Going further than Lipinski's rule in drug design

Bibliography

• Lipinski C, Lombardo F, Dominy B, Feeney P. Experimental and computational approaches to estimate solubility and permeability in drug discovery and development settings. Adv Drug Deliv Rev 1997;23:3-25
   Web of Science ®Google Scholar
• Sneader W. Drug Discovery: A History. Wiley-Interscience; New York: 2005
   Google Scholar
• Sneader W. Drug Prototypes and their Exploitation. Chichester; New York: 1996
   Google Scholar
• Lajiness MS, Vieth M, Erickson J. Molecular properties that influence oral drug-like behavior. Curr Opin Drug Discov Devel 2004;7:470-7
   PubMed Web of Science ®Google Scholar
• Leeson PD, Davis AM. Time-related differences in the physical property profiles of oral drugs. J Med Chem 2004;47:6338-48
   PubMed Web of Science ®Google Scholar
• Leeson PD, Springthorpe B. The influence of drug-like concepts on decision-making in medicinal chemistry. Nat Rev Drug Discov 2007;6:881-90
   PubMed Web of Science ®Google Scholar
• Leeson P, St-Gallay S. Impact of ion class and time on oral drug molecular properties. Med Chem Commun 2011;2:91-105
   Web of Science ®Google Scholar
• Walters WP, Green J, Weiss JR, Murcko MA. What do medicinal chemists actually make? A 50-year retrospective. J Med Chem 2011;54:6405-16
   PubMed Web of Science ®Google Scholar
• Roughley SD, Jordan AM. The medicinal Chemist's toolbox: an analysis of reactions used in the pursuit of drug candidates. J Med Chem 2011;54:3451-79
   PubMed Web of Science ®Google Scholar
• World Drug Index. Available from: http://thomsonreuters.com/products_services/science/science_products/az/world_drug_index/
   Google Scholar
• Ghose AK, Viswanadhan VN, Wendoloski JJ. A knowledge-based approach in designing combinatorial or medicinal chemistry libraries for drug discovery. 1. A qualitative and quantitative characterization of known drug databases. J Comb Chem 1999;1:55-68
   PubMed Web of Science ®Google Scholar
• Oprea TI. Property distribution of drug-related chemical databases. J Comput Aided Mol Des 2000;14:251-64
   PubMed Web of Science ®Google Scholar
• Comrehensive Medicinal Chemistry Database. Available from: http://accelrys.com/products/databases/bioactivity/comprehensivemedicinal-chemistry.html
   Google Scholar
• MACCS Drug Data Report. Available from: http://accelrys.com/products/databases/bioactivity/mddr.html
   Google Scholar
• Feher M, Schmidt JM. Property distributions: differences between drugs, natural products, and molecules from combinatorial chemistry. J Chem Inf Model 2003;43:218-27
   Web of Science ®Google Scholar
• Lovering F, Bikker J, Humblet C. Escape from flatland: increasing saturation as an approach to improving clinical success. J Med Chem 2009;52:6752-6
   PubMed Web of Science ®Google Scholar
• Ritchie TJ, Macdonald SJF. The impact of aromatic ring count on compound developability–are too many aromatic rings a liability in drug design? Drug Discov Today 2009;14:1011-20
   PubMed Web of Science ®Google Scholar
• Luker T, Alcaraz L, Chohan KK, Strategies to improve in vivo toxicology outcomes for basic candidate drug molecules. Bioorg Med Chem Lett 2011;21:5673-9
   PubMed Web of Science ®Google Scholar
• Clemons PA, Bodycombe NE, Carrinski HA, Small molecules of different origins have distinct distributions of structural complexity that correlate with protein-binding profiles. Proc Natl Acad Sci USA 2010;107:18787-92
   PubMed Web of Science ®Google Scholar
