Computer Aided Drug Design QSAR Related Methods

Importance of PROCESS is not less than PRODUCT5/27/2014 1

Computer Aided Drug Design:

QSAR Related Methods

Jahan B Ghasemi

DDSLab K N Toosi Univ of Tech.

Tehran, Iran

5/27/2014 Importance of PROCESS is not less than PRODUCT

Topics in this Talk are:

General Introduction

Some of These QSAR Steps:

3

Data Pre-Processing

Normalization

Standardization

Variable Selection

Subset Selection

Outlier Detection

Multivariate Analysis

MLR

PCA

PLS

SVM

ANN

CART

Molecular Descriptors

Constitutional

Electronic

Geometrical

Hydrophobic

Lipophilicity

Solubility

Steric

Quantum Chemical

Topological

Molecular Structures

OC1=CC=CC=C1 1D

2D

3D

Statistical Evaluation

R

R2

Q2

MSE

RMSE

PRESS

Importance of PROCESS is not less than PRODUCT

"Well begun is half done“ Aristotle

Renes Descartes in 1619 Quantitative

Measurement in Science

Research Types

Inductive Approach

Deductive Approach

Abductive

Approach

5/27/2014 4

General Introduction

Importance of PROCESS is not less than PRODUCT

Theory

Hypothesis

Confirmation

Observation

Theory

Hypothesis

Observation

Pattern

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.

5/27/2014

-Metrics as soft-computing or soft-modeling are Inductive Research Approaches. Uncertainty

Are humans natural logic reasoners?

No!!!

5


What Do We Need to Know in a Successful QSAR Modeling as a Drug Design Tool?

6

I- Math-Science or Informatique or Informatics Aspect

Linear Algebra

Vectors, Matrices, Tensors…

Homogenous and regular linear and nonlinear simultaneous equations

Graph Theory

Maximal Subgraph

Clique Detection

Multivariate Statistical Analysis

Column Space, Row SpacePattern Recognition

(Dis)Similarity Distance Metrics, Euclidean,

Manhattan, Mahalanobis

Fingerprints, Tanimoto, Jaccard

Supervised and Unsupervised Pattern Recognition

Clustering, Agglomerative(bottom up), Divisive(top down) MLR, PCA, PLS

Optimization

Selection of the most informative variables,

GA

Selection of the most representative objects, KS

Function minimization, Newton, Gauss-Newton, Marquradt-Levenberg

Computer

Computer Graphic

HPC

5/27/2014 Importance of PROCESS is not less than PRODUCT 7


II-

Bio

-Sci

ence

A

spec

t

Chemistry

Organic Chemistry

Quantum/Molecular Mechanics

Forcefield, Conformer, Bioactive Conformer

Medicinal Chemistry

BiologyMolecular Biology

Systems Biology

Pharmacology

Pharmacokinetics

Pharmacodynamics

Toxicity

ADMET

8

Combination of I and II

OMICS

Bioinformatics

Proteomics

Metabolomics

Genomics

Metrics

Biometrics

Chemometrics

Technometrics

Chem(o)informatics


QSAR is related to the most of –OMICS and –

METRICS routines

Bio-Science

Part Start Here:


Chemical Space

(Gathering Information from All Involved Species)

Aggregation

Host-Guest Complex

Receptor-Inhibitor Complex

Macromolecules

Protein

Receptor

Host

Small Molecules

Guest

Ligand

Inhibitor


Chemical Space

Chemical Information

Information

due to

Macromolecule Structure

Information

due to

Aggregation Structure

Information

Due to

Small Molecule Structure


To have and use

Chemical Space:

Extract and Convert

Chemical Information

to

Numerical Values

We Are Calling These Numerical

Values: MolecularDescriptors


Descriptors should be associated with

the following desirable features:

Easy Interpretation

Show Correlation with a Property

Discrimination of Isomers

Independence

Simplicity

Not to be based on properties

Not to be trivially related to other descriptors

Allow for efficient construction

Use familiar structural concepts

Show gradual change with gradual change in structures

5/27/2014Importance of PROCESS is not less than PRODUCT

End Points to Be Modeled

Chemical properties

Boiling point

Retention time

Dielectric constant

Diffusion coefficient

Dissociation constant

Melting point

Reactivity

Solubility

Stability

Thermodynamic properties

Viscosity 5/27/2014Importance of PROCESS is not less than PRODUCT

End Points to Be Modeled

Biological Properties

Bioconcentration

Biodegradation

Carcinogenicity

Drug metabolism and clearance

Inhibition constant

Mutagenicity

Permeability

Blood brain barrier

Skin

Pharmacokinetics

Receptor binding 5/27/2014Importance of PROCESS is not less than PRODUCT

There are more than 5500 Mol.

