug Discov Today 2008;13(3-4):99–109
   PubMed Web of Science ®Google Scholar
• Duffy BC, Zhu L, Decornez H, Kitchen DB. Early phase drug discovery: Cheminformatics and computational techniques in identifying lead series. Bioorgan Med Chem 2012;20(18):5324–42
   PubMed Web of Science ®Google Scholar
• Segall M. Multi-parameter optimization in drug discovery. Innov Pharm Technol 2011;39:42–6
   Google Scholar
• Wagner TT, Hou X, Verhoest PR, Villalobos A. Moving beyond rules: the development of a central nervous system multiparameter optimization (CNS MPO) approach to enable alignment of druglike properties. ACS Chem Neurosci 2010;1(6):435–49
   PubMed Web of Science ®Google Scholar
• Ritchie TJ, McLay IM. Should medicinal chemists do molecular modelling? Drug Discov Today 2012;17(11-12):534–7
   PubMed Web of Science ®Google Scholar