• Dancik V, Seiler KP, Young DW, Distinct biological network properties between the targets of natural products and disease genes. J Am Chem Soc 2010;132:9259-61
   PubMed Web of Science ®Google Scholar
• Sadowski J, Kubinyi H. A scoring scheme for discriminating between drugs and nondrugs. J Med Chem 1998;41:3325-9
   PubMed Web of Science ®Google Scholar
• Ajay, Walters WP, Murcko MA. Can we learn to distinguish between “Drug-like” and ‘Nondrug-like’ molecules? J Med Chem 1998;41:3314-24
   PubMed Web of Science ®Google Scholar
• Gillet VJ, Willett P, Bradshaw J. Identification of biological activity profiles using substructural analysis and genetic algorithms. J Chem Inf Model 1998;38:165-79
   Web of Science ®Google Scholar
• Brustle M, Beck B, Schindler T, Descriptors, physical properties, and drug-likeness. J Med Chem 2002;45:3345-55
   PubMed Web of Science ®Google Scholar
• Li Q, Bender A, Pei J, Lai L. A large descriptor set and a probabilistic kernel-based classifier significantly improve druglikeness classification. J Chem Inf Model 2007;47:1776-86
   PubMed Web of Science ®Google Scholar
• Rayan A, Marcus D, Goldblum A. Predicting oral druglikeness by iterative stochastic elimination. J Chem Inf Model 2010;50:437-45
   PubMed Web of Science ®Google Scholar
• Murcia-Soler M, Perez-Gimenez F, Garcia-March FJ, Drugs and nondrugs: an effective discrimination with topological methods and artificial neural networks. J Chem Inf Model 2003;43:1688-702
   Web of Science ®Google Scholar
• Anzali S, Barnickel G, Cezanne B, Discriminating between drugs and nondrugs by prediction of activity spectra for substances (PASS). J Med Chem 2001;44:2432-7
   PubMed Web of Science ®Google Scholar
• Hutter MC. Separating drugs from nondrugs: a statistical approach using atom pair distributions. J Chem Inf Model 2007;47:186-94
   PubMed Web of Science ®Google Scholar
• Schneider N, Jackels C, Andres C, Hutter MC. Gradual in silico filtering for druglike substances. J Chem Inf Model 2008;48:613-28
   PubMed Web of Science ®Google Scholar
• Ursu O, Rayan A, Goldblum A, Oprea TI. Understanding drug-likeness. WIREs Comput Mol Sci 2011;1:760-81
   Google Scholar
• Proudfoot JR. Drugs, leads, and drug-likeness: an analysis of some recently launched drugs. Bioorg Med Chem Lett 2002;12:1647-50
   PubMed Web of Science ®Google Scholar
• Teague S, Davis A, Leeson P, Oprea T. The design of leadlike combinatorial libraries. Angew Chem Int Ed Engl 1999;38:3743-8
   PubMed Web of Science ®Google Scholar
• Hann M, Leach A, Harper G. Molecular complexity and its impact on the probability of finding leads for drug discovery. J Chem Inf Comput Sci 2001;41:856-64
   PubMedGoogle Scholar
• Oprea TI, Davis AM, Teague SJ, Leeson PD. Is there a difference between leads and drugs? A historical perspective. J Chem Inf Model 2001;41:1308-15
   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;53:2986-97
   PubMed Web of Science ®Google Scholar
• Hann MM, Oprea TI. Pursuing the leadlikeness concept in pharmaceutical research. Curr Opin Chem Biol 2004;8:255-63
   PubMed Web of Science ®Google Scholar
• Hajduk PJ, Greer J. A decade of fragment-based drug design: strategic advances and lessons learned. Nat Rev Drug Discov 2007;6:211-19
   PubMed Web of Science ®Google Scholar
• Congreve M, Carr R, Murray C, Jhoti H. A “rule of three” for fragment-based lead discovery? Drug Discov Today 2003;8:876-7
   PubMed Web of Science ®Google Scholar
• Hajduk PJ. Fragment-based drug design: how big is too big? J Med Chem 2006;49:6972-6
   PubMed Web of Science ®Google Scholar
