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Molecular Descriptors and Molecular Descriptors and Virtual Virtual
Screening using Datamining Screening using Datamining approachapproach
Abhik Seal Abhik Seal OSDD CheminformaticsOSDD Cheminformatics
Aim of Cheminformatics Aim of Cheminformatics Project Project To screen molecules interacting with
the Potential TB targets using classifiers.
Select the selected molecules and dock with Targets to further screen the molecules for leads.
Use cheminformatics techniques such as QSAR ,3D qsar, ADMET to look for potential leads and design Drugs using the leads – by building combinatorial libraries.
QSAR and Drug DesignQSAR and Drug Design
Compounds + biological activity
New compounds with improved biological activity
QSAR
What is QSAR?What is QSAR?
QSAR is a mathematical relationship between a biological activity of a molecular system and its geometric and chemical characteristics.
A general formula for a quantitative structure-activity relationship
(QSAR) can be given by the following:
activity = f (molecular or fragmental properties)
QSAR attempts to find consistent relationship between biological activity and molecular properties, so that these “rules” can be used to evaluate the activity of new compounds.
Molecule PropertiesMolecule Properties
SPC : Structure Property SPC : Structure Property CorrelationCorrelation
INTRINSIC PROPERTIESINTRINSIC PROPERTIES Molar Volume Connectivity Indices Charge Distribution Molecular WeightPolar surface Area...........
INTRINSIC PROPERTIESINTRINSIC PROPERTIES Molar Volume Connectivity Indices Charge Distribution Molecular WeightPolar surface Area...........
MOLECULE STRUCTUREMOLECULE STRUCTURE
CHEMICAL PROPERTIESCHEMICAL PROPERTIESpKaLog PSolubilityStability
CHEMICAL PROPERTIESCHEMICAL PROPERTIESpKaLog PSolubilityStability
BIOLOGICAL PROPERTIESBIOLOGICAL PROPERTIESActivityToxicityBiotransformationPharmacokinetics
BIOLOGICAL PROPERTIESBIOLOGICAL PROPERTIESActivityToxicityBiotransformationPharmacokinetics
o Molecular descriptors are numerical values that characterize properties of molecules.
o The descriptors fall into Four classes . a) Topological b) Geometrical c) Electronic d) Hybrid or 3D Descriptors
Molecule DescriptorsMolecule Descriptors
Classification of DescriptorsClassification of Descriptors
Topological Descriptors Topological Descriptors
Topological descriptors are derived directly from the connection table representation of the structure which include:
a) Atom and Bond Counts
b) substructure counts
c) molecular connectivity Indices (Weiner Index , Randic Index, Chi Index)
d) Kappa Indices
e) path descriptors
f) distance-sum Connectivity
g) Molecular Symmetry
Geometrical DescriptorsGeometrical Descriptors
Geometrical descriptors are derived from the three-dimensional representations and include:
a) principal moments of inertia,
b) molecular volume,
c)solvent-accessible surface area,
d) Charged partial Surface area
e) Molecular Surface area
Electronic DescriptorsElectronic Descriptors
Electronic descriptors characterize the molecular Strcutures with such
quantities :
a)dipole moment, b)Quadrupole moment, c) polarizibility, d)HOMO and LUMO energies, e)Dielectric energyf) Molar Refractivity
Hybrid and 3D DescriptorsHybrid and 3D Descriptorsa) geometric atom pairs and
topological torsionsb) spatial autocorrelation vectorsc) WHIM indicesd) BCUTse) GETAWAY descriptorsf) Topomersg) pharmacophore fingerprintsh) Eva Descriptorsi) Descriptors of Molecular Field
Limit Of DescriptorsLimit Of DescriptorsThe data set should contain at least 5 times as many compounds as descriptor in the QSAR.
