Multivariate Chemical Space

Importance of PROCESS is not less than PRODUCT7/29/2014

7/29/2014 Importance of PROCESS is not less than PRODUCT

My Academic Perspective

(Resume)

Teaching Trend Explaining in a simple and popular way the basic concepts of Chemometrics and Chemoinformatics

Research Trends

Development of new Chemometrics and

Chemoinformatics Algorithms

Object Selection

Variable Selection

Regression Methods

Pattern RecognitionClustering

Classification

Application of Chemometrics Methods in Quantitative and Qualitative Determination

Heavy and Toxic Metal Ions Determination

Toxic Organometallic Compounds Determination

Solution Equilibria and Kinetics Studies

pKa, kf and Kf Determination

Drug Design in Silico

QSAR

Docking

MDS

Pharmacophore

Virtual Screening

Simulation of some analytical methods

Chromatographic Separation

Micellization

Cloud Point Extraction

Ionic Liquids

Let me emphasize on

a very important

point:

We are in the Interdisciplinary

Age!

Why?

As an example; Systems Biology (Systeomics) comes from very diverse disciplines: Biology, Physics, Chemistry,

Biochemistry,….

So?

We have to organize

Research Teams and do the job in

a Team Framework.



را صفتغيبیموصوفدر معرفت مي کند يکیهم چنان که هر

Why do we study?

To overcome or Control:

Ignorance Unknowns Un-optimized

systems

Importance of PROCESS is not less than PRODUCT

Theory

Hypothesis

Confirmation

Observation

Theory

Tentative Hypothesis

Observation

Pattern

Deductive Vs. Inductive

Induction is usually described as moving from the specific to the general, while deduction begins

with the general and ends with the specific.

Arguments based on laws, rules and accepted principles are generally used for Deductive

Reasoning. Observations tend to be used for Inductive Arguments.

7/29/2014

-Metrics as a soft-computing or soft-modeling is a Inductive Research Approach


x

Z

Y

Var3

Var2

Var1

Spaces

Multivariate Chemical SpaceMultivariate Space

Varmm

What are the Objectives of An Analytical Chemist’s Studies?

In summary:

1-To study, 2- To measure and 3- To discriminate the OBJECTS.

But in more details:




Pattern Recognition

Unsupervised or Subjective without Mapping Function

Clustering

Hierarchical or Dendrogram

Divisive

Agglomerative

Non-Hierarchical

PCA

k-means

Supervised or Objective with Mapping Function

or Class Label

Classification

Linear

LDA

PLSDA

kNN

Nonlinear

QDA

ANN

c-SVM

Calibration-Prediction

Linear

Original Variables

MLR

CLS

ILS

Hidden Variables

PCR

PLS

Nonlinear

Original Variables

ANN

r-SVM

Latent Variables

ANN

r-SVM


Optimization

Analytical

Gredient

Non-Gredient

Emperical

OVAT AVAT

Simplex

DOE

Quality Control

Statistical Quality Control

Control Charts

Pareto Charts

Cause and Effect or Fish-Bone Diagram


But what do we mean by VARIABLE and OBJECT?


Wh

at a

re t

he

Obje

cts?

An aliquot of a solution

A piece of cake

Intact Wheat

Intact sugar

Intact meat

A molecule

A protein

A complex



Wh

at a

re t

he

Var

iab

les,

Fea

ture

s o

r O

bse

rver

s?Physical or Practical

Based Variables

pH

Time

Ionic Strength

Temp.

Wavelength

Excitation

Emission

m/e

Molecular Descriptors

Constitutional

Topological

Quantum Chemical

Geometrical

MIF

Fingerprints

A Very Important Point about the Variables:

The most desired situation is when they are Mutually Perpendicular

Overlapped

Segment or Space

has seen twice,

Covariance≠0.


Total Information or Hidden Variance in the Sample or

Object

In wavelength direction

In pH or Time direction


DATA

Dependent Data Vector

pKa

pIC50

Concentration

Activity

…….

Independent Data Matrix

Experimental

Spectroscopic

UV-Vis

IR

NMR

MS

Raman

Electrochemistry

Polarography

Voltammetry

Separation

Chromatography

LC-MS

GC-MS

HPLC-DAD

Non-Chromatographic

SPE

SPME

Extraction

Computational

QSAR

MDS

DOCKING

Virtual Screening

Objects

as rows

Variables as Columns

y =

En

d P

oin

t V

ecto

r

123....

