MOE-QSAR课件

1、Advanced Application in MOEQSAROutlineQSAR OverviewDescriptor calculationDescriptor selection (PCA)Deriving QSAR modelsModel ValidationQSARQuantitative Structure-Activity Relationship (QSAR) applications correlate experimental data (e.g. biological activity or physical properties) with the structure

2、 of chemical compounds in a quantitative manner. QSAR models allow the interpretation and prediction of properties of structurally related compounds.*)The art of deriving a QSAR model lies in: Identifying a suitable mathematical functional form Reducing the complex dimensionality of reality into as

3、few dimensions as possible while still being able to give useful predictions of specific properties for molecules not experimentally tested so far. Most QSAR models are based on linear correlations. QSAR Model DevelopmentRobust QSAR model development generally proceeds as follows:Assemble a database

4、 of experimental results and molecular structures. Identify a descriptor set that correlates highly with the property in question, use descriptors which are mutually orthogonal and as meaningful and intuitive as possible(based on the underlying physico-chemical properties).Split the dataset into an

5、appropriate training and test set. The training set will be used to develop the model. The test set will be used to validate the predictive power of the model. In most cases the applicability of the model will be closely limited to the property space of the test set.Apply methods (regression, classi

6、fication, etc.) to generate the predictive models based on the training set.Predict activities for the test set to assess robustness of the model.Descriptor calculation(QuaSAR-Descriptor)Descriptor selection(Principle Components, QuaSAR-Contingency)Modelvalidation(Model-Evaluate)Modeldevelopment (Qu

7、aSAR-Model, Model-Composer)Quantitative & Qualitative QSAR Models in MOEBesides the selection of most appropriate descriptors and a meaningful separation of available data into training and test sets, the choice of an appropriate functional form is key to successful QSAR modeling. MOE provides quant

8、itative as well as qualitative QSAR approaches: Quantitative approaches include linear regression methods such as Partial Least Squares (PLS) and Principal Component Regression (PCR). Qualitative approaches include a non-linear binary filter based on Bayesian statistics as well as a binary classific

9、ation tree. ModeldevelopmentModelvalidationDescriptor selectionDescriptorcalculationDescriptor CalculationInitial Steps in Understanding a DatasetThe initial steps in interpreting an experimental dataset involve:Building preliminary Structure Activity RelationshipsCommon fragments to actives/inactiv

10、esLooking for patterns within the data structureAre clusters present in the data?Evaluating the relative importance of descriptors for a potential modelInvolves both stochastic and heuristic evaluationFinding commonality, and diversity within the dataRobustness in chemical spaceStep 1 in data analys

11、is: Find the relevant set of descriptorsMolecular Descriptors and FingerprintsMolecular Descriptors encode molecular properties per molecule into single numerical values. Qualitative: yes/no flags for presence or absence of certain features (like bits in fingerprints see below). Quantitative: numeri

12、cal measures of physico-chemical or structural properties.May dependent on connectivity and chemistry only (2D) or also on conformation / 3D geometry (3D).Fingerprints typically consists of bit strings of several hundreds or even thousands of individual yes/no flags. Each position of the bit string

13、encodes the presence (1) or absence (0) of a distinct property or feature. Including substructure fragments, connectivity patterns or pharmacophore type functional properties.010010100101001. bit stringMolecular weight:385.282logP:2.552# rotatable single bonds:5or2,5,7,10,12,15, bit positionQuaSAR D

14、escriptor PanelNumerical molecular descriptors may be calculated either via (MOE | Compute | QuaSAR | QuaSAR-Descriptor) without opening a database or via (DBV | Compute | Descriptors).Input databaseDescriptor synchronization with databaseDescriptor listDisplay filtersOverview of MOE Descriptors300

15、2D and 3D descriptorsTopological indicesSurface area propertiesPhysical propertiesEnergy termsAdd new descriptors with SVLAutomatically added to relevant calculationsExisting descriptors can be used as templateProprietary VSA descriptors Subdivision of surface area based on LogP, MR(molar refractivi

16、ty) and Partial Charge2D based approximation (for speed on large datasets)Semi empirical descriptorsDescriptor names prefixed with Hamiltonian: AM1_, PM3_, MNDO_Total energy, electronic energy, heat of formation, HOMO, LUMO, Ionization PotentialBinned VSA Descriptors IA subset of highly uncorrelated