The reason for this is that too few compounds relative to the number of descriptors will give
a falsely high correlation:
2 point exactly determine a line. 3 points exactly determine a plane (etc.) A data set of drug candidate that is similar
in size meaningless correlation
Tools To calculate Molecular Tools To calculate Molecular Descriptors Freely availableDescriptors Freely availableCDK tool http://rguha.net/code/java/cdkdesc.html POWER MV http://nisla05.niss.org/PowerMV/?
q=PowerMV/ MOLD2
http://www.fda.gov/ScienceResearch/BioinformaticsTools/Mold2/default.htm
PADEL Descriptor http://www.downv.com/Windows/install-
PaDEL- Descriptor-10439915.htm
Admet Descriptors to Screen Molecules
BioavailabilityBioavailability The Bioavailability of a compound is
classified as :Bioavailability
Absorbtion Liver Metabolism
Permeability Gut-wall Metabolism
Transporters
Lipophilicity Solubility Flexibility
Hydrogen Bonding
Molecular Size/Shape
PREDICTION OF PREDICTION OF ADMET PROPERTIESADMET PROPERTIESRequirements for a drug:
◦ Must bind tightly to the biological target in vivo
◦ Must pass through one or more physiological barriers (cell membrane or blood-brain barrier)
◦ Must remain long enough to take effect◦ Must be removed from the body by
metabolism, excretion, or other meansADMET: Absorption, Distribution,
metabolism, Excretion (Elimination), Toxicity
Lipinski Rule of Five(Oral Drug Lipinski Rule of Five(Oral Drug Properties)Properties)Poor absorption or permeation is
more likely when:◦MW > 500◦LogP >5◦More than 5 H-bond donors (sum of
OH and NH groups)◦More than 10 H-bond acceptors (sum
of N and O atoms)
Polar Surface AreaPolar Surface Areao Defined as amount of molecular surface(vander-walls) arising
from polar atoms(Nitrogen and oxygen atom together with attached hydrogens)
o PSA seems to optimally encode those drug properties which play an important role in membrane penetration: molecular polarity, H - bonding features and also solubility.
o It provide excellent correlations with transport properties of drugs.(PSA used in the Prediction of Oral absorbtion,Brain penetration, Intestinal Absorption, Caco-2- permeability)
o It has also been effectively used to characterize drug likeness during virtual screening & combinatorial library design.
o The calculation of PSA, however, is rather time-consuming because of the necessity to generate a reasonable 3Dmolecular geometry and the calculation of the surface itself.
o Peter Ertl introduced an extremely rapid method to obtain PSA descriptor simply from the sum of contributions of polar fragments in a molecule without the necessity to generate its three - dimensional (3D) geometry.
PSA In Intestinal absorptionPSA In Intestinal absorption Intestinal absorption is usually expressed as fraction absorbed
(FA), expressing the percentage of initial dose appearing in a portal vein.
A model for PSA was done for the β - adrenoreceptor antagonists[1].A excellent sigmoidal relationship between PSA and FA after oral administration was obtained. Similar sigmoidal relationships can also be obtained for the topological PSA (TPSA).
These results suggest that drugs with a PSA < 60 Å 2 are completely (more than 90%) absorbed, whereas drugs with a PSA > 40 Å are absorbed to less than 10%.This conclusion was later confirmed with the correct classification of a set endothelin receptor antagonists as having either low, intermediate or high permeability.
PSA was also shown to play an important role in explaining human in vivo jejunum permeability[2]. A Model based on PSA and LogP for the prediction of drug absorption was developed for 199 well absorbed and 35 poorly absorbed compounds[3].
PSA In Blood brain barrier PSA In Blood brain barrier penetration(BBB)penetration(BBB)
Drugs that act on the CNS need to be able to cross the BBB in order to reach their target, while minimal BBB penetration is required for other drugs to prevent CNS side effects.
A common measure of BBB penetration is the ratio of drug conc’s in the brain and the blood, which is expressed as log (C brain /Cblood ).
Van de Waterbeemd and Kansy were probably the first to correlate the PSA of a series of CNS drugs to their membrane transport. They obtained a fair correlation of brain uptake with single conformer PSA and molecular volume descriptors.