.

.

.

.

.

.n

1 2 3 . . . . . . . . . m

Objects

as rows

123....

.

.

.

.

.

.n

Preprocessing

Dependent y Vector

log Transformation

Mean Centering

Autoscaling

Independent X Matrix

Mean Centering-Has its general purpose

AutoscalingHas its general purpose

Outlier Detection AD

Dimensionality Reduction

PCA


Now we can Define Multivariate Chemical Space(s):

The OBJECTS in the Multivariate SPACE formed by one, two,…, hundreds or even thousands of

variables(Dimensions).We need some Tools and Devices to imagine and use

this information.DISTANCE and SIMILARITY Play very important roles:



Geometrical Interpretation of

Information Matrix

Spaces

Row SpaceColumn Space:

Object Map

Relationship Between Objects

Similarity Dissimilarity

Metrics

Distances

Euclidean Manhattan Mahalanobis

Patterns

Classes Clusters Groups

Row Space!

Is it informative? How? What does it mean? How can we use it?

On

O1

O2

Each Point is a Vector!

m-dimensional space Sm

n- points pattern Pn


Column Space

Objects Map Scientists(Chemists, Biologists..) are interest in!!!

Is it informative? How? What does it mean? How can we use it?

Vn

V1

V2

Class I or Group I

Class II or Group II

Each Point is a Vector!

n-dimensional space Sn

m- points pattern Pm


Instrument Order or Measurement Order:

Zero Order

First Order

Second

Order

Third

Order

Order

A Table

Geometric Representation Example

pH, ISE, SB-

SL Spect.

GC, LC, Scan

Spect.

HPLC-DAD,

Spec. Fluo.

2D NMR is Not

Second Order(each

Dim. is not a

Physical var).Why?

LC-Spec. Flou.

LC-MS-MS? No!!

Why?

Time

Time

Wavelength

lem

lem

A Scalar, Zero Dimension Point

An Array

A BoxImportance of PROCESS is not less than PRODUCT

Is signal a

dimension?

7/29/2014

Just from Analytical Chemist View: How much information can be obtained per sample/object?

Importance of PROCESS is not less than PRODUCT

A Table A Table A Table A Table A Table A Table A Table A Table A Table A Table A Table A Box

lem

lem

7/29/2014

A Table

More Information, Less Simple

A BoxImportance of PROCESS is not less than PRODUCT7/29/2014

A Table

More Simple, Less Informative


A Table

A Box

Un

ivariate

Multiw

ay

Mu

ltivariate

A Table



• How to Use Column and Row Spaces?

• 1- Lets first check a real physical example:

• HPLC-DAD or GC-IR Second Order Data:

• 2- In the absence of the C and S matrices we can generate and use row and column Spaces: U and V matrices by PCA or SVD or EVD………..

• 3- Fingerprints(as Variables to form Space) and use of Tanimoto Index for Virtual Screening purpose


Example for a Second Order Instrument HPLC-DAD

One-Component

System

Two-Component

System

1

2

16

1

2

16

1 2

141 2

14

3

1 2 3

A C ST

= ×

Rows of A are Linear Combination of Rows of “what”.and Columns of A are Linear Combination of Columns of “what”.

If these are true:

The Pure(est) Column and Row Spaces of A can be extracted and used for Quant. And Qualit. Determination

How to Use Row or Column Spaces:A simple view of Projection or Orthogonalization


O

P1

P1’

P2

P2’

Row or Column Space

Development a new method for generation of three way data by

beta-cyclodextrin complexes

65

UV-Vis

Spectrophotometer

Inclusion complexes with β-CD

Analaysis of data with PARAFAC and

BLLS/RBL method using MVC2 program

CA

VA

3-D plots for two calibration samples

66

BLLS/RBL outputs for spectral deconvolution of CA and VA in

sour cherry juice sample

67

Spectral profile Concentration of β-CD profile

Development a new method for generation of three way data by

time gradient in bulk liquid membrane system

68

UV-Vis

Spectrophotometer

Time gradient with BLM

Obtaining concentrations and spectral profiles with

BLLS/RBL method using MVC2 program

SY

AR

3-D plots for two calibration samples

69

BLLS/RBL outputs for spectral deconvolution of SY and AR in chewy candy

70

Time profileSpectral profile

Complexometric titration with FIA

Titration is done by injection of 50 μL of the metal solution into

flow of ligand solution.

