LEADER 03633nam 2200661Ia 450 001 9910450847803321 005 20200520144314.0 010 $a1-280-81056-4 010 $a9786610810567 010 $a1-4294-6772-X 010 $a1-60750-219-4 010 $a600-00-0604-7 010 $a1-4337-0155-3 035 $a(CKB)1000000000340132 035 $a(EBL)287003 035 $a(OCoLC)437176685 035 $a(SSID)ssj0000267733 035 $a(PQKBManifestationID)11195663 035 $a(PQKBTitleCode)TC0000267733 035 $a(PQKBWorkID)10211843 035 $a(PQKB)10057265 035 $a(MiAaPQ)EBC287003 035 $a(Au-PeEL)EBL287003 035 $a(CaPaEBR)ebr10167400 035 $a(CaONFJC)MIL81056 035 $a(EXLCZ)991000000000340132 100 $a20061115d2006 uy 0 101 0 $aeng 135 $aur|n|---||||| 181 $ctxt 182 $cc 183 $acr 200 00$aVirtual ADMET assessment in target selection and maturation$b[electronic resource] /$fedited by B. Testa and L. Turski 210 $aAmsterdam ;$aWashington, DC $cIOS Press$dc2006 215 $a1 online resource (268 p.) 225 1 $aSolvay Pharmaceuticals Conferences,$x1566-7685 ;$vv. 6 300 $aOrganized May 11-13th, 2005 in Lucerne, Switzerland. 311 $a1-58603-703-X 320 $aIncludes bibliographical references and index. 327 $aPreface; List of Contributors; Contents; Conference Preface; The Risky Business of Developing Drugs; Benefits and Limits of in Silico Predictions; Musings on ADME Predictions and Molecular Structure; Lipophilicity: Its Calculation and Application in ADMET Predictions; Interpretation of the Role of the Electrotopological State and Molecular Connectivity Indices in the Prediction of Physical Properties and ADME-Tox Behavior - Case Study: Human Plasma Protein Binding; Molecular Descriptors for Predicting ADMET Properties; Molecular Fields to Assess Recognition Forces and Property Spaces 327 $aExtracting Pharmacophores from Bio-Active MoleculesIn Silico Models for Human Bioavailability; In Silico Models to Predict Brain Uptake; Algorithms to Predict Affinity for Transporters; Predicting Affinity for and Metabolism by Cytochromes P450; Expert Systems to Predict Biotransformation; Expert Systems to Predict Toxicity; From in Vivo to in Vitro/in Silico ADME: Progress and Challenges; Author Index 330 $aToday, biologists and medicinal chemists realize that there is a strong relationship between pharmacodynamic (what the drug does to the organism) and pharmacokinetic (what the organism does to the drug) effects. A significant contributing factor to the evolution in drug discovery was the methodological and technological revolution with the advent of combinatorial chemistry, high-throughput screening and profiling, and in silico prediction of target-based activity and ADMET (absorption, distribution, metabolism, excretion and toxicity) properties. High-throughput screening and in silico methods 410 0$aSolvay Pharmaceuticals Conferences ;$vv. 6. 606 $aDrug development$xComputer simulation$vCongresses 606 $aPharmacology 608 $aElectronic books. 615 0$aDrug development$xComputer simulation 615 0$aPharmacology. 676 $a615/.190285 701 $aTesta$b Bernard$0290760 701 $aTurski$b Lechoslaw$0915809 801 0$bMiAaPQ 801 1$bMiAaPQ 801 2$bMiAaPQ 906 $aBOOK 912 $a9910450847803321 996 $aVirtual ADMET assessment in target selection and maturation$92053054 997 $aUNINA