QSAR in Development of Antidepressants

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Leads and biological activity IC 50 (nM) Drug TCAs imipramine nortriptyline protriptyline trimipramine Second generation Amoxapine mirtazapine venlafazine fluoxetine fluvoxamine paroxetine sertaline Norepinephrine 25 6 10 5000 25 40 300 200 500 70 300 5-HT 50 200 250 10000 600 20000 50 15 5 1 4
QSAR Design • Create training set – consisting of known antidepressant and biological activity • activity –log IC 50 – structures built in MOE – 20 current antidepressant • all descriptors for Cerius2 used – except alignment family • Generate QSAR equation – Genetic Function Approximation(GFA) • Activity independent variable • Descriptors dependent variable • Validate the equation – Test set • 5 other antidepressant with known biological activity • Predict activity – New leads
Page 1 and 2: The Next Generation of Antidepressa
Page 3 and 4: Depression as a Disease • 1 out 5
Page 5 and 6: Serotonin Hypothesis • serotonerg
Page 7 and 8: Serotonin effect Phil Skolnick, P.
Page 9: Project Aim • explore a faster ac
Page 13 and 14: Why use GFA • builds multiple mod
Page 15 and 16: • r 2 =.87 QSAR results • valid
Page 17 and 18: Strategy Nh 2 fluoxetine F Cl O H N
Page 19 and 20: Synthetic scheme S antidepressants
Page 21 and 22: Synthetic scheme
Page 23: References • Phil Skolnick, P. P.

QSAR in Development of Antidepressants

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