Des. BUT!

Why do we need more and more Molecular

Descriptors?

Each molecular descriptor takes into account a small part of the whole chemical information contained into the real molecule and, as a consequence, the number of descriptors is continuously increasing

with the increasing request of deeper investigations on chemical and biological systems.

Different descriptors have independent methods or perspectives to view a molecule, taking into account the various features of chemical structure. Molecular

descriptors have now become some of the most important variables used in molecular modeling,

and, consequently, managed by statistics, chemometrics, and chemoinformatics.



Cost to Generate:

Cheap Expensive



How to Calculate Molecular Descriptors?

By Hand! By Software

Dragon SYBYLPaDEL-

DescriptorAdrianaCode



Classes!

Different Classes?

Yes

How many?

Many classes

What are the bases of Classification?

Based of Dimensionality

0D-4D

Geometric Constitutional TopologicalQuantum Chemical

etc….

Based of Origin

Theoretical Experimental

Both!



Do they have equal importance?

0D<1D<2D<2.5D<3D<4D…<nD

Low Information Content High Information Content


Now We Have Molecular Descriptors and Chemical, Molecular or Information Space

But first define and introduce:

Objects=Molecules

Variables=

Descriptors

Object to Variable ratio ≥ 4

Why? Least-Squares Need it!



Math-Science Part Start Here: Using a Very Efficient Way to Show

Chemical Information:

Matrix-Vector

Objects

as rows

Variables as Columns

123....

.

.

.

.

.

.n

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

Objects

as rows

123....

.

.

.

.

.

.n

Preprocessing

On End Point Vector y

nM unit

log Transformation

To Linearized the Variation

To Have LFER InterpretationMean Centering

Autoscaling

On Molecular Descriptors Matrix

X

Mean Centering-Has its general purpose

AutoscalingHas its general purpose

Outlier Detection AD

Dimensionality Reduction

PCA


Geometrical Interpretation of Information Matrix

Spaces

Row Space

Column Space: Object Map

Metrics

Distances

Euclidean and….

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


QSAR Model Building

Based on Molecular Geometry

2D-QSAR 2.5D-QSAR 3D-QSAR


QSAR Model Building

Type of Mapping Function

A Crucial Decision

Linear

MLR kNN PLS

Nonlinear

ANN SVM

Linear+Non-Linear

DT + other Tree and Ensemble

Methods


QSAR Model Building

Object Selection-Data Splitting-Train-Test SetsTo have Good 1- Representative and 2- Diversity

y-Based Method

Randomly Evenly

X-Based Methods

Random Selection

kNNSelection

Similarity Principle

KS,SOM, LMD, Duplex, MDC


QSAR Model Building

Variable Selection

Filters(Subjective)

Uninformative Variable Elimination (UVE)

Correlation Ranking (CR)

Wrappers(Objective)

GA-PLS

Embedded(Selection+Mapping Integrated)

Stepwise Selection

RM, ERM, FFD


QSAR Model Building

Model Validation- There are different Criteria in the Literatures

Residual Analysis

Analysis of Varaince

Applicability Domain

Residual Leverage

Good Leverage

Bad Leverage

Q_Residual T2_Hotelling

Model Precision(Confidence Intervals of Model Parameters)

Bootstrap Resampling

Jackknife Resampling

Model Accuracy(Predic

tion Error)

Internal Validation

Cross Validation

Leave One Out

Leave Many Out

Scrambling

X-randomization

y-randomization

External Validation

External and Fully Unseen or

Independent Data Set


Final word on Validation: The

external Independent Unseen Data

Set Is Mandatory for a Successful

QSAR Model: Do you know why?