• Congreve M, Chessari G, Tisi D, Woodhead AJ. Recent developments in fragment-based drug discovery. J Med Chem 2008;51:3661-80
   PubMed Web of Science ®Google Scholar
• Leach AR, Hann MM. Molecular complexity and fragment-based drug discovery: ten years on. Curr Opin Chem Biol 2011;15:489-96
   PubMed Web of Science ®Google Scholar
• Gleeson MP. Generation of a set of simple, interpretable ADMET rules of thumb. J Med Chem 2008;51:817-34
   PubMed Web of Science ®Google Scholar
• Waring MJ. Defining optimum lipophilicity and molecular weight ranges for drug candidates—molecular weight dependent lower logD limits based on permeability. Bioorg Med Chem Lett 2009;19:2844-51
   PubMed Web of Science ®Google Scholar
• Egan WJ, Merz KM Jr, Baldwin JJ. Prediction of drug absorption using multivariate statistics. J Med Chem 2000;43:3867-77
   PubMed Web of Science ®Google Scholar
• Johnson TW, Dress KR, Edwards M. Using the golden triangle to optimize clearance and oral absorption. Bioorg Med Chem Lett 2009;19:5560-4
   PubMed Web of Science ®Google Scholar
• Martin YC. A bioavailability score. J Med Chem 2005;48:3164-70
   PubMed Web of Science ®Google Scholar
• Veber D, Johnson S, Cheng H, Molecular properties that influence the oral bioavailability of drug candidates. J Med Chem 2002;45:2615-23
   PubMed Web of Science ®Google Scholar
• Lu JJ, Crimin K, Goodwin JT, Influence of molecular flexibility and polar surface area metrics on oral bioavailability in the rat. J Med Chem 2004;47:6104-7
   PubMed Web of Science ®Google Scholar
• Hou T, Wang J, Zhang W, Xu X. ADME evaluation in drug discovery. 6. Can oral bioavailability in humans be effectively predicted by simple molecular property-based rules? J Chem Info Model 2007;47:460-3
   PubMed Web of Science ®Google Scholar
• Hughes JD, Blagg J, Price DA, Physiochemical drug properties associated with in vivo toxicological outcomes. Bioorg Med Chem Lett 2008;18:4872-5
   PubMed Web of Science ®Google Scholar
• Price DA, Blagg J, Jones L, Physicochemical drug properties associated with in vivo toxicological outcomes: a review. Expert Opin Drug Metab Toxicol 2009;5:921-31
   PubMed Web of Science ®Google Scholar
• Pardridge WM. Biopharmaceutical drug targeting to the brain. J Drug Target 2010;18:157-67
   PubMed Web of Science ®Google Scholar
• van de Waterbeemd H, Kansy M. Hydrogen-bonding capacity and brain penetration. Chimia (Aarau) 1992;48:299-303
   Google Scholar
• Clark DE. Rapid calculation of polar molecular surface area and its application to the prediction of transport phenomena. 1. Prediction of intestinal absorption. J Pharm Sci 1999;88:807-14
   PubMed Web of Science ®Google Scholar
• Hitchcock S, Pennington L, Lin L, Lanza T Jr. Structure-brain exposure relationships. J Med Chem 2007;49:7559-83
   Web of Science ®Google Scholar
• Mehdipour AR, Hamidi M. Brain drug targeting: a computational approach for overcoming blood–brain barrier. Drug Discov Today 2009;14:1030-6
   PubMed Web of Science ®Google Scholar
• Abraham M. In Silico Models to Predict Brain Uptake. Virtual ADMET Assessment in Target Selection and Maturation. 2005
   Google Scholar
• Hou TJ, Xu XJ. ADME evaluation in drug discovery. 3. Modeling blood-brain barrier partitioning using simple molecular descriptors. J Chem Info comput sci 2003;43:2137-52
   PubMedGoogle Scholar
• Feng Y, Mitchison TJ, Bender A, Multi-parameter phenotypic profiling: using cellular effects to characterize small-molecule compounds. Nat Rev Drug Discov 2009;8:567-78
   PubMed Web of Science ®Google Scholar
