PRESENTED BY: HARSHPAL SINGH WAHI, SHIKHA D. POPALI
USEFUL FOR PHARMACY STUDENTS AND ACADEMICS, INDUSTRIALS FOR MOLECULE DEVELOPMENT, MODELING, DRUG DISCOVERY, COMPUTATIONAL TOOLS, MOLECULAR DOCKING ITS TYPES, FACTORS AFFECTING, DIFFERENT STAGES, QSAR ADVANTAGES, NEED
Quantum Mechanics in Molecular modelingAkshay Kank
This slides gives you the information related to computer aided drug design and its application in drug discovery. Also you learn the Quantum mechanics related to the molecular mechanics. Theory related to molecular modeling and how the molecular modeling helps in drug discovery.
PHARMACOHORE MAPPING AND VIRTUAL SCRRENING FOR RESEARCH DEPARTMENTShikha Popali
THE PHARMACOPHORE MAPPING AND VIRTUAL SCRRENING , THESE PRESENTATION INCLUDES THE DEATIL ACCOUNT ON PHARMACOPHORE, MAPPING, ITS IDENTIFIATION FEATURES, ITS CONFORMATIONAL SEARCH, INSILICO DRUG DESIGN, VIRTUAL SCREENING, PHARMACOPHORE BASED SCREENING
Pharmacophore Mapping and Virtual Screening (Computer aided Drug design)AkshayYadav176
Pharmacophore Mapping and Virtual Screening (Computer aided Drug design)
Concept of pharmacophore, Pharmacophore mapping, Identification of pharmacophore features and pharmacophore modeling, Conformation search used in pharmacophore mapping, Virtual screening.
Drug discovery take years to decade for discovering a new drug and very costly
Effort to cut down the research timeline and cost by reducing wet-lab experiment use computer modeling
Others have done the work. Some have used the work. I have spoken only on behalf of their behalf.
molecular docking its types and de novo drug design and application and softw...GAUTAM KHUNE
This ppt deals with all the aspects related to molecular docking ,its types(rigid ,flexible and manual) and screening based on it and also deals with de novo drug design , various softwares available for docking methodologies and applications for molecular docking in new drug design
Cadd and molecular modeling for M.PharmShikha Popali
THE CADD IS FOR THE DRUG DEVELOPMENT THE DIFFERENT STRATEGIES ARE MENTIONED LIKE QSAR MOLECULAR DOCKING, THE DIFFERENT DIMNSIONAL FORMS OF QSAR , THE ADVANCE SAR of it.
Quantum Mechanics in Molecular modelingAkshay Kank
This slides gives you the information related to computer aided drug design and its application in drug discovery. Also you learn the Quantum mechanics related to the molecular mechanics. Theory related to molecular modeling and how the molecular modeling helps in drug discovery.
PHARMACOHORE MAPPING AND VIRTUAL SCRRENING FOR RESEARCH DEPARTMENTShikha Popali
THE PHARMACOPHORE MAPPING AND VIRTUAL SCRRENING , THESE PRESENTATION INCLUDES THE DEATIL ACCOUNT ON PHARMACOPHORE, MAPPING, ITS IDENTIFIATION FEATURES, ITS CONFORMATIONAL SEARCH, INSILICO DRUG DESIGN, VIRTUAL SCREENING, PHARMACOPHORE BASED SCREENING
Pharmacophore Mapping and Virtual Screening (Computer aided Drug design)AkshayYadav176
Pharmacophore Mapping and Virtual Screening (Computer aided Drug design)
Concept of pharmacophore, Pharmacophore mapping, Identification of pharmacophore features and pharmacophore modeling, Conformation search used in pharmacophore mapping, Virtual screening.
Drug discovery take years to decade for discovering a new drug and very costly
Effort to cut down the research timeline and cost by reducing wet-lab experiment use computer modeling
Others have done the work. Some have used the work. I have spoken only on behalf of their behalf.
molecular docking its types and de novo drug design and application and softw...GAUTAM KHUNE
This ppt deals with all the aspects related to molecular docking ,its types(rigid ,flexible and manual) and screening based on it and also deals with de novo drug design , various softwares available for docking methodologies and applications for molecular docking in new drug design
Cadd and molecular modeling for M.PharmShikha Popali
THE CADD IS FOR THE DRUG DEVELOPMENT THE DIFFERENT STRATEGIES ARE MENTIONED LIKE QSAR MOLECULAR DOCKING, THE DIFFERENT DIMNSIONAL FORMS OF QSAR , THE ADVANCE SAR of it.
