Plan. Lecture: What is Chemoinformatics and Drug Design? Description of Support Vector Machine (SVM) and its used in Chemoinformatics.

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2 Plan Lecture: What is Chemoinformatics and Drug Design? Description of Support Vector Machine (SVM) and its used in Chemoinformatics. Exercise: Example and exercise with herg potassium channel: Use of SVM in WEKA program

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5 Drug discovery

6 Drug and drug design A drug is a key molecule involved in a particular metabolic or signaling pathway that is specific to a disease condition or pathology. Action of activation (agonist) or inhibition (antagonist) to a biological target (protein, receptor, enzymes, cells ). Drug design is the approach of finding drugs by design, based on their biological targets. An important part of drug design is the prediction of small molecules binding to a target protein (pharmacophore, docking, QSAR, )

7 Chemoinformatics- definition F.K. Brown (1998): Annual Reports in Medicinal Chemistry, 33, The use of information technology and management has become a critical part of the drug discovery process. Chemoinformatics is the mixing of those information resources to transform data into information and information into knowledge for the intended purpose of making better decisions faster in the area of drug lead identification and optimization.

8 Chemoinformatics Chemoinformatics refers to the building and use of chemical databases and linked the information related to (like chemical and/or biological properties) for the identification and optimisation of new drugs. It involves computational technique for a rapid assessment of chemical libraries. data prediction Major aspects of chemoinformatics Information Acquisition: Methods for generating and collecting data empirically (experimentation) or from theory (molecular simulation) Information Management: Storage and retrieval of information Information Use: Data Analysis, correlation, and application to problems in the chemical and biochemical sciences

9 Chemical databases Information Acquisition: As the chemical information is in the center of chemoinformatics, structural information is a key element. Fortunately a lot of repositories have this information.

10 Information Management: Example of one chemical database repository

11 Information Management: Searching in PubChem

12 Information Management: Structural representation

13 Information Use: Association of chemical with other biological information NMR spectra Drug delivery Chemical reaction Binding affinity Solubility PK/PD Toxicity Such association of information can be used for the prediction of molecular properties relevant for new chemicals

14 Molecular descriptors Molecular descriptors are numerical values that characterize properties of a molecule. They can be related to experimental measurements (logp, molar refractivity, ) They can be theoretical and in this case derived from the structure representation that characterize a molecule. Therefore, they can vary in complexity

15 Assuming that molecules with similar structure share similar biological properties, predictive models can be developed Drug-likeness with a simple counting method, rule of five Octanol-water partition coefficient (logp) 5 Molecular weight 500 No. hydrogen bond acceptors (HBA) 10 No. hydrogen bond donors (HBD) 5 If two or more of these rules are violated, the compound might have problems with oral bioavailability. (Lipinski et al., Adv. Drug Delivery Rev., 23, 1997, 3.) Rules have always exception. (antibiotics, antibacterials and antimicrobials, )

16 Descriptors 1D descriptors: MW, number of features, MACCS key 2D descriptors: Topological, physichochemical, BCUT,

17 Descriptors QSAR based on 3 D interaction energies (GRID, CoMFA...) GRID: Determines a total interaction energy. Etot = Evdw + Eelec + Ehb Structural model Molecular interaction field (GRID) PCA and PLS model DRY O N1 H2O

18 Model of prediction variant1 63 variant2 82 Activity S001 S002 S003 S131 Activity = y + as001 + bs zs131

19 The most common method used to correlate data are usually, PLS, SVM, ANN, K-means.

20 Prediction models at CBS

21 Machine learning process Classification: Assignment of an unknown sample to a specific class among a predefined number of classes (qualitative answer) Regression: Prediction of a parameter value on the basis of example data (quantitative answer) Optimization: Selection of the best solution to a problem among a set of possible solutions.

22 Example: poor regression model, good classification

23 Support Vector Machines Computer algorithm that learns by example to assign labels to objects It works based on the maximisation of particular mathematical function with respect to a given collection of data Vladimir Vapnik in 1995

24 Linear Classifiers Support Vector Machines denotes +1 denotes -1 w x + b>0 f(x,w,b) = sign(w x + b) How would you classify this data? w x + b<0

25 Actually many possibilities denotes +1 denotes -1 f(x,w,b) = sign(w x + b) How would you classify this data?

26 denotes +1 denotes -1 f(x,w,b) = sign(w x + b) How would you classify this data?

