Synthetic library design.

Compound libraries have an important role in the drug discovery process. Various computational methods are available as decision support tools for medicinal chemists involved in compound library synthesis programs. These methods can be used to assemble a flexible library design scheme consisting of a structure-based library design followed by property-biased library refinement and final selection according to structure-activity-relationship considerations.

[1]  R. Austin,et al.  A UNIFIED MODEL FOR PREDICTING HUMAN HEPATIC, METABOLIC CLEARANCE FROM IN VITRO INTRINSIC CLEARANCE DATA IN HEPATOCYTES AND MICROSOMES , 2005, Drug Metabolism and Disposition.

[2]  G. Müller,et al.  Medicinal chemistry of target family-directed masterkeys. , 2003, Drug discovery today.

[3]  C. Lipinski.  Drug-like properties and the causes of poor solubility and poor permeability. , 2000, Journal of pharmacological and toxicological methods.

[4]  F. Lombardo,et al.  Experimental and computational approaches to estimate solubility and permeability in drug discovery and development settings , 1997 .

[5]  Li Xing,et al.  Influence of molecular flexibility and polar surface area metrics on oral bioavailability in the rat. , 2004, Journal of medicinal chemistry.

[6]  W. L. Jorgensen,et al.  Prediction of drug solubility from structure. , 2002, Advanced drug delivery reviews.

[7]  A. Vedani,et al.  In silico prediction of harmful effects triggered by drugs and chemicals. , 2005, Toxicology and applied pharmacology.

[8]  Stephen R. Johnson,et al.  Molecular properties that influence the oral bioavailability of drug candidates. , 2002, Journal of medicinal chemistry.

[9]  Y. Martin,et al.  A bioavailability score. , 2005, Journal of medicinal chemistry.

[10]  Robert P Bywater,et al.  Recognition of privileged structures by G-protein coupled receptors. , 2004, Journal of medicinal chemistry.

[11]  Juan J Perez,et al.  Managing molecular diversity. , 2005, Chemical Society reviews.

[12]  Jun Xu,et al.  Drug-like Index: A New Approach To Measure Drug-like Compounds and Their Diversity , 2000, J. Chem. Inf. Comput. Sci..

[13]  M Stahl,et al.  Structure-based library design: molecular modelling merges with combinatorial chemistry. , 2000, Current opinion in chemical biology.

[14]  Ferenc Darvas,et al.  In Silico and Ex silico ADME approaches for drug discovery. , 2002, Current topics in medicinal chemistry.

[15]  Michael S Lajiness,et al.  Molecular properties that influence oral drug-like behavior. , 2004, Current opinion in drug discovery & development.

[16]  M. Hutzler,et al.  Predicting drug-drug interactions in drug discovery: where are we now and where are we going? , 2005, Current opinion in drug discovery & development.

[17]  Hans Briem,et al.  Classifying “Kinase Inhibitor‐Likeness” by Using Machine‐Learning Methods , 2005, Chembiochem : a European journal of chemical biology.

[18]  A. Ghose,et al.  Atomic physicochemical parameters for three dimensional structure directed quantitative structure‐activity relationships III: Modeling hydrophobic interactions , 1988 .

[19]  A R Leach,et al.  Synergy between combinatorial chemistry and de novo design. , 2000, Journal of molecular graphics & modelling.

[20]  Ajay,et al.  Can we learn to distinguish between "drug-like" and "nondrug-like" molecules? , 1998, Journal of medicinal chemistry.

[21]  A. Ghose,et al.  Atomic Physicochemical Parameters for Three‐Dimensional Structure‐Directed Quantitative Structure‐Activity Relationships I. Partition Coefficients as a Measure of Hydrophobicity , 1986 .

[22]  Gabriele Cruciani,et al.  Predictive models for hERG potassium channel blockers. , 2005, Bioorganic & medicinal chemistry letters.

[23]  H. Kubinyi,et al.  A scoring scheme for discriminating between drugs and nondrugs. , 1998, Journal of medicinal chemistry.

[24]  D V S Green,et al.  Methods for library design and optimisation. , 2004, Mini reviews in medicinal chemistry.

[25]  Michael C Sanguinetti,et al.  Predicting drug-hERG channel interactions that cause acquired long QT syndrome. , 2005, Trends in pharmacological sciences.

[26]  C. Zechel,et al.  Combinatorial Synthesis of Small Organic Molecules , 1996 .

[27]  J. Krakover,et al.  High-throughput purification of compound libraries. , 2001, Drug discovery today.

[28]  P. Selzer,et al.  Fast calculation of molecular polar surface area as a sum of fragment-based contributions and its application to the prediction of drug transport properties. , 2000, Journal of medicinal chemistry.

[29]  Denis M. Bayada,et al.  Polar Molecular Surface as a Dominating Determinant for Oral Absorption and Brain Penetration of Drugs , 1999, Pharmaceutical Research.

[30]  R. Dolle.  Comprehensive survey of combinatorial library synthesis: 2004. , 2005, Journal of combinatorial chemistry.

[31]  Nagarajan Vaidehi,et al.  HierVLS hierarchical docking protocol for virtual ligand screening of large-molecule databases. , 2004, Journal of medicinal chemistry.

[32]  Tudor I. Oprea.  Current trends in lead discovery: Are we looking for the appropriate properties? , 2002, J. Comput. Aided Mol. Des..

[33]  G. Rishton.  Nonleadlikeness and leadlikeness in biochemical screening. , 2003, Drug discovery today.

[34]  T. Kennedy.  Managing the drug discovery/development interface , 1997 .

[35]  Christof Brändli,et al.  Automated equipment for high-throughput experimentation , 2003 .

[36]  Brian K. Shoichet,et al.  Virtual screening of chemical libraries , 2004, Nature.

[37]  Gordon M. Crippen,et al.  Atomic physicochemical parameters for three-dimensional-structure-directed quantitative structure-activity relationships. 2. Modeling dispersive and hydrophobic interactions , 1987, J. Chem. Inf. Comput. Sci..

[38]  I. Kola,et al.  Can the pharmaceutical industry reduce attrition rates? , 2004, Nature Reviews Drug Discovery.

[39]  Jan M. Kriegl,et al.  Prediction of Human Cytochrome P450 Inhibition Using Support Vector Machines , 2005 .