C372_L11 - Molecular Modeling: Statistical Analysis of...

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Unformatted text preview: Molecular Modeling: Statistical Analysis of Complex Data C372 Dr. Kelsey Forsythe Terminology SAR (Structure-Activity Relationships) Circa 19 th century? QSPR (Quantitative Structure Property Relationships) Relate structure to any physical-chemical property of molecule QSAR (Quantitative Structure Activity Relationships) Specific to some biological/pharmaceutical function of molecule (Absorption, Distribution/Digestion, Metabolism, Excretion) Brown and Frazer (1868-9) constitution related to biological response Statistical Models Simple Mean, median and variation Regression Advanced Validation methods Principal components, co-variance Multiple Regression QSAR,QSPR Modern QSAR Hansch et. Al. (1963) Activity travel through body partitioning between varied solvents C (minimum dosage required) (hydrophobicity) (electronic) E s (steric) 1/ C = a + b 2 + c + dE s + const . Choosing Descriptors Buffons Problem Needle Length? Needle Color? Needle Compostion? Needle Sheen? Needle Orientation? Choosing Descriptors Constitutional MW, N atoms Topological Connectivity,Weiner index Electrostatic Polarity, polarizability, partial charges Geometrical Descriptors Length, width, Molecuar volume Quantum Chemical HOMO and LUMO energies Vibrational frequencies Bond orders Energy total Choosing Descriptors Constitutional MW, N atoms of element Topological Connectivity,Weiner index (sums of bond distances) 2D Fingerprints (bit-strings) 3D topographical indices, pharmacophore keys Electrostatic Polarity, polarizability, partial charges Geometrical Descriptors Length, width, Molecular volume Choosing Descriptors Chemical Hydrophobicity (LogP) HOMO and LUMO energies Vibrational frequencies Bond orders Energy total G , S , H Statistical Methods 1-D analysis Large dimension sets require decomposition techniques Multiple Regression PCA PLS Connecting a descriptor with a structural element so as to interpolate and extrapolate data Simple Error Analysis(1-D) Given N data points Mean Variance Regression 2 = 1 N y i- y ( 29 2 i = 1 N y = 1 N y i i = 1 N y calc = mx + b y calc = y obs x calc = x obs = ) ( ) ( ) , ( Y Std X Std Y X Cov R Simple Error Analysis(1-D) Given N data points Regression residual y y y y obs i calc i + = y calc = mx + b obs calc obs calc x x y y = = Simple Error Analysis(1-D) Given N data points (Poor 0<R 2 <1(Good) ( 29 2 ) ( ) ( ) , ( ) ( - = = = N i calc y y SSR Y Std...
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This note was uploaded on 06/28/2011 for the course C 372 taught by Professor Yoonsuplee during the Spring '11 term at Korea Advanced Institute of Science and Technology.

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C372_L11 - Molecular Modeling: Statistical Analysis of...

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