27 denotes +1 denotes -1 f(x,w,b) = sign(w x + b) Any of these would be fine....but which is best?

28 Classifier Margin denotes +1 denotes -1 f(x,w,b) = sign(w x + b) Define the margin of a linear classifier as the width that the boundary could be increased by before hitting a datapoint.

29 Maximum Margin denotes +1 denotes -1 Support Vectors are those datapoints that the margin pushes up against 1. Maximizing the margin is good according f(x,w,b) to intuition = sign(w x + b) 2. Implies that only support vectors are important; other The training maximum examples are ignorable. Linear SVM margin linear classifier is the linear classifier with the, sum, maximum margin. 3. Empirically it works very very well. This is the simplest kind of SVM (Called an LSVM)

30 Linear SVM Mathematically x + M=Margin Width X - What we know: w. x + + b = +1 w. x - + b = -1 w. (x + -x -) = 2 M

31 Dataset with noise denotes +1 denotes -1 Hard Margin: So far we require all data points to be classified correctly - No training error What if the training set is noisy? - Solution 1: use very powerful kernels OVERFITTING!

32 Soft Margin Classification Slack variables ξi can be added to allow misclassification of difficult or noisy examples. ε 2 ε 11 What should our quadratic optimization criterion be? Minimize ε 7 Parameter C can be viewed as a way to control overfitting.

33 Non-linear SVMs Datasets that are linearly separable with some noise work out great: 0 x But what are we going to do if the dataset is just too hard? 0 x How about mapping data to a higher-dimensional space: x 2 0 x

34 Non-linear SVMs: Feature spaces General idea: the original input space can always be mapped to some higher-dimensional feature space where the training set is separable: Φ: x φ(x)

35 Linear: K(xi,xj)= xi T xj Polynomial of power p: K(xi,xj)= (1+ xi T xj)p Linear Polynomial of degree 2

36 Polynomial of degree 3 Polynomial of degree 10

37 Gaussian (radial-basis function network): Radial basis function Radial basis function = 0.5 = 2.0 controls le shape of the separating hyperplane

38 Properties of SVM Flexibility in choosing a similarity function Sparseness of solution when dealing with large data sets - only support vectors are used to specify the separating hyperplane Ability to handle large feature spaces - complexity does not depend on the dimensionality of the feature space Overfitting can be controlled by soft margin approach Nice math property: a simple convex optimization problem which is guaranteed to converge to a single global solution

39 SVM Applications SVM has been used successfully in many real-world problems - text (and hypertext) categorization - image classification - bioinformatics (Protein classification, Cancer classification) - Chemoinformatics - hand-written character recognition

40 It is sensitive to noise Weakness of SVM - A relatively small number of mislabeled examples can dramatically decrease the performance It only considers two classes - how to do multi-class classification with SVM? 1) with output parity m, learn m SVM s SVM 1 learns Output==1 vs Output!= 1 SVM 2 learns Output==2 vs Output!= 2 SVM m learns Output==m vs Output!= m 2)To predict the output for a new input, just predict with each SVM and find out which one puts the prediction the furthest into the positive region.

41 Some Issues Choice of kernel - Gaussian or polynomial kernel is default - if ineffective, more elaborate kernels are needed - domain experts can give assistance in formulating appropriate similarity measures Choice of kernel parameters - e.g. σ in Gaussian kernel - σ is the distance between closest points with different classifications - In the absence of reliable criteria, applications rely on the use of a validation set or cross-validation to set such parameters. Optimization criterion Hard margin v.s. Soft margin - a lengthy series of experiments in which various parameters are tested

42 How to assess accuracy of classification method like SVM? True Positive Rate (TPR) True Negative rate

43 Matthews Coefficient Correlation MCC is used for classification performance. Range between (-1) and 1 1: perfect prediction 0: random prediction -1: opposite to perfect prediction

44 Pearson Coefficient Correlation PCC is used for regression performance. Range between 0 and 1 1: perfect prediction 0: no prediction

45 Others performance measurement Accuracy: ACC = (TP+TN)/(P+N) Positive predictive value: PPV = TP/ (TP+FP) Negative predictive value: NPV = TN/ (TN+FN) False discovery rate: (FDR) = FP/ (FP+TP) Specificity: (SPC) = TN/ (FP + TN)

46 Interested on Chemoinformatics?

47 Time for a break!!!

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