17、, intuitive and meaningful 2D descriptors has been implemented in MOE to provide a stable “default” approach for new datasets: the binned Van-der-Waals surface area descriptors (referred to as binned VSA descriptors in MOE)1). LogP (partition coefficient), MR (molar refractivity) and partial charge

18、are used to cover a meaningful property space from hydrophobic to hydrophilic interactions. Each of these descriptor sets is derived from, or related to the Hansch汉施 and Leo descriptors.2)The descriptor returns the approximate surface area of a molecule, produced from a 2D representation, that falls

19、 into a given range of property values. Using the subset of binned VSA descriptors may help to overcome the necessity of using automatic descriptor selection routines.3) Binned VSA Descriptors IIThe surface contribution which may be sensed by neighboring molecules is approximated by subtracting over

20、lapping surface areas from first shell atom neighbors. The 2019 Wildman & Crippen1) atom type model is used to map properties onto individual atoms. Contributions to LogP and MR are derived in linear models from datasets of about 10,000 experimental data points each2). For partial charge calculation

21、, the Gasteiger PEOE charges is used.The approximate surface area contributions of a given molecule are added for each property bin.3)Vi values: V7 V2 V1 V6 V3 V4 +V8+V5Pi range:0,1)1,2)2,3)3,4)4,5)5,6)6 Descriptors:D1D2D3D4D5D6C8C3C4C5C6N7O2C12D BCUT and GCUT DescriptorsBCUT: Burden Matrix eigenval

22、uesThe BCUT descriptors*) are calculated from the eigenvalues of a modified adjacency matrix. The adjacency matrix contains a 1 if atoms i, j are bonded; 0 otherwise.Each ij entry of the adjacency matrix takes the value bij-1/2 where bij is the formal bond order between bonded atoms i and j. The dia

23、gonal takes the value of the associated PEOE, SMR, logP descriptor. The resulting eigenvalues are sorted and the smallest, 1/3 percentile, 2/3 percentile and largest eigenvalues are reported. GCUT: Inverse graph distance matrix eigenvaluesThe GCUT descriptors are calculated from the eigenvalues of a

24、 modified graph distance adjacency matrix, similar to BCUT descriptors.Each ij entry of the adjacency matrix takes the value dij-2 where dij is the (modified) graph distance between atoms i and j. The diagonal takes the value of the associated PEOE partial charges, SMR or logP descriptors. The resul

25、ting eigenvalues are sorted and the smallest, 1/3 percentile, 2/3 percentile and largest eigenvalues are reported. Caveats in Descriptor CalculationTo ensure consistent i3D and x3D descriptor values if starting from 2D structures without hydrogens, the following procedure should be used:Via the DBV:

26、1. Import the structures without adding hydrogens2. Energy minimize the database enabling the following options: - “Rebuild 3D”- “Add Hydrogens”- “Calculate forcefield partial charges” In the Command Line via sdproc, which adds hydrogens, calculates partial charges, performs energy minimization, and

27、 descriptor calculation in a single pass.Note: Differences may arise whenSMILES structures are used as a molecular source random initial coordinates.Hydrogens, partial charge, and energy minimization steps are performed in series coordinate truncation errors Exercise: Descriptor CalculationDescripto

28、r selection depends on the experience of the user. TPSA is used to consider the molecule size and electrostatic interaction, SlogP is used for the permeability, and SMR for polarization. Correlation between the 3 descriptors is plotted.1.Open the merged_bb.mdb file, and save a local copy to the work

29、ing directory.*)2.Open the QuaSAR-Descriptor panel(DBV | Compute | Descriptors).A list of the built-in descriptors is displayed, which can be navigated using text filters. 3.Enter TPSA in the Descriptor Filter.4.Left mouse click once to select the TPSA descriptor in the descriptor list.Exercise: Des

30、criptor Calculation5.Enter SMR in Descriptor Filter and select the SMR descriptor from the filtered list.6.Enter SlogP in Descriptor Filter and select SlogP from the filtered list. 7.Press OK to calculate the three selected descriptors.Exercise: Descriptor CalculationCheck descriptor correlations: 8

