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Introduction Support Vector Regression QSAR Problems and Data SVMs for QSAR

Introduction Support Vector Regression QSAR Problems and Data SVMs for QSAR Linear Program Feature Selection Model Selection and Bagging Computational Results Discussion. Support Vector Regression. e -insensitive loss function. Quadratic SVMs with L 2 -norm.

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Introduction Support Vector Regression QSAR Problems and Data SVMs for QSAR

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  1. Introduction • Support Vector Regression • QSAR Problems and Data • SVMs for QSAR • Linear Program Feature Selection • Model Selection and Bagging • Computational Results • Discussion

  2. Support Vector Regression e-insensitive loss function

  3. Quadratic SVMs with L2-norm

  4. Linear SVMs with L1-norm (n-SVR)

  5. QSAR Problems and Data Preparation of Input DATA (Bioactivity value, Structures) 3D Geometry Optimization Calculation of Descriptors SVMs for QSAR Statistical Analysis QSAR Model Building

  6. Data Sets • HIV dataset five classes of Anti-HIV molecules, 64 molecules, 620 descriptors • Lombardo benchmark dataset Brain-blood barrier partitioning dataset, 62 molecules, 649 descriptors Data Matrix descriptor1 descriptor2 - - - descriptor m Activity Molecule 1 x11 x12 x1m ln BB Molecule 2 x21 x22 x2m ln BB - - - - - - Molecule n x n1 x n2 x nm ln BB

  7. Data Matrix descriptor1 descriptor2 descriptor3 - - - descriptor m Activity Molecule 1 x11 x12 x13 x1m ln BB Molecule 2 x21 x22 x23 x2m ln BB - - - - - - Molecule n x n1 x n2 x n3 x nm ln BB

  8. SVMs for QSAR Construct Datasets Model Selection C, e, n, s Feature Selection Bagging Models Optimize Model Final Model

  9. Linear Program Feature Selection

  10. Model Selection • Choose SVM model parameters, C, e or n, s • Select evaluation function Q2 • Evaluate on testing data • Adjust using cross validation Bagging • Different validation sets give different models • Many local minima in SVM parameter search • Average models

  11. Methods (10-fold CV) Full Data (649) LP FS (21) NN SA (9) Computational Results Q2 q2 Q2 q2 Q2 q2 L1-SVM .384 .382 .157 .153 .219 .217 L2-SVM .310 .292 .171 .160 .247 .245 NN .320 .301 .222 .193 .247 .238

  12. Discussion • Robust optimization methods • LPFS outperforms NNSA • L1-SVM can run faster than L2-SVM • ? May improve LPFS method • ? May improve performance of L1-SVM This work is supported by NSF (IIS-9979860 and 970923)

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