Determination of stoichiometry of complexation is done using Principal

component analysis (PCA).

Notation in MATLAB :

[u,s,v]=svd (name of matrix)

Stoichiometry of complexation

I. Calibration of the dispersion pattern of the sample in flow and

calculating dispersion coefficients.

II. Injection of small volume of the metal solution into ligand solution for

obtaining spectral-mole ratio data.

III. Calculation of [Mn+] in each time step using calibrated dispersion

coefficients.

IV. Studying the effect of ligand dilution due to injection of metal solution.

V. Determination of stability constant.

Determination of stability constant by FIA consists of:

Calibration is made via injection of the

calibrator dye (for example murexide or

fluorescein) into the blank solution flow.

𝑫𝒕,𝒄𝒂𝒍 = 𝑨𝒐,𝒄𝒂𝒍

𝑨𝒕,𝒄𝒂𝒍

𝑫𝒕,𝒄𝒂𝒍: 𝐂𝐚𝐥𝐜𝐮𝐥𝐚𝐭𝐞𝐝 𝐝𝐢𝐬𝐩𝐞𝐫𝐬𝐢𝐨𝐧 𝐂𝐨𝐞𝐟𝐟𝐢𝐜𝐢𝐞𝐧𝐭𝐬.

𝑨𝒐,𝒄𝒂𝒍: Absorbance of the calibrator dye

when it acts as carrier in FIA.

𝑨𝒕,𝒄𝒂𝒍: Absorbance of the calibrator dye

when a small volume of the calibrator dye is

injected into the blank solution flow.

I. Calibration of dispersion pattern

Determination of stability constant by FIA.

(a) Reaction coil length: 50 cm and flow rate of 1.25 ml/min.(b) Reaction coil length: 250 cm and flow rate of 0.75 ml/min.

II and III. Injection of small volume of the metal solution into the ligand

solution for obtaining spectral-mole ratio data and Calculation of [Mn+] in each

time step.

𝑪𝑴𝒕 = 𝑪𝑴𝒐,𝒕

𝑫𝒕,𝒄𝒂𝒍𝑪𝑴𝒕= The total concentration of the injected metal solution in each time step.

𝑪𝑴𝒐,𝒕 = The Initial concentration of the injected metal solution into flow of

ligand.

𝑫𝒕,𝒄𝒂𝒍 = The calculated dispersion coefficients in the calibration step.


𝑪𝑳𝒕 = ( 𝑨𝒕,𝑳

𝑨𝟎,𝑳) × 𝑪𝑳𝟎

𝑪𝑳𝒕: The concentration of the ligand in time t after blank solution injection.

𝑨𝒕,𝑳: 𝐓𝐡𝐞 𝐚𝐛𝐬𝐨𝐫𝐛𝐚𝐧𝐜𝐞 𝐨𝐟 𝐭𝐡𝐞 𝐥𝐢𝐠𝐚𝐧𝐝 𝐬𝐨𝐥𝐮𝐭𝐢𝐨𝐧 𝐚𝐭 𝐭𝐢𝐦𝐞 𝐭 𝐚𝐟𝐭𝐞𝐫 𝐭𝐡𝐞 𝐛𝐚𝐥𝐧𝐤 𝐬𝐨𝐥𝐮𝐭𝐢𝐨𝐧 𝐢𝐧𝐣𝐞𝐜𝐜𝐭𝐢𝐨𝐧.

𝑨𝟎,𝑳: The absorbance of the ligand solution when the injected blank solution has not reached to

the detector yet.

𝑪𝑳𝟎: 𝐓𝐡𝐞 𝐢𝐧𝐢𝐭𝐢𝐚𝐥 𝐜𝐨𝐧𝐜𝐞𝐧𝐭𝐫𝐚𝐭𝐢𝐨𝐧 𝐨𝐟 𝐭𝐡𝐞 𝐥𝐢𝐠𝐚𝐧𝐝 𝐬𝐨𝐥𝐮𝐭𝐢𝐨𝐧.

III and IV. Studying the effect of ligand dilution (Injection of the

blank solution into flowing ligand solution).