31、. Select the activity field (logBB) and the three descriptor fields in the database (SlogP, SMR, TPSA)Descriptor Calculation: CorrelationThe relationship between two variables X and Y is described by the correlation coefficient R. This is determined by linear regression analysis (see QSAR models), w

32、here a linear equation that has the smallest x and y values of all data points is derived.The correlation coefficient is calculated by:A correlation coefficient of 1 indicates a perfect correlation, -1 being inversely correlated and 0 being unrelated.*)yxyxxyR=1.00R=-0.72R=-0.06R=0.95R=0.77Correlati

33、on Between Stork Populations and Human Birthrates(H. Sies, Nature, 332 (1988) 495)Any correlation between descriptors and experimental data has to be meaningful mechanistically.196519671969197119731975197719791981Year500700900110013001500170019002100AmountStorksBabiesExercise: Descriptor Calculation

34、 - Correlation MatrixModels will be more robust if uncorrelated descriptors are used*). Correlation can be inspected using either a correlation plot or a matrix.1.Select (DBV | Compute | Analysis | Correlation Matrix).The numbers in the icons in the correlation matrix correspond to percent correlati

35、on.2.Double-Click on the highlighted cell to bring up the correlation plot (or by (DBV | Compute | Analysis | Correlation Plot) and selecting two numeric fields).Exercise: Descriptor Calculation - Correlation PlotA correlation coefficient (R2) of 0.0756 and the linear regression equation are indicat

36、ed in the header line of the correlation plot. There is virtually no correlation between SlogP and TPSA.3.Select e.g. active compounds (logBB 0.5) in the DBV or any data points in the plot (Left mouse drag over selection).The selection is interactive between the plot and the database viewer.To desel

37、ect entries, use the (DBV| Entry | Clear Entry Selection) menu, the Entry Popup menu or the Clear Selection button in the DBV plot.Exercise: Descriptor Calculation - Correlation PlotA correlation coefficient (R2) of 0.0756 and the linear regression equation are indicated in the header line of the co

38、rrelation plot. There is virtually no correlation between SlogP and TPSA.3.Select e.g. active compounds (logBB 0.5) in the DBV or any data points in the plot (Left mouse drag over selection). The selection is interactive between the plot and the database viewer. To deselect entries, use the (DBV| En

39、try | Clear Entry Selection) menu, the Entry Popup menu or the Clear Selection button in the DBV plot.Exercise: Descriptor Calculation - Correlation PlotDisplay attributes may be modified and data exported to other tools.4.Clear the selection using Clear Selection button in the Plot5.Select Data to

40、Clipboard to copy the XY values e.g. into a text editor, or to import the data into Excel.6.Select Attributes to change to a white background, black foreground, black markers, etc.*)Descriptor SelectionDescriptorcalculationModeldevelopmentModelvalidationDescriptor selectionDescriptor SelectionIn the

41、 preceding example one of the three descriptors (SMR) shows low relationship to logBB. In practice, many descriptors (some correlated, some not) are calculated and used as starting point to build a QSAR model.There are two approaches in the development of robust QSAR models:Descriptor reduction: Sel

42、ect calculated descriptors which are not or which are only weakly correlated (orthogonal). Either manually or semi-automatic by QuaSAR-Contingency.Dimension reduction: Use all calculated (possibly correlated) descriptors in a Principal Component Analysis (PCA). Descriptor Selection: QuaSAR-Contingen

43、cyQuaSAR-Contingency (DBV | Compute | QuaSAR-Contingency) is a statistical application to assist in the selection of descriptors for QSAR or QSPR. The application performs a bivariate contingency analysis for each descriptor and the activity or property value. It produces a table of coefficients tha

44、t helps to select important descriptors.Input databasePredictable propertyDescriptor listExercise: QuaSAR-ContingencyDetermine the most (un)important descriptors for the merged_bb.mdb dataset.1.Open the QuaSAR-Contingency panel (DBV | Compute | QuaSAR-Contingency).2. Select the 3 descriptors (SlogP,

45、 SMR, and TPSA) and press OK.3. Examine the result in the text editor.SlogP is considered as the most unimportant descriptor Contingency measuresDescriptor dependenceMost important descriptorsPrincipal Components Analysis (PCA)PCA reduces the dimensionality of a set of molecular descriptors by linea

