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For the HIV-1RT ligand-receptor binding, various linear response schemes are presented and discussed on this relatively large binding set, consisting of a total of 57 ligands. 74. 69. The further blind tests on seven mostly high-potent candidates (not originally in the training subset) also show very high accuracies. Finally, six new ligands are designed for optimal binding based on our predictions. The binding mechanism of this HIV-1RT receptor is also analyzed in detail. For the influenza antigen-receptor and antigen-antibody binding, the goal is to identify which mutations on the viral glycoprotein hemagglutinin (HA) might cause its receptor binding specificity to switch from avian to human, and which mutations might cause its escape of antibody neutralization, through large scale Free Energy Perturbation calculations.

5] J. Cartmell, S. Enoch, D. E. Des. 2005, 19, 821-833. [6] S. L. M. Davis, H. Sci. 2007, 4, 511-521. L. M. P. Tomkinson, H. Model. 2007, 47, 2401-2407. L. M. P. Tomkinson, H. van de Waterbeemd, An analysis of the predictivity of ADME QSAR models over time, in preparation. [9] H. Biodiv. 2009, 6, 1760-1766. 34 18th European Symposium on Quantitative Structure-Activity Relationships PLENARY LECTURE 6 THE BIOCHEMISTRY OF DRUG METABOLISM – WHICH ARE THE IMPORTANT REACTIONS AND ENZYMES? Bernard Testa1, Alessandro Pedretti2 and Giulio Vistoli2 Dept of Pharmacy, University Hospital Centre, Rue du Bugnon, CH-1011 Lausanne, Switzerland Dipartimento di Scienze Farmaceutiche “Pietro Pratesi”, Facoltà di Farmacia, Università degli Studi di Milano, Via Mangiagalli, I-20133 Milano, Italy 1 2 This lecture is not about QSAR, not even about SAR.

They combine a modicum of medium throughput IC50 training data (MTS) for the new kinase target, with a large database of historical kinase activities. The 2D “Profile QSAR” method builds conventional fragment-based QSARs for over 70 kinases, then uses the predicted activities as chemical descriptors for a PLS model of the new kinase. The resulting profile QSAR models, each now based on over 700,000 data points, predict affinity much better than the conventional QSARs on which they were based. The 3D Surrogate Ensemble AutoShim method uses the MTS data to adjust pharmacophore “shims” in a standardized “Universal Kinase Surrogate Receptor” (UKSR) ensemble of 16 diverse kinase crystal structures.

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