V. Calculation


The general formula resulting from mass-balance equations:

[𝑳]𝒏+𝟏𝜷𝒏 + [𝑳]𝒏 𝜷𝒏 𝒏𝑪𝒕,𝑴 − 𝑪𝒕,𝑳 + 𝜷𝒏−𝟏 + [𝑳]

𝐧−𝟏 𝜷𝒏−𝟏 𝒏 − 𝟏 𝑪𝒕,𝑴 − 𝑪𝒕,𝑳 + 𝜷𝒏−𝟏 +⋯− 𝑪𝒕,𝑳

[L] : The concentration of free ligand in each time step in reaction coil.

β : The stability constants.

Ct,M: The total concentration of metal at each time step in reaction coil.

Ct,L: The total concentration of ligand at each time step in reaction coil.

n: Stoichiometry of complexation.

The calculated stability constants by the proposed method and comparison with other methods

Application of molecular modeling in

chromatography

Explore the mechanism of

adsorption

Find a selective ligand for affinity chromatography

Optimize the process

parameter

The molecular model used to investigate the affinity adsorbant is composed of four parts which describe the four main components:

• support• spacer• ligand• analyte

The support, the spacer and the ligand arecovalently bound and can have electrostatic andvan derWaals interactions with the analyte.

Molecular model

The surface can be visualized by

• All atom model

• End groups and ligands

Supprot

81

1. Design an affinity ligand

Molecular docking

Rigid protein Water deleted

Predict a ligand which can bind strongly to key regions of

protein

Main drawback

Molecular dynamics simulation (MDs)

Computational cost

Time consuming process

Solvent effect

can be performed only for a few ligand–protein

Flexibility of protein and ligand

• This work involves the design of peptide ligand of human serumalbumin by molecular simulations, aiming at the development of anovel affinity chromatography matrix for HSA purification.

This strategy includes four steps:• (1) building peptide library by amino acids location,• (2) docking these peptides into the candidate pocket of the target

protein and evaluating the affinity of the peptides to the targetprotein by using a scoring function,

• (3) more validating the affinity between the selected peptide ligandand the target protein by MD simulations.

• (4) a dipeptide of high affinity to HSA was found by the procedure. Itwas synthesized and its affinity to HSA was evaluated by UV-Visspectroscopy to ensure that this ligand has enough affinity for thepurification of HSA by affinity chromatography.

Ala-AlaAla-ArgAla-Asn

.

.

.Ala-TrpAla-TyrAla-Val

Ala-AlaArg-AlaAsn-Ala

.

.

.Trp-AlaTyr-AlaVal-Ala

Ala-TrpAla-tyrAla-PheAla-His

Trp-AlaPhe-AlaTyr-Ala

3 x 4 = 12

Peptid

e

Gold score

fitness

Peptide Gold score

fitness

Trp-Trp -70.667 Phe-

Phe

-62.425

Trp-Tyr -63.075 Phe-

His

-60.269

Trp-

Phe

-65.426 Tyr-Trp -65.665

Discovery Studio 2.5. GOLD program

P1

P2

ligand

spacer lys[ NH(CH2)6NH2] of ECH Sepharose 4B gel

Trp-Trp

• After the docking simulations, the complex of the candidate ligand and HSAwas further analyzed by MD simulations. The GROMACS3.3 simulation packagewas used to study the changes of the complex with time.• Water molecules can provide bridging interactions between the ligand andsome amino acid residues of the candidate pocket, and they are involved in thesolvation and desolvation processes in ligand binding.

a mathematical transformation employed to transform signals between time (or spatial) domain and frequency domain.

It is used to determine the frequency content of local sections of a signal as it changes over

time.

The function to be transformed is first multiplied by a window function, and the resulting

function is then transformed with a Fourier transform to derive the time-frequency analysis.

Wide windows do not provide good localization at high frequencies

Narrow windows do not provide good localization at low frequencies

Overcomes the preset resolution problem of the STFT by using a variable length window:

Use narrow windows at high frequencies for

better time resolution

Use wider windows at low frequencies for better frequency

resolution

Mother waveletsignal

Convolution is similar to cross-correlation.

convolution

A greater scaling factor is associated to a wider wavelet function and therefore

results in the low frequency components of the signal and vice versa. Therefore,

wavelet transform reveals the characteristic information of the signal at different

scales.

CWTof objects in class 1

Class 1

Class 2

CWTof objects in class 1

F-matrix

data matrix of samples F-ratio

thresholding

Best feature space

A careful search method using wavelet transform

S X L X NNXL

Toward applying CWT a fine multi-resolution space and characterization feature space is

provided. Indeed, CWT due to its redundancy property is able to provide such a finer multi-

resolution space that visualizes all information content of the signal.