Clark etal. Derived a model of 55 compounds using TPSA and LogP
LogBB= 0.516-0.115* TPSA
n= 55 r2 =0.686 r= 0.828 σ = 0.42
TPSA in combiantion with ClogP
LogBB= 0.070-0.014*TPSA+0.169*ClogP
n=55 r2 =0.787 r=0.887 σ =0.35 Great majority of orally administered CNS drugs have a PSA <70 Å2 . Non CNS
compounds suggested that these have a PSA < 120Å2 . Thus to conclude a majority of the Non CNS penetrating and orally absorbed
compounds have PSA values between 70 and 120 A2.
.
1-Octanol is the most frequently used lipid phase in pharmaceutical research. This is because:
It has a polar and non polar region (like a membrane phospholipid) Po/w is fairly easy to measure Po/w often correlates well with many biological properties It can be predicted fairly accurately using computational models
Xaqueous Xoctanol
P
Partition coefficient P (usually expressed as log10P or logP) is defined as:
P =[X]octanol
[X]aqueous
P is a measure of the relative affinity of a molecule for the lipid and aqueous phases in the absence of ionisation.
Partition coefficients
LogP for a molecule can be calculated from a sum of fragmental or atom-based terms plus various corrections.
logP = fragments + corrections
C: 3.16 M: 3.16 PHENYLBUTAZONEClass | Type | Log(P) Contribution Description Value
FRAGMENT | # 1 | 3,5-pyrazolidinedione -3.240ISOLATING |CARBON| 5 Aliphatic isolating carbon(s) 0.975ISOLATING |CARBON| 12 Aromatic isolating carbon(s) 1.560EXFRAGMENT|BRANCH| 1 chain and 0 cluster branch(es) -0.130EXFRAGMENT|HYDROG| 20 H(s) on isolating carbons 4.540EXFRAGMENT|BONDS | 3 chain and 2 alicyclic (net) -0.540
RESULT | 2.11 |All fragments measured clogP 3.165
clogP for windows output
N
N
CC
CC
C
C
C
O
C
C
O
C
C
C
C
C
C
C
C
C
C
H
H
H
H
H H
H
HH
H
H
H
H
H
H
H
H
H
H
H
Phenylbutazone
Branch
Calculation of logP
logPBinding to enzyme / receptor
Aqueous solubility
Binding to P450
metabolising enzymes
Absorption through membrane
Binding to blood / tissue proteins – less drug free to act
Binding to hERG heart ion channel -cardiotoxicity risk
So log P needs to be optimised
What else does logP affect?
Admet Descriptors Admet Descriptors Calculation ToolsCalculation Tools PreADMET http://preadmet.bmdrc.org/ Molecular Descriptors Calculation - 1081 diverse molecular
descriptors Drug-Likeness Prediction - Lipinski rule, lead-like rule, Drug DB like
rule ADME Prediction - caco-2, MDCK, BBB, HIA, plasima protein
binding and skin permeability data Toxicity Prediction - Ames test and rodent carcinogenicity assay SPARC Online Calculator
http://ibmlc2.chem.uga.edu/sparc/
SPARC on-line calculator for prediction of pK,, solubility, polarizability, and other properties; search in the database of experimental pKa values is also available
Daylight Chemical Information Systems
www.daylight .com/ daycgi/clogp
Calculation of log P by the CLOGP algorithm from BioByte; also access to the LOGPSTARdatabase of experimental log P data .
Admet Tools Continued..Admet Tools Continued.. Molinspiration Cheminformatics
www.molinspiration.com/seruices/index.
Calculation of molecular properties relevant to drug design and QSAR, including log P, polar surface area, Rule of Five parameters, and drug-likeness index
Pirika - www.pirika.com
Calculation of various types of molecular properties, including boiling point, vapor pressure, and solubility; web demo restricted to only aliphatic molecules
Actelion -www.actelion.com/page/property_explorer
Calculation of molecular weight, logP, solubility, drug-score and toxlcity risk .