• Dobson PD, Patel Y, Kell DB. “Metabolite-likeness” as a criterion in the design and selection of pharmaceutical drug libraries. Drug Discov Today 2009;14:31-40
   PubMed Web of Science ®Google Scholar
• Cherkasov A. Can ‘Bacterial-Metabolite-Likeness‘ model improve odds of ‘in Silico’ antibiotic discovery? J Chem Inf Model 2006;46:1214-22
   PubMed Web of Science ®Google Scholar
• Gupta S, Aires-de-Sousa J. Comparing the chemical spaces of metabolites and available chemicals: models of metabolite-likeness. Mol Divers 2007;11:23-36
   PubMed Web of Science ®Google Scholar
• Rishton G. Reactive compounds and in vitro false positives in HTS. Drug Discov Today 1997;9:382-4
   Google Scholar
• Walters WP, Stahl MT, Murcko MA. Virtual screening—an overview. Drug Discov Today 1998;3:160-78
   Web of Science ®Google Scholar
• Baell JB, Holloway GA. New substructure filters for removal of Pan Assay Interference Compounds (PAINS) from screening libraries and for their exclusion in bioassays. J Med Chem 2010;53:2719-40
   PubMed Web of Science ®Google Scholar
• Snodin DJ. Genotoxic impurities: from structural alerts to qualification. Org Process Res Dev 2010;14:960-76
   Web of Science ®Google Scholar
• Casalegno M, Benfenati E, Sello G. Identification of toxifying and detoxifying moieties for mutagenicity prediction by priority assessment. J Chem Inf Model 2011;51:1564-74
   PubMed Web of Science ®Google Scholar
• Ellison CM, Sherhod R, Cronin MTD, Assessment of methods to define the applicability domain of structural alert models. J Chem Inf Model 2011;51:975-85
   PubMed Web of Science ®Google Scholar
• Stepan AF, Walker DP, Bauman J, Structural alert/reactive metabolite concept as applied in medicinal chemistry to mitigate the risk of idiosyncratic drug toxicity: a perspective based on the critical examination of trends in the top 200 drugs marketed in the United States. Chem Res Toxicol 2011;24:1345-410
   PubMed Web of Science ®Google Scholar
• Valerio LG. In silico toxicology models and databases as FDA critical path initiative toolkits. Hum Genomics 2011;5:200-7
   PubMedGoogle Scholar
• Lipinski C. Lead- and drug-like compounds: the rule-of-five revolution. Drug Discov Today Technol 2004;1:337-41
   PubMedGoogle Scholar
• Bennani YL. Drug discovery in the next decade: innovation needed ASAP. Drug Discov Today 2011;16:779-92
   PubMed Web of Science ®Google Scholar
• Zhao H. Lead optimization in the nondrug-like space. Drug Discov Today 2011;16:158-63
   PubMed Web of Science ®Google Scholar
• Terrett NK. Methods for the synthesis of macrocycle libraries for drug discovery. Drug Discov Today Technol 2010;7:e97-e104
   Google Scholar
• Bird GH, Madani N, Perry AF, Hydrocarbon double-stapling remedies the proteolytic instability of a lengthy peptide therapeutic. Proc Natl Acad Sci USA 2010;107:14093-8
   PubMed Web of Science ®Google Scholar
• Dandapani S, Marcaurelle LA. Current strategies for diversity-oriented synthesis. Curr Opin Chem Biol 2010;14:362-70
   PubMed Web of Science ®Google Scholar
• Muster W, Breidenbach A, Fischer H, Computational toxicology in drug development. Drug Discov Today 2008;13:303-10
   PubMed Web of Science ®Google Scholar
• Fischer H, Atzpodien EA, Csato M, In-silico assay for assessing phospholipidosis potential of small drug like molecules: training, validation and refinement using several datasets. J Med Chem 2011; In press
   Web of Science ®Google Scholar
• Ursu O, Rayan A, Goldblum A, Oprea TI. Understanding drug-likeness. WIREs Comput Mol Sci 2011;1:760-81
   Google Scholar