DiscoTech

• A series of low-energy conformers for each molecule is computed.

• Each conformer of molecule is characterized byligand points and site points.

• Site points are the hypothetical position ofcomplementary atoms in the binding site and aredetermined from the position of heavy atoms in theligand structure.

• A clique detection algorithms used to align structures based on these distances.

• Each conformation of the reference molecule in turn is compared to all conformations of the other molecules.

• The cliques identified are examined to find one that is common to at least one conformation of every molecule.

• This process is repeated for every conformation of the reference molecule. If no solution is found the tolerances on the clique detection process are increased until either a solution is found or the maximum tolerance is reached.

• The output from a Disco run is a ranked list of all possible pharmacophoremappings

Gasp

Conformational analysis together with randomrotations and a random translation are appliedon the fly before any superimposition.

The chromosomes encode the angles of rotationof the rotatable bonds in all of the molecules andthe mapping of the pharmacophoric features inthe base molecule to corresponding features ineach of the other molecules.

A least-squares procedure is used to overlay eachmolecule onto the base molecule using themappings.

Fitness is calculated as a combination of the

similarity and the number of the overlaid features,

together with the volume integral of the overlay.

Case Study

Case Study• 32 2,4-dioxopyrimidine-1-carboxamides endowed

with acid ceramidase inhibitory activity; pIC50values

• Kennard and Stones (KS) algorithm was used tosplit the dataset (25 compounds were included intraining and 7 compounds in test set)

• Alignment was applied by Field-fit and DistillRigid body

• CoMFA and CoMSIA as 3D QSAR methods

• DiscoTech and GASP as pharmacophore modelbuilding methods

ResultsSummary of CoMFA and CoMSIA results based on two different alignment methods.

PLS statistics Distill alignment Field fit alignment

CoMFA CoMSIA CoMFA CoMSIA

R2cv 0.868 0.804 0.841 0.801

SEP 0.387 0.472 0.424 0.475

R2ncv 0.977 0.927 0.973 0.938

F-ratio 304.08 89.01 249.83 106.27

R2pred 0.899 0.844 0.727 0.846

Results

Result of pharmacophore hypothesis generated by DISCOtech and refined by GASP.

Model

No.

Fitness

score

Size Hits Dmean Features

1 3150.66 5 5 5.9044 DS1, AA1, HY1, AA2, DS2

2* 3399.37 5 5 5.9048 DS1, AA1, HY1, AA2, DS2

3 3364.77 5 5 5.9105 DS1, AA1, HY1, AA2, DS2

4 3286.6 5 5 5.8958 DS1, AA1, HY1, AA2, DS2

Size: total number of features in the models; Hits: number of molecules that matched during the search; Dmean: average inter-point distance

between the features; DS: donor site; AA: acceptor atom; HY: hydrophobic. *The best selected model.

Plots of the experimental versus predicted pIC50 values of 3D-QSAR from both CoMFA and CoMSIA for

the training and test compounds of Distill alignment + Steric contour map

4

5

6

7

8

9

4 5 6 7 8 9

Pre

dic

ted

pIC

50

va

lues

Experimental pIC50 values

CoMFA training compounds

CoMSIA training compounds

CoMFA test compounds

CoMSIA test compounds

Whole the samples in the

space (MLR, PLS, SVM,…)

Regression

Split the space and do local

regress on each sub region

(Decision trees,…)

112

Root or Parent node

Child /Parent node

Leaf or Terminal node

X1

X3X2

Split

Node

If-Then

Best- first

Up-Down

Left-Right

113

CART: Classification and Regression Tree

Binary recursive partitioning tree

• Binary

split parent node into two child nodes

• Recursive Partitioning

each child node can be treated as parent node

For example: X1 is the best variable

A ColdQuinsy

آماس لوزه ها

T<37

Influenza or

Pneumonia?

X3

Cough? Y /N

The

influenzaPneumonia

Cold or

Quinsy?

X 2

Throat?