Virtual Computational Chemistry Laboratory www. vcclab. org
Prediction of log P and water solubility based on associative neural networks as well as other parameters; comparison of various prediction methods
Virtual Screening Virtual Screening
Ways to Assess Structures from Ways to Assess Structures from a Virtual Screening Experimenta Virtual Screening ExperimentUse a previously derived
mathematical model that predicts the biological activity of each structure
Run substructure queries to eliminate molecules with undesirable functionality
Use a docking program to ID structures predicted to bind strongly to the active site of a protein (if target structure is known)
Filters remove structures not wanted in a succession of screening methods
Main Classes of Virtual Main Classes of Virtual Screening MethodsScreening MethodsDepend on the amount of structural and
bioactivity data available◦ One active molecule known: perform similarity
search (ligand-based virtual screening)◦ Several active molecules known: try to ID a
common 3D pharmacophore, then do a 3D database search
◦ Reasonable number of active and inactive structures known: train a machine learning technique
◦ 3D structure of the protein known: use protein-ligand docking
STRUCTURE-BASEDSTRUCTURE-BASED VIRTUAL VIRTUAL SCREENINGSCREENINGProtein-Ligand Docking
◦ Aims to predict 3D structures when a molecule “docks” to a protein Need a way to explore the space of possible
protein-ligand geometries (poses) Scoring of the ligand poses uch that the score
reflects binding affinity of the ligand; Need to score or rank the poses to ID most likely
binding mode and assign a priority to the molecules
◦ Problem: involves many degrees of freedom (rotation, conformation) and solvent effects
Conformations of ligands in complexes often have very similar geometries to minimum-energy conformations of the isolated ligand
Protein-Ligand Docking Protein-Ligand Docking MethodsMethodsModern methods explore orientational
and conformational degrees of freedom at the same time◦ Monte Carlo algorithms (change
conformation of the ligand or subject the molecule to a translation or rotation within the binding site
◦ Genetic algorithms◦ Incremental construction approaches
Distinguish “Docking” and Distinguish “Docking” and “Scoring”“Scoring”
Docking involves the prediction of the binding mode of individual molecules◦ Goal: ID orientation closest in geometry to
the observed X-ray structureScoring ranks the ligands using some
function related to the free energy of association of the two units◦ DOCK function looks at atom pairs of
between 2.3-3.5 Angstroms◦ Pair-wise linear potential looks at attractive
and repulsive regions, taking into account steric and hydrogen bonding interactions(eg moldock)
Structure-Based Virtual Structure-Based Virtual Screening: Other AspectsScreening: Other AspectsComputationally intensive and complexMultitude of possible parameters figure
into docking programsDocking programs require 3D
conformation as the starting point or require partial atomic charges for protein and ligand
X-Ray Crystallographic studies don’t include hydrogens, but most docking programs require them.
Ligand Based Virtual ScreeningLigand Based Virtual Screening The Ligand based approach mainly uses pharmacophore maps and
(QSAR) to identify or modify a lead in the absence of a known three dimensional structure of the receptor. It is necessary to have experimental affinities and molecular properties of a set of active compounds, for which the chemical structures are known.
a)PHARMACOPHORE:A pharmacophore is an explicit geometric hypothesis of the critical features of a ligand.Standard features include H-bond donors and acceptors, charged groups,and Hydrophobic patterns.The hypothesis can be used to screen databases for compounds and to refine existing leads.
For a geometric alignment of the functional groups of the leads, it is necessary to specify the conformations that individual compounds adopt in their bound state.
Since the simple presence of a pharmacophoric fingerprint is not sufficient for predicting activity, inactive compounds possessing the required pharmacophoric features must also be considered.
By comparing the volume of the active and the inactive compounds, a common volume can be constructed in order to approximate the shape of the (unknown) receptor site to further refine the pharmacophore model and to screen out additional compounds.
3D compound Structures
Feature Analysis
Set of Conformers
Align to template
compare
validation
Pharmacophore
Application
Pharmacophore Modelling Workflow
Continued.......Continued.......
b)QSAR: The goal of QSAR studies is to predict the activity of new compounds based solely on their chemical structure. The underlying assumption is that the biological activity can be attributed to incremental contributions of the molecular fragments determining the biological activity. This assumption is called the linear free energy principle. Information about the strength of interactions is captured for each compound by,for example, steric,electronic,and hydrophobic descriptors.
Molecular similarity and searching MoleculesMolecular similarity and searching Molecules
Chemical, pharmacological or biological properties of two compounds match.
The more the common features, the higher the similarity between two molecules.
Chemical
Pharmacophore
What is it?
