Applications of Computational tools in Drug Design & Discovery – Medicinal Chemistry III B. Pharma 6th Semester

Applications of Computational tools in Drug Design & Discovery

Importance of CADD

       Traditional drug discovery includes
random screening, serendipitous discovery and process optimization

       Process takes nearly a decade to
complete with an average expense of ~300 million dollar

       CADD tends to curtail this
expenditure and timeline by providing holistic view

       Evolution of CADD began in 1900s
when Emil Fischer (1894) and Paul Ehlrich (1909) propagated the concept of
receptors and lock and key mechanisms

       Scientific advancements during the
past two decades have changed the way pharmaceutical research generate novel
bioactive molecules

Druglikeness Screening

drug candidates fail in clinical trials because of reasons unrelated to potency
against the intended drug target

and toxicity issues are blamed for more than half of all failures in clinical

part of virtual screening evaluates the druglikeness of small molecules

molecules exhibit favorable absorption, distribution, metabolism, excretion,
and toxicological (ADMET) parameters

Discovery of ACE Inhibitor- Captopril

       Angiotensin converting enzyme is a
carboxypeptidase with a zinc ion as cofactor

       Plays a key role in the
renin-angiotensin cascade involving blood pressure control

       Captopril, a clinically important,
potent and reversible inhibitor of ACE

       Design of captopril is one the early
endeavours and success of structure based drug design

collected for SBDD

       Enzymatic mechanism of ACE was
similar to that of carboxypeptidase A

       Exceptional is ACE cleaves off a
dipeptide whereas a carboxypeptidase A cleave a single amino acid residue from
carboxyl end of a protein

       L-benzylsuccinic acid is a potent
inhibitor of carboxypeptidase A

       BPP5a (HOOC-Glu-Lys-Trp-Ala-Pro-NH),
a potent pentapeptide inhibitor of ACE that was isolated from the venom of the
Brazilian viper

       Captopril is the first ACE inhibitor
to enter clinical use following its approval by USFDA in 1981

       Frontline therapeutic agent for the
treatment of hypertension   and heart


Selectivity or promiscuity? Or both?

       Modern drug development projects
should aim to deliver target specific active compounds

       Approach should be- one disease, one
target and one drug

       Retrospective analysis proved that
approved drugs are promiscuous and bind to several target proteins

       Property of active compound binding
to multiple proteins is termed as polypharmacology

       Sorafenib- A Raf inhibitor
originally developed against lung or pancreatic cancer

       Proved effective against renal cell
cancer by its action on VEGFR2 receptors

       Paxil- A serotonin uptake inhibitor
also binds to beta adrenergic receptors offering plausible explanation for
increased heart rate

       Prediction of compound
polypharmacology has the potential to identify possible adverse effects

       Several methods have been developed
for computational prediction of compound polypharmacology

       At present over 5000 drugs, ~10
million virtual library compounds and 1,45,219 biological macromolecule
structures are available for exploration which are publicly accessible

       Efficient polyphamacology prediction
may be helpful in the future to facilitate drug repurposing and

       To discover more potent drug with
less off-target toxicity

Structure of protein

       Have to focus on detailed three
dimensional structure of biological molecules

       Like shape or structure of a protein
offers clues about the role it plays in the body

       Proteins are shaped to get their job

       May help in developing new medicines
or diagnostic

       Design of lock helps in making

Proteins are the body’s worker molecules

X-ray Crystallography

Strategies for drug design

       Molecular docking and Dynamics

       Quantitative Structure Activity

       Pharmacophore modelling

Virtual Screening

of overall drug likeness

to specifically bind to a given drug target

reduction of enormous virtual chemical space of small organic molecules to
synthesize and/or screen against a specific target to a manageable number of
compounds that exhibit the highest chance to lead to drug candidate

of information

does a drug look like in general?

is known about compounds that interact with the receptor?

is known about the structure of target protein and protein-ligand interactions?

screening is a category of in silico methods that can be utilized to identify
molecules that will (potentially) bind to a target of interest

methods are classified as either structure-based or ligand-based:

Before commencing a screen, ask yourself “given the data I
have, which virtual screening tool is most appropriate?”