Local-X-Global or Induction

Research has Uncertainty

33

Purposes OF QSAR:

Rational Identification of New Leads with:

Pharmacological, Biocidal or Pesticidal Activity.

Optimization of New Leads with:

Pharmacological, Biocidal or PesticidalActivity.

The Rational Design of:

Surface-active agents, Perfumes,

Dyes, and Fine Chemicals. 5/27/2014Importance of PROCESS is not less than PRODUCT

Purposes OF QSAR:

The Selection of Compounds with

Optimal Pharmacokinetic

Properties.

The Prediction of a variety of Physico-

chemical Properties of Molecules.

The Prediction of the Fate of Molecules.

The Rationalization and Prediction of

the Combined Effects of

Molecules.


Purposes OF QSAR:

The Identification of Hazardous

Compounds at Early Stages.

The Designing out of Toxicity and Side-Effects in

New Compounds.

The Prediction of Toxicity of

Compounds to Humans.

The Prediction of Toxicity to

Environmental Species.


Original Data Set

CuratedDataset

Split into training, test and external validation set

Multiple Training

Sets

Y-Randomization

Combi-QSAR modeling

Multiple Test Sets

Activity Prediction

Only Retain Models that

pass both internal and

external accuracy

filters

Validated Predictive

models with High Internal and External

Accuracy

External Validation using Applicability Domain

Virtual Screening Using Applicability

Domain

Experimental Validation

The Most Rigorous and Currently Accepted QSAR Methodology



A S

mal

l Q

ues

tio

n!!

! Why is QSAR alive in spite of the existence of very strong rivals like Docking, MDs, Pharmacophore, SB

and LB methods?

Modeling and taking into account all pharmacological phenomena is:

Nearly or totally impossible even in high level and advanced research laboratories.

38


Thank You All!

1

2

a

d

c

b

Which one would be the third point? a, b, c or d?

1 and 2 have the largest distance.

They are firstly selected. Then

distance between of all unselected

points and all selected points

calculated.

Calculate distances 1a and 2a then min(1a,2a)= 2a.

Calculate distances 1b and 2b then min(1b,2b)= 2b.

Calculate distances 1c and 2c then min(1c,2c)= 1c.

Calculate distances 1d and 2d then min(1d,2d)= 1d.

Max(min(1a,2a),min(1b,2b),min(1c,2c),min(1d,2d))=1d

Then the point d is selected as the Third Point and so on…

1a

2a

1b

2b

1c

2c1d

2d

KSA Graphical Algorithm

5/27/2014 40Importance of PROCESS is not less than PRODUCT


Applicability Domain

41

Q Residuals and Hotelling T2




-1

-0.5

0

0.5

1

1.5

2

2.5

3

3.5

4

4.5

0

2000

4000

6000

8000

10000

12000

1 2 3 4 5 6 7 8 9 10 11

Original Data

log Values

45

Activity Descr 1 Descr 2 … Descr m

Y1 X11 X12 … X1m

Y2 X21 X22 … X2m

… … … … …

Yn Xn1 Xn2 … Xnm

Yi = a0 + a1 Xi1 + a2 Xi2 +…+ am Xim

Don’t consider the nonlinearity effects

Multiple Linear Regression (MLR)


nnn FqtqtqtY 2211

• t latent variables or scores

• q loading vectors

Partial Least Square (PLS)

Robust with respect to collinear descriptors

Only one model optimization parameter (LV’s )

Fast computational 47

http://www.bioss.sari.ac.uk/smart/unix/mplsgxe/slides/glossary.htm

http://www.bioss.sari.ac.uk/smart/unix/mplsgxe/slides/glossary.htm

48

Works on Similarity Principle

A compound in space close to, its kNN compounds from the training set and predicts the activityclass that is most highly represented among these neighbors.

The k-NN scheme is sensitive: 1-Distance Metric 2-Number of training compounds 3- k can be optimized to yield best results.