Structure based drug design- kiranmayiKiranmayiKnv
This presentation helps in detail learning about the structure based drug design. It includes types of structure based drug design and detailed study of docking, de novo drug design.
1. Scoring functions are the mathematical functions used to approximately predict the binding affinity between two molecules after they have been docked.
The evaluation and ranking of predicted ligand conformations is a crucial aspect of structure-based virtual screening.
2. Scoring functions implemented in docking programs make simplifications in the evaluation of modeled complexes.
3. Affinity scoring functions are applied to the energetically best pose found for each molecule, and comparing the affinity scores for different molecules gives their relative rank-ordering.
Review on Computational Bioinformatics and Molecular Modelling Novel Tool for...ijtsrd
Advancement in science and technology has brought a remarkable change in the field of drug discovery. Earlier it was very difficult to predict the target for receptor but nowadays, it is easy and robust task to dock the target protein with ligand and binding affinity is calculated. Docking helps in the virtual screening of drug along with its hit identification. There are two approaches through which docking can be carried out, shape complementary and stimulation approach. There are many procedures involved in carrying out docking and all require different software's and algorithms. Molecular docking serves as a good platform to screen a large number of ligands and is useful in Drug-DNA studies. This review mainly focuses on the general idea of molecular docking and discusses its major applications, different types of interaction involved and types of docking. Rishabh Jain "Review on Computational Bioinformatics and Molecular Modelling: Novel Tool for Drug Discovery" Published in International Journal of Trend in Scientific Research and Development (ijtsrd), ISSN: 2456-6470, Volume-3 | Issue-1 , December 2018, URL: http://www.ijtsrd.com/papers/ijtsrd18914.pdf
http://www.ijtsrd.com/pharmacy/pharmacoinformatics/18914/review-on-computational-bioinformatics-and-molecular-modelling-novel-tool-for-drug-discovery/rishabh-jain
MUTUAL PRODRUG IS DISCUSSED HERE IN DETAIL WITH ITS MULTIPLE TYPES AND FUCTIONAL GROUPS IT IS USE FOR AND FAILURE WITH PRODRUGS, WITH PHARMACEUTICAL EXAMPLES AND STRUCTURE ARE ALSO SHARE, SYNTHETIC APLLICATIONS.
HERE PRESENTS AN OLIGONUCLEOTIDE THERAPY, ITS INTRODUCTION TO OLIGONUCLEOTIDE, ITS TECHNIQUES, DEVELOPED METHODS AND THEIR APP,LICATIONS IN PHARMACEUTICAL ARE HERE DISCUSSED IN DETAIL
OXIDATION [PHARMACEUTICAL PROCESS CHEMISTRY]Shikha Popali
INTRODUCTION TO OXIDATION , WHICH IS PROCESS OF ADDITION OF OXYGEN TO THE COMPOUND IN RPOCESS CHEMISTRY AND LIQUID PHASE OXIDATION AND OTHER OXIDISING AGENTS ARE DISCUSSED.
Synthetic reagent and applications OF ALUMINIUM ISOPROPOXIDEShikha Popali
SYNTHETIC REAGENTS AND APPLICATIONS OF ALUMINIUM ISOPROPOXIDE ITS ALTERNATIVE NAMES AND ITS PHYSICAL PROPERTIRS , HANDLING, STORAGE, PRECAUTIONS, PREPARATIONS, SYNTHETIC APPLICATIONS
PTC IS THE PHASE TRANSFER CATALYSIS HERE TYPES OF PTC ARE DISCUSSED , THEORIES OF CATALYSIS AND MECHANISM OF PTC, ADVANTAGES OF PTC, APPLICATION OF PTC
SWERTIA CHIRATA NATURAL PRODUCT OF PHARMACEUTICALSShikha Popali
HERE THE NATURAL PRODUCT SERTIA CHIRATA IS DISCUSSED WITH ITS COMMON NAME, CHEMICAL CONSTITUENTS, ACTIVE CONSTITUENTS, SAR, MEDICINAL ACTIVITY AND MORE
THE DCC I.E. DICYCLOCARBODIIMDE IS A REAGENT AND HERE THE DETAIL ACCOUNT ON IT IS GIVEN INCLUDING MOLECULAR WEIGHT, STRUCTURE, SYNTHESIS AND PHYSICAL PARAMETERS AND APPLICATIONS FOR OTERS SYNTHESIS ARE ALSO DISCUSSED, THE DIFFERENT SYNTHESIS WITH DCC COMBINATION ARE ALSO MENTIONED
COMPARATIVE EVALUATION OF DIFFERENT PARACETAMOL BRANDSShikha Popali
THE PARACETAMOL TABLETS IS COMMONLY TAKEN AND PRESCRIBED FOR FEVER , SO HERE WE HAVE MADE PRACTICAL IS IT TRUE EVALUATION LABEL AND WHICH BRAND IS MORE SAFE.