       Ligand-Based Drug Design (or
indirect drug design)

       Relies on knowledge of other
molecules that bind to the biological target of interest

       May be used to derive a
pharmacophore model that will define the minimum necessary structural characteristics
a molecule must possess in order to bind to the target


       Structure-Based Drug Design (or
direct drug design):

       Relies on knowledge of the three
dimensional structure of the biological target

       Obtained through methods such as
X-ray crystallography or NMR spectroscopy

       Using the structure of the
biological target, candidate drugs are predicted that will bind with high
affinity and selectivity to the target

       Interactive graphics and the
intuition of a medicinal chemist are further used in this design process

SP & XP modes of docking

       High Throughput virtual screening

       SP- Standard precision and XP- Extra

       XP scoring function include more
stringent terms like hydrophobic effects and charged interactions

       Induced-fit docking (IFD)

       Molecular Dynamics

Chemical compound repositories





Available Chemicals Directory (ACD)


















Quantitative Structure Activity Relationship

       Most popular approach

       QSAR- computational method to
quantify the correlation between chemical structures of series of compounds and
a particular chemical or biological process

       Hypothesis behind the concept is
similar structural or physicochemical properties have similar activity

Methodology of QSAR

       Identification of ligands with
experimentally measured values of desired biological activity.

       Should be of adequate chemically
diversity to have large deviation in activity

       Identify and determine molecular
descriptors associated with various structural and physico-chemical properties
of the molecules under study 

Definition of
molecular descriptor

molecular descriptor is the final result of a logic and mathematical
procedure which transforms chemical information encoded within a symbolic
representation of a molecule into a useful number, or the result of some standardized

       Roberto Todeschini and Viviana Consonni

What are descriptors?

molecular weight,


bonding donors & acceptors





Good descriptors
should characterize molecular properties important for
molecular interactions

suggests that more than 2000
molecular descriptors can be calculated  

       Discover correlations between
molecular descriptors and the biological activity that can explain the
variation in activity in the data set

       Test the statistical stability and
predictive power of the QSAR model

       Goal here is to create a molecular
“fingerprint” for each molecule that relates to its activity

       Essential part of the drug optimization

       Success  of any QSAR model greatly depends on the

a)      choice of molecular descriptors and

b)      ability to generate the appropriate
mathematical relationship between the descriptors and the biological activity
of interest

       Statistical methods applied in QSAR:

Multivariable linear regression analysis (MLR)

       Simplest method to quantify the
molecular descriptor having good correlation with the variation in activity

       For large numbers of descriptors the
MLR method can be time consuming

b) Principle
Component Analysis (PCA)

       Efficient method for reduction of
the number of independent variables

       Highly useful for systems with a
larger number of molecular descriptors than the number of observations

       Results from PCA are often difficult
to analyze

c) Partial
Least Square analysis (PLS)

       Combination of MLR and PCA

       Gives good correlation

       Advantageous for systems with more
than one dependent variable

       Biological systems often display
non-linear relationship between the molecular descriptors and the activity

       Once an initial QSAR model has been
developed is must be validated

       By internal validation and external

Manikanta et
., European Journal of Medicinal Chemistry, 2017, 130, 154-170

Pharmacophore modelling

       Describe 3D features of a molecule

       Molecular descriptors are then
combined to create a pharmacophore that can explain the biological activity of
the ligands

       A pharmacophore is defined as a
spatial arrangement of functional groups and substructures common to active
molecules and essential to biological activities

       On the concept of “molecular
similarity” of small molecules are derived from a series of active compounds
and inactive ones

       Pharmacophore- qualitative aspect


       Describe the two or three
dimensional arrangement of physicochemical properties of a compound.

       Can be used to screen for molecules
with similar arrangement of features.

       In phase , the properties

Include A (acceptor), D (donor), H
(hydrophobic), N (Negative), P (positive), and R (aromatic).

Have defined geometry (point, vector or group).

Are defined via SMARTS patterns.

How to create a
hypothesis manually

1. Prepare molecule:

Prepare Ligands (LipPrep)

Generate Conformers (CobfGen)

2. Create a common hypothesis:

Align known actives and identify common features

3. Screen a library:

Identity compounds that match all or most

Tweak hypothesis if necessary

Shape based screening

       Hard-sphere overlaps

       Conformers for a screening structure
B are aligned to a template structure A.

       Hundreds of trial alignment are
considered for each conformer of B

conformer and alignment with the largest A-B overlaps wins.


       CADD- moving drugs from concept to
the clinic

       Computational structure-based design
supported by medicinal chemistry strategies, can lead to the development of
drugs or

       Drug-like molecules with refined
pharmacological activity that is better than the parent molecule

       SBDD has been recognized as the tool
that facilitated the development of several important drugs in current clinical
use or late stage clinical development

       Allowing many drug discovery
scientists to carry out more focused, hypothesis-driven discovery initiatives
limiting the number of compounds that are synthesized

       Adoption of early stage PK and PD
studies has also contributed greatly to the significantly reduced late-stage
attrition rate of clinical candidates


Leave a Comment