The k-Nearest Neighbor Method kNN

Artificial Neural Network (ANN)

495/27/2014 Importance of PROCESS is not less than PRODUCT Des

crip

tors

or

Ori

gin

al S

pac

e

Nonli

nea

r or

Hid

den

Spac

e

Pro

per

ties

Bei

ng P

redic

ted

otherwise

if

0:Only the points outside the ε-tube are penalized in a

linear fashion

ε-Insensitive Loss Function

Support Vector Regression (SVR)

Support Vector Classification (SVC)


Non-linear SVMs

Datasets that are linearly separable with some noise work out great:

But what are we going to do if the dataset is just too hard?

How about… mapping data to a higher-dimensional space:

0x

0 x

0 x

x2


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)


Decision Trees as a Greedy Algorithm:

CART: Classification and regression TreeBinary recursive partitioning tree

Best First

Left Right

Up down

Here the Variable to classify

Audience! Here the First

Variable is “Biologist or Not”?

Why? We are in Bio-Dept.


3D-QSARNotes

Advantages over 2D-QSAR

No reliance on experimental values

Can be applied to molecules with unusual substituents

Not restricted to molecules of the same structural class in (Pharmacophre 3D-QSAR case)

Predictive capability 5/27/2014 Importance of PROCESS is not less than PRODUCT 54

No experimental constants or measurements are involved

Properties are known as ‘Fields’

Steric field - defines the size and shape of the molecule

Electrostatic field - defines electron rich/poor regions of molecule

3D-QSAR

Comparative molecular field analysis (CoMFA) - Tripos

Build each molecule using modelling software

Identify the active conformation for each molecule

Identify the pharmacophore

Method

NHCH3

OH

HO

HO

Active conformation

Build 3Dmodel

Define pharmacophore


3D-QSARMethod

NHCH3

OH

HO

HO

Active conformation

Build 3Dmodel

Define pharmacophore


Comparative molecular field analysis (CoMFA) - Tripos

Build each molecule using modelling software

Identify the active conformation for each molecule

Identify the pharmacophore

3D-QSAR

•Place the pharmacophore into a lattice of grid points

Method

•Each grid point defines a point in space

Grid points

..

.

.

.


3D-QSARMethod

•Each grid point defines a point in space

Grid points

..

.

.

.

•Position molecule to match the pharmacophore


3D-QSAR

•A probe atom is placed at each grid point in turn

Method

•Probe atom = a proton or sp3 hybridised carbocation

..

.

.

.Probe atom


3D-QSAR

•A probe atom is placed at each grid point in turn

Method

•Measure the steric or electrostatic interaction of the probe atom with the molecule at each grid point

..

.

.

.Probe atom


3D-QSARMethod

Compound Biological Steric fields (S) Electrostatic fields (E)

activity at grid points (001-998) at grid points (001-098)

S001 S002 S003 S004 S005 etc E001 E002 E003 E004 E005 etc

1 5.1

2 6.8

3 5.3

4 6.4

5 6.1

Tabulate fields for each compound at each grid point

Partial least squares analysis (PLS)

QSAR equation Activity = aS001 + bS002 +……..mS998 + nE001 +…….+yE998 + z

. ..

..


3D-QSAR

•Define fields using contour maps round a representative molecule

Method


A procedure based on the information included in the

MIF

generating a handful of informative variables,

independent of the location of the molecules within the

grid

Two main steps of the procedure of transformation:

Field filtering

Maximum auto-cross correlation(MACC2) encoding.

2 means distance between two points in the space.

2.5D-QSAR or GRIND methodology


MACC2 transform

The MACC transform hasmaximum value of the products ofthe two i and j field values, found

at each different rij distance.

Here the colors represent theactivity of the compounds (blueinactive, red active)

33 means the energy productsproduced by two N1 probes

8 means the 8th variable of auto-correlogram 33


GRID interaction fields

calculated using the N1 probe:

positive (yellow) interactions

describe unfavorable and

negative (blue) interactions

describe favorable interactions

they should have low

energy values

(representing highly

favorable interactions)

they should be as far as

possible one from each

other.



Each number are corresponds to

a specific distance of the fields






One of the unique features of the MACC

transform is that it is possible to trace back the

variables that generated this "most intense"

interaction.


VRS

Technology

Computer Aided Drug Design QSAR Related Methods