Multi-source connectivity as the driver of solar wind variability in the heli...Sérgio Sacani
The ambient solar wind that flls the heliosphere originates from multiple
sources in the solar corona and is highly structured. It is often described
as high-speed, relatively homogeneous, plasma streams from coronal
holes and slow-speed, highly variable, streams whose source regions are
under debate. A key goal of ESA/NASA’s Solar Orbiter mission is to identify
solar wind sources and understand what drives the complexity seen in the
heliosphere. By combining magnetic feld modelling and spectroscopic
techniques with high-resolution observations and measurements, we show
that the solar wind variability detected in situ by Solar Orbiter in March
2022 is driven by spatio-temporal changes in the magnetic connectivity to
multiple sources in the solar atmosphere. The magnetic feld footpoints
connected to the spacecraft moved from the boundaries of a coronal hole
to one active region (12961) and then across to another region (12957). This
is refected in the in situ measurements, which show the transition from fast
to highly Alfvénic then to slow solar wind that is disrupted by the arrival of
a coronal mass ejection. Our results describe solar wind variability at 0.5 au
but are applicable to near-Earth observatories.
Slide 1: Title Slide
Extrachromosomal Inheritance
Slide 2: Introduction to Extrachromosomal Inheritance
Definition: Extrachromosomal inheritance refers to the transmission of genetic material that is not found within the nucleus.
Key Components: Involves genes located in mitochondria, chloroplasts, and plasmids.
Slide 3: Mitochondrial Inheritance
Mitochondria: Organelles responsible for energy production.
Mitochondrial DNA (mtDNA): Circular DNA molecule found in mitochondria.
Inheritance Pattern: Maternally inherited, meaning it is passed from mothers to all their offspring.
Diseases: Examples include Leber’s hereditary optic neuropathy (LHON) and mitochondrial myopathy.
Slide 4: Chloroplast Inheritance
Chloroplasts: Organelles responsible for photosynthesis in plants.
Chloroplast DNA (cpDNA): Circular DNA molecule found in chloroplasts.
Inheritance Pattern: Often maternally inherited in most plants, but can vary in some species.
Examples: Variegation in plants, where leaf color patterns are determined by chloroplast DNA.
Slide 5: Plasmid Inheritance
Plasmids: Small, circular DNA molecules found in bacteria and some eukaryotes.
Features: Can carry antibiotic resistance genes and can be transferred between cells through processes like conjugation.
Significance: Important in biotechnology for gene cloning and genetic engineering.
Slide 6: Mechanisms of Extrachromosomal Inheritance
Non-Mendelian Patterns: Do not follow Mendel’s laws of inheritance.
Cytoplasmic Segregation: During cell division, organelles like mitochondria and chloroplasts are randomly distributed to daughter cells.
Heteroplasmy: Presence of more than one type of organellar genome within a cell, leading to variation in expression.
Slide 7: Examples of Extrachromosomal Inheritance
Four O’clock Plant (Mirabilis jalapa): Shows variegated leaves due to different cpDNA in leaf cells.
Petite Mutants in Yeast: Result from mutations in mitochondrial DNA affecting respiration.
Slide 8: Importance of Extrachromosomal Inheritance
Evolution: Provides insight into the evolution of eukaryotic cells.
Medicine: Understanding mitochondrial inheritance helps in diagnosing and treating mitochondrial diseases.