The two structures on top are chemically similar to each other. This is reflected in their common sub-graph, or scaffold: they share 14 atoms
The two structures above are less similar chemically (topologically) yet have the same pharmacological activity, namely they both are Angiotensin-Converting Enzyme (ACE) inhibitors
Molecular similarityMolecular similarity
How to calculate it?
)&()()(
)&(),(
yxByBxB
yxByxT
n
iii yxyxE
1
2),(
Sequences/vectors of bits, or numeric values that can be compared by distance functions, similarity metrics .
E= Euclidean distance T = Tanimoto index
Quantitative assessment of similarity/dissimilarity of structuresneed a numerically tractable formmolecular descriptors, fingerprints, structural keys
hashed binary fingerprinto encodes topological properties of the chemical graph: connectivity,
edge label (bond type), node label (atom type)o allows the comparison of two molecules with respect to their
chemical structure
Molecular descriptorsMolecular descriptors
a) chemical fingerprint
Construction
1. find all 0, 1, …, n step walks in the chemical graph2. generate a bit array for each walks with given number of bits set3. merge the bit arrays with logical OR operation
Molecular descriptorsMolecular descriptors
Example 1: chemical fingerprint
ExampleCH3 – CH2 – OH walks from the first carbon atom
length walk bit array
0 C 1010000000
1 C – H 0001010000
1 C – C 0001000100
2 C – C – H 0001000010
2 C – C – O 0100010000
3 C – C – O – H 0000011000
merge bit arrays for the first carbon atom: 1111011110 This example illustrates how a 10 bits long topological chemical fingerprint is
created for a simple chain structure. In this example all walks up to 3 steps are considered, and 2 bits are set for each pattern.
Molecular SimilarityMolecular Similarity
Example 1: chemical fingerprint
0100010100010100010000000001101010011010100000010100000000100000
0100010100010100010000000001101010011010100000000100000000100000
Molecular descriptorsMolecular descriptors
Example 2: pharmacophore fingerprint
encodes pharmacophore properties of molecules as frequency counts of pharmacophore point pairs at given topological distance
allows the comparison of two molecules with respect to their pharmacophore
Construction
1. map pharmacophore point type to atoms2. calculate length of shortest path between each pair of atoms3. assign a histogram to every pharmacophore point pairs and count
the frequency of the pair with respect to its distance
Molecular descriptorsMolecular descriptors
Example 2: pharmacophore fingerprint
Pharmacophore point type based coloring of atoms: acceptor, donor, hydrophobic, none.
AA1
AA2
AA3
AA4
AA5
AA6
DA1
DA2
DA3
DA4
DA5
DA6
DD1
DD2
DD3
DD4
DD5
DD6
HA1
HA2
HA3
HA4
HA5
HA6
HD1
HD2
HD3
HD4
HD5
HD6
HH1
HH2
HH3
HH4
HH5
HH6
0
1
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AA1
AA2
AA3
AA4
AA5
AA6
DA1
DA2
DA3
DA4
DA5
DA6
DD1
DD2
DD3
DD4
DD5
DD6
HA1
HA2
HA3
HA4
HA5
HA6
HD1
HD2
HD3
HD4
HD5
HD6
HH1
HH2
HH3
HH4
HH5
HH6
0
1
2
3
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12
000000010000110100000010101000000000011000001000010000100000100001000101100100100101100110100111001111010000001100000001100010000100010100011101010000110000101000010011000010100000000100100000000110111001110111111010000010001000011011011000000010011010000001000101001101000100000000100000000100100000001001000010001010000100011100011101000100001011101100110110010010001101001100001000010111010011010101011111100001000001111110001000010000100010100001000101001111010100001000100000000100100000101001000010001010000001000100010100010100100000000000001010000010000100000100000000010001010001001100000000000000000001010000001000000000000000000001000101000101000000000000001010000100100000000001000000000000000101010101111100111110100000000000011010100011100100001100101000010001010001100001000001100000000001000100000011000000000110000000000001000000000100001000000000000010101000000001000001001000000100010100010100000000100000000000010000000000000100001000011000000100010000110001001010000001010010101110001000010000100010100001000111000101000100001000010011100100100000100011000000001010000101010100010100010100100000000000010010000010010100100100010000
query
targets
query fingerprint
proximity
target fingerprints
hits
0101010100010100010100100000000000010010000010010100100100010000
Virtual screening using fingerprintsVirtual screening using fingerprints
Individual query structure
Hypothesis FingerprintsHypothesis FingerprintsAdvantages Disadvantages
