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

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

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

       First part of virtual screening evaluates the druglikeness of small molecules

       Druglike 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

Information 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 failure                                                                                                    


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 done

       May help in developing new medicines or diagnostic

       Design of lock helps in making key 

Proteins are the body’s worker molecules

X-ray Crystallography

Strategies for drug design

       Molecular docking and Dynamics

       Quantitative Structure Activity Relationship 

       Pharmacophore modelling

Virtual Screening

       Assessment of overall drug likeness

       Ability to specifically bind to a given drug target

       Goal- 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

       Source of information

       What does a drug look like in general?

       What is known about compounds that interact with the receptor?

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

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

       These 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 (HTVS)

       SP- Standard precision and XP- Extra Precision

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

       Induced-fit docking (IFD)

       Molecular Dynamics

Chemical compound repositories


Sample Size



Accerlrys Available Chemicals Directory (ACD)












WOMBAT Database






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

       The 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 experiment

       Roberto Todeschini and Viviana Consonni

What are descriptors?

Includes molecular weight,


Hydrogen bonding donors & acceptors

Molecular connectivity

Molecular topology

Molecular geometry


Good descriptors should characterize molecular properties important for molecular interactions

Literature 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 process

       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:

a) 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 techniques

       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 validation

Manikanta et al., 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

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

Ø  Have defined geometry (point, vector or group).

Ø  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 features

·         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.

       The 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


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