Normal /Red

T>38.5

Classification ExampleY= {a cold, quinsy, The Influenza, Pneumonia, Healthy}

X1=temperature, X2 =a reddening throat, X3=coughing

1. If X1 < 37 , Y="is health“

2. If X1 ∈ [37, 38.5] and X2="there is reddening of throat", then Y="angina";

3. If X1 ∈ [37, 38.5] and X2="there is no reddening of throat", then Y="to catch cold";

4. If 38.5 >X1 and X2="there is cough", then Y="pneumonia";

5. If 38.5 >X1 and X2="there is no cough", then Y="influenza";

Health

PatientsX1

Temperature

[37.0, 38.5]

Temperature

Cough? Y /N

Throat? Normal /Red

Splitting Space to sub regions

Simulation of physician’s brain in decision

when the quality of a particular split falls below

the threshold, the tree is not grown further along

that branch. The tree is called fully grown tree.

Split-stopping

xp

••••••

•••••

•

y

Ῡr

Ῡl

•Split point

•

Split Criterion (regression):

Advantages of CART

It is nonparametric

Handle all types of variables: numeric, categorical

Immune to outliers

Computational is fast

"Off-the-shelf" procedure: Few tunable parameters

Interpretable model representation: Binary tree graphic

Disadvantages of CART

CART does not use combinations of variables

Tree structures may be unstable: a change in

the sample may give different trees

Low prediction accuracy

Ensemble, to maintain advantages

while increasing both accuracy and the

stability of classification and regression

trees.

Why Ensemble?

Ensemble Methods=

Committee methods =

Consensus methods=

Fusion methods

CART (Breiman, Friedman, Stone, Olshen,

1983)

Bagging (Breiman, 1996)

Random Forest (Ho, 1995; Breiman 2001)

Boosting

AdaBoost (Freund, Schapire, 1997)

Gradient Boosting (Friedman, 2001)

Stochastic Gradient Boosting (Friedman, 1999)

Ensemble methods

125

مثال موسیقی حاصل از جماعتی از

ک نوازندگان خبره زیباتر از یک ت

نوازنده است حتی اگر ساز خود را

خوب کوک کرده باشد

نمره دادن به یک سمینار 2مثال

ه بصورت خوب بد متوسط و یا نمر

دهی به ارائه دهنده توسط یک نمره

دهنده

p

all p variables

(Bagging: Bootstrap aggregating)

Aggregation of experts:

:البته برای رفع خستگی

Aggregation of experts: به فارسی میشه مجلس رو

ای خبرگان هم ترجمه کرد یعنی انصافا ما قبل از آمریکایی ه

این روش ها رو کشف 1357توسط اقای هاشمی و کنی در

کرده بودیم و در موضوع انتخاب رهبری از اونها استفاده

(البته تکبیر فراموش نشه). کرده بودیم

X Y

127

متغییرها را تصادفی انتخابCARTدر اینجا درخت

ییرها نمی کند بلکه در ریگراسیون اول یک سوم متغ

بطور تصادفی انتخاب می شوند بعد ازمیان انها

ر با توجه به متغییری که خطای کل دCARTدرخت

splitکردن را بیشتر کاهش می دهد را، انتخاب می

اجرا میشود CARTکند یعنی همان الگوریتم

CART درRF از نوعunpruned است

128

مثال پیرمردی که به هر فرزند یک

چوب داد و فرزندان را نصیحت می

نند کرد که یک چوب ضعیف رابشک

.کردboostو بعد چوبها را

130

Adaboost R2 (regression):

• 1. Initially, to each training pattern we assign a weight, wi =1

• 2. Construct a regression machine t from that training set.

• 3. Pass every member of the training set through this machine to obtain a prediction

• 4. Calculate a loss for each training sample

131

5. Calculate an average loss:

6. Form β, confidence index:

7. Update the weights:

8. For a particular input xi, each of the T machines makes a prediction ht , t =1, ..., T. Obtain the cumulative prediction hf using the T predictors:Calculate the weighted median for each sample

Adaboost R3

139

Results of different tree based algorithms for crizotinib data set

RMSEC RMSEP Condition(s)

CART 0.2182 1.0271 Prune=On

Bagging 0.4771 0.6898 mtry=241, ntree=1000, node size=5

RF 0.4919 0.6600 mtry=80, ntree=1000, node size=5

Boosting (Adaboost R2)

0.4098 0.7104 Sq, ntree =100

241 variables; Sq: Squared Loss Function, ntree: Number of tree in ensemble, mtry: Number of used Variables

Dock (Accelrys)MIF (Pentacle Software) Duplex

Date post:	16-Apr-2017
Category:	Data & Analytics
Upload:	jahan-b-ghasemi
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Multivariate Chemical Space

Data & Analytics