•strict conditions for hits if actives are fairly similar
• false results with asymmetric metrics
•misses common features of highly diverse sets
•very sensitive to one missing feature
•captures common features of more diverse active sets
• less selective if actives are very similar
•captures common features of more diverse active sets
•specific treatment of the absence of a feature
• less sensitive to outliers
• less selective if actives are very similar
SUMMARYSUMMARYVirtual screening methods are central
to many cheminformatics problems in:◦ Design◦ Selection◦ Analysis
Increasing numbers of molecules can be evaluated using these techniques
Reliability and accuracy remain as problems in docking and predicting ADMET properties
Need much more reliable and consistent experimental data
Datamining and Machine Datamining and Machine Learning Approaches to Learning Approaches to Virtual ScreeningVirtual Screening
Idea of Datamining Idea of Datamining Is discovering for patterns in the
data i.e for example a)an hunter looks pattern in animal migration
behavior. b)farmers seek patterns in crop growth. c) politcians seek patterns in voters opinion d) Pattern in the compound structures .The Patterns which are discovered must
be meaningful and lead to some advantage.
The process must be automatic or semiautomatic.
Canonical learning Canonical learning ProblemsProblems
Supervised Learning: given examples of inputs and corresponding desired outputs, predict outputs on future inputs.
a) Classification b) Regression c) Time series predictionUnsupervised Learning: given only inputs,
automatically discover representations, features, structure, etc.
a) Clustering b) Outlier detection c) Compression
Datamining MethodsDatamining MethodsSubstructural Analysis The Substrcutural fragments makes a contribution to
activity irrespective of the other fragments of the molecule. The idea is to derive a weight for each fragment which reflects to be active or inactive. The sum of weight gives the score of molecule which enables a new set of structures to be ranked in Decreasing probability of activity.
The weight is calculated using the eq :
Where act(i) is the number of active molecules that contain the i th fragment and inact(i) is the number of inactive molecules that contain the i th fragment
Discriminant algorithmsDiscriminant algorithms The aim of discriminant analysis is try to
separate the molecules into constituent classes. The simplest Linear discriminant which in case
of two activity class and two descriptors which aim to find a st. line that separates data such that maximum number of compounds are classified.
If more than variable uses the line become hyperplane.
The idea is to express a class as a linear combination of attributes.
X= w0+w1a1+w2a2+w3a3+.........
X =class a1 a2 = attributes w1 w2 = weights
Neural Networks(NN)Neural Networks(NN) The two most commonly used neural network
architectures used in chemistry are the feed forward networks and the Kohonen networks.
The feed forward NN is a supervised learning method as it uses the values of dependent variables to derive the model. The Kohonen or Self Organizing map (SOM) is an unsupervised method.
The Feed forward NN contains layers of nodes with connection between all pairs of nodes in the adjacent layers. A key feature is presence of hidden nodes along with back propagation algorithm makes the network applicable to many fields.
The neural network must first be trained with set of inputs. Once it has been trained it can then be used to predict values for new and unseen molecules.
Neural Networks Neural Networks Continued...Continued...
The Figure Below shows a Feed forward network with 3Hidden nodes and one output.
A Kohonen NN consist of rectangular array of nodes and each nodes associates a vector that corresponds to input data (Descriptors values)
The data is presented to the network one molecule at a time and the distance between each of node vectors and molecule vectors are determined with distance metric. The node with minimum distance becomes the wining node.
Disadvantage of Neural Disadvantage of Neural NetworksNetworks Its is difficult to design a perfect model for neural
networks with number of hidden layers and nodes which will best fit the data.
Another practical issue is Overtraining .An overtrained NN will give excellent results train data but will perform poorly on an unseen data(test data).This is because the network memorizes the data.