46、rly transforming the data such that all components remain orthogonal. The 1st PC describes the direction of greatest data varianceThe 2nd PC describes the direction of the second greatest data variance etc.Descriptor 1Descriptor 2Descriptor 3PC 1PC 2PCA Pre-ProcessingSince descriptors may be heterog

47、eneous in nature (units, scale, etc.), the data should be pre-processed to build meaningful models. PCA is generally applied to scaled and/or mean centered data.Scaling: Usually appropriate in systems where the variables have different units and/or cover different magnitudes, e.g. variation between

48、100-110 C and 0.01- 0.1 M. Puts all descriptors on an equal basis in the analysisMean centering: Translates the origin to the mean of the data.Autoscaling: Combines unit variance (UV) scaling and mean centering.MeancenteringUV-scalingPrincipal Components Analysis - TheoryAny matrix X (objects n x de

49、scriptors/PCs m) can be decomposed into a product of three matrices (scores, loadings, and errors) as shown in the “linear correlation” slide before (Y = X B+E). In PCA the loading matrix B is orthonormal and any pair of loading vectors is orthogonal.1 mnX= featurematrixu1u21mb1b2xiTuiB= loading mat

50、rixU = X.B= score matrixScore plotu1u2Each point is an object n (neighboring points are similar)Each point is a feature/descriptor m (neighboring points are correlating)b2Loading plotb1Exercise: PCA CalculationCalculate the PCs:1.Select the merged_bb.mdb2.(DBV | Compute | Principal Components)3. Sel

51、ect the 3 descriptors(SlogP, SMR, TPSA)calculated before4. Press Report (see next slide).5. Then press OK.The PCs will be written as new fields.Input databasePreprocessing and weightingDescriptor listParameters affecting the PCA outputExercise: PCA CalculationPCA input parameters%Variance that is ac

52、counted for by each PC.In this case, almost all information is contained in the first 2 PCs.Useful for dimensionality reduction.The importance of each descriptor on each PC. First PC contains 53% of the variance and is equally weighted in importance by SMR and SlogPDatabase 3D Plot PanelThe 3D Plot

53、application visualizes the relationships between numeric data (DBV | Compute | Analysis | 3D Plot).Data being plottedData preprocessing“Color coding”Axis labelingGrid rangeGrid patternColored component (Grid, tick labels, axis title)RGB (red, green, blue) intensities Exercise: PCA Pattern Recognitio

54、nPlotting the first three PCs in a 3D plot visualizes how the PCs map onto the variable of interest. This is a good way to examine clusters within the data and to see how test and training data map onto each other.1.Select (DBV | Compute | Analysis | 3D Plot) to open the Database 3D Plot panel 2.Def

55、ine the following axes: X Y ZPC1PC2PC33. Switch OFF “Decorrelate Axes” *)Choose Activity: logBB.Color points by Threshold: 0.(This separates the data points by color)4. Press Plot. Exercise: PCA Pattern Recognition5. The 3D plot appears in the MOE Window. Active molecules: RedInactive molecules: Pur

56、ple6.Change the size of the points using(MOE | Render | Ball and Stick)7.Rotate the plot to locate clusters of special interest.8.Select a cluster in the plot. These will alsobe selected in the DBV.9.In the DBV, select (DBV | Entry | Hide Unselected Entries) or use the Browser (DBV | File | Browse)

57、and focus on selected entries. Switch to Subject: mol2D.10.Scroll through these entries and look for any obvious structural similaritiesNote: All these molecules are small in size simple small molecules, and aromatic rings.Exercise: PCA Pattern Recognition11.Select possible outliers. Property space

58、will be restricted to exclude these because of the high leverage of such points.12.Identify the selected entries using the Browser (DBV | File | Browse) Note: Two of them have charged nitrogen groups attached to aromatic heterocyclic rings.13.In the database, select:(DBV | Entry | Hide Selected Entr

59、ies)*) BUT: do not delete they have not statistically been shown to be outliersPCA Plot ResultsObservations from PCA:If clusters in chemistry space correspond with those in PCA space, look for similar structural classes.Evaluate each cluster to determine which descriptors are important for this grou

60、pWhere are the actives located? If they tend to cluster together in one area, do they have a common structural class?Conclusions:Generate individual regression models on individual clusters. Combine into a consensus model to improve predictability Build individual models using only the relevant desc