By Han van de Waterbeemd, Raimund Mannhold, Povl Krogsgaard-Larsen, Hendrik Timmerman
Using robust desktops has revolutionized molecular layout and drug discovery. completely researched and well-structured, this complete instruction manual covers powerful and effective concepts in 3D-QSAR and complicated statistical research. The emphasis is on displaying clients the right way to follow those equipment and steer clear of expensive and time-consuming methodical error. subject matters lined comprise * blend of statistical tools and molecular modeling instruments * rational use of databases * complex statistical concepts * neural networks and specialist platforms in molecular layout This booklet addresses the practitioner in and learn, in addition to the amateur wishing to develop into accustomed to sleek instruments in medicinal chemistry.
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Additional info for Advanced Computer-Assisted Techniques in Drug Discovery (Methods and Principles in Medicinal Chemistry, Vol 3)
Med. Chem. , Di Pace, L. Mol. Des. 1 Chemometrics and Molecular Modeling Demetrio Pitea, Ugo Cosentino, Giorgio Moro, Laura Bonati, Elena Fraschini, Murina Lasagni, and Roberto Todeschini Abbreviations 5 -Hydroxytryptamine Arylhydrocarbon Angiotensin IT Comparative Molecular Field Analysis 50% Effective Concentration Ec50 Generating Optimal Linear PLS Estimations GOLPE Highest Occupied Molecular Orbital HOMO 50% Inhibitory Dose IDSO Affinity constant Kd HMG-COA 3-Hydroxy-3-methylglutarylCoenzyme A Linear Discriminant Analysis LDA Linear Discriminant Classification Tree LDCT Lowest Unoccupied Molecular Orbital LUMO Monochlorinated Dibenzo-p-dioxins MCDD Molecular Electrostatic Potential MEP Principal Component PC Principal Component Analysis PCA Polychlorinated Dibenzo-p-dioxins PCDD Potential Energy Surface PES Partial Least Squares PLS Quantitative Structure-Activity Relationships QSAR Squared Correlation Coefficient r2 2 Cross-validated Squared Correlation Coefficient rcv Regularized Discriminant Analysis RDA Structure-Activity Relationships SAR 5-HT Ah A1I CoMFA 10 SCF-HF SD TCDD TrCDD D.
874 . 99MEP(d) Models with only one variable have been obtained by the least squares method. and 2 (Table 5), makes it possible to rationalize the global model on the basis of the characteristics of the two sets. The best models for Set 1 take into account the MEP features in both the lateral regions, as summarized by y' and 6' and high affinity values are related to negative MEP values at both these points. The best models for Set 2 are given solely by the 6 or y variable: binding affinity increases as the MEP value in 6 increases (or as the MEP value in y decreases).
Misclassified conformations are moved to the corresponding calculated classes. The iterative process is stopped when the classification of the active confor- 36 D. Pitea et al. mations no longer changes in two successive steps. e. the presence of different models for the geometric pharmacophore within the active class. LDCT consists in the combined use of Linear Discriminant Analysis, LDA, [16, 171 see Volume 2, and tree classification methods [18, 191. The tree structure is composed of nodes and leaves, the starting node being the initial estimate.