The way solve this problem is to divide the sets in train and test and then watch performance of the set . If the performance of the test set increase such that till it reaches a plateau and start to decline ,at this point network has maximum predictive ability.
DECISION TREES(DT)DECISION TREES(DT) In Feed forward NN it is not possible to determine the
result for a given input due to complex nature of interconnection between nodes one cannot determine which properties are important.
Decision trees consist of set of rules that associate molecular descriptor values with property of interest.
A DT is a tree with nodes containing specific rules .Each Rule may correspond to the presence or absence of a particular feature .
In a DT one start at the root node and follows the edge with appropriate first rule. This continues until a terminal node is reached at which point one can assign the molecule into active and inactive class.
DTs like ID3 ,C4.5,C 5.0 uses information theory to choose which criteria to choose at each step.
Random forests a small subset of the descriptors is randomly selected at each node rather than using the full set.
Support Vector Support Vector Machines(SVM)Machines(SVM)
Support vector machines select a small number of critical boundary instances called support vectors from each class and build a linear discriminant function that separates them as widely as possible.
Molecules in the test set are mapped to the same feature space and
their activity is predicted according to which side of the hyper plane they fall.
The distance to the boundary can be used to assign confidence level to the prediction such that higher the distance the higher the confidence.
The output of SVM is given by f(x)=sign(g(x)) where g(x)=w(t)x+b, w is a vector and b is a scalar.
linear SVM can be applied only when the active and inactive compounds can be divided by a straight line (hyperplane) in the feature space.
SVM continued....SVM continued.... When the data cannot be separated linearly, kernel
functions are used to transform to the Higher dimensions.
The output of SVM is given by f(x)=sign(g(x)) and g(x) is given by
where K is the so-called kernel function, the suffix k
represents the support vector, and m stands for the number of support vectors.
The Gaussian and the Polynomial kernel function are used
Strengths and Weaknesses Strengths and Weaknesses of SVMof SVM
Strengths Training is relatively easy No local optima It scales relatively well to high dimensional data Tradeoff between classifier complexity and error can
be controlled explicitly Non-traditional data like strings and trees can be used
as input to SVM, instead of feature vectors
WeaknessesNeed to choose a “good”kernel function.
Measuring Classifier Measuring Classifier PerformancePerformance
N= total number of instances in the dataset
TPj= Number of True Positives for class j
FPj = Number of False positives for class j
TNj= Number of True Negatives for class j
FNj= Number of False Negatives for class j
Accuracy =
Sensitivity/recall =
Specificity/precision =
Types of Datamining Types of Datamining learning learning Process in Weka Process in WekaClassification- learning-the learning scheme
is presented with a set of classified examples from which it is expected to learn a way of classifying unseen examples.
Association Learning-any association among features is sought, not just ones that predict a particular class value
Clustering-groups of examples that belong together are sought
Numeric prediction-the outcome to be predicted
is not a discrete class but a numeric quantity.
Classifier Algorithms in Classifier Algorithms in WEKAWEKAa)Bayes Classifier c) Functions AODE LINEAR REGRESSION BAYES NET LOGISTIC NAÏVE BAYES MULTILAYERD PERCEPTRON NAÏVE BAYES MULTINOMIAL RBF NETWORK NAÏVE BAYES UPDATABLE SIMPLE LINEAR REGRESSION SIMPLE LOGISTIC
SMO,SMO REG. b)Trees d)Rules ADTREE CONJUCTIVE RULEID3 DECISION TABLE J48 JRIPLMT M 5RULES
NB5TREE NNGERANDOM FOREST ONE R RANDOM TREE PRISMREP TREE ZERO R
SummarySummary Machine learning is mainly applied to ligand-based
drug screening and it is applied to the calculation of the optimal distance between the feature vectors of active and inactive compounds.
A kernel is essentially a similarity function with certain mathematical properties, and it is possible to define kernel functions over all sorts of structures for example, sets, strings, trees, and probability distributions .
Interest in neural networks appears to have declined since the arrival of support vector machines, perhaps because the latter generally require fewer parameters to be tuned to achieve the same (or greater) accuracy.
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