Agriculture: Chloroplast inheritance can be used in plant breeding and genetic modification.
Slide 9: Recent Research and Advances
Gene Editing: Techniques like CRISPR-Cas9 are being used to edit mitochondrial and chloroplast DNA.
Therapies: Development of mitochondrial replacement therapy (MRT) for preventing mitochondrial diseases.
Slide 10: Conclusion
Summary: Extrachromosomal inheritance involves the transmission of genetic material outside the nucleus and plays a crucial role in genetics, medicine, and biotechnology.
Future Directions: Continued research and technological advancements hold promise for new treatments and applications.
Slide 11: Questions and Discussion
Invite Audience: Open the floor for any questions or further discussion on the topic.
(May 29th, 2024) Advancements in Intravital Microscopy- Insights for Preclini...Scintica Instrumentation
Intravital microscopy (IVM) is a powerful tool utilized to study cellular behavior over time and space in vivo. Much of our understanding of cell biology has been accomplished using various in vitro and ex vivo methods; however, these studies do not necessarily reflect the natural dynamics of biological processes. Unlike traditional cell culture or fixed tissue imaging, IVM allows for the ultra-fast high-resolution imaging of cellular processes over time and space and were studied in its natural environment. Real-time visualization of biological processes in the context of an intact organism helps maintain physiological relevance and provide insights into the progression of disease, response to treatments or developmental processes.
In this webinar we give an overview of advanced applications of the IVM system in preclinical research. IVIM technology is a provider of all-in-one intravital microscopy systems and solutions optimized for in vivo imaging of live animal models at sub-micron resolution. The system’s unique features and user-friendly software enables researchers to probe fast dynamic biological processes such as immune cell tracking, cell-cell interaction as well as vascularization and tumor metastasis with exceptional detail. This webinar will also give an overview of IVM being utilized in drug development, offering a view into the intricate interaction between drugs/nanoparticles and tissues in vivo and allows for the evaluation of therapeutic intervention in a variety of tissues and organs. This interdisciplinary collaboration continues to drive the advancements of novel therapeutic strategies.
Richard's entangled aventures in wonderlandRichard Gill
Since the loophole-free Bell experiments of 2020 and the Nobel prizes in physics of 2022, critics of Bell's work have retreated to the fortress of super-determinism. Now, super-determinism is a derogatory word - it just means "determinism". Palmer, Hance and Hossenfelder argue that quantum mechanics and determinism are not incompatible, using a sophisticated mathematical construction based on a subtle thinning of allowed states and measurements in quantum mechanics, such that what is left appears to make Bell's argument fail, without altering the empirical predictions of quantum mechanics. I think however that it is a smoke screen, and the slogan "lost in math" comes to my mind. I will discuss some other recent disproofs of Bell's theorem using the language of causality based on causal graphs. Causal thinking is also central to law and justice. I will mention surprising connections to my work on serial killer nurse cases, in particular the Dutch case of Lucia de Berk and the current UK case of Lucy Letby.
THE IMPORTANCE OF MARTIAN ATMOSPHERE SAMPLE RETURN.Sérgio Sacani
The return of a sample of near-surface atmosphere from Mars would facilitate answers to several first-order science questions surrounding the formation and evolution of the planet. One of the important aspects of terrestrial planet formation in general is the role that primary atmospheres played in influencing the chemistry and structure of the planets and their antecedents. Studies of the martian atmosphere can be used to investigate the role of a primary atmosphere in its history. Atmosphere samples would also inform our understanding of the near-surface chemistry of the planet, and ultimately the prospects for life. High-precision isotopic analyses of constituent gases are needed to address these questions, requiring that the analyses are made on returned samples rather than in situ.
Professional air quality monitoring systems provide immediate, on-site data for analysis, compliance, and decision-making.
Monitor common gases, weather parameters, particulates.
3. DRUG DISCOVERY & DEVELOPMENT
Identify disease
Isolate protein
involved in
disease (2-5 years)
Find a drug effective
against disease protein
(2-5 years)
Preclinical testing
(1-3 years)
Formulation
Human clinical trials
(2-10 years)
Scale-up
FDA approval
(2-3 years)
Drug Design
- Molecular Modeling
- Virtual Screening
4. TECHNOLOGY IS IMPACTING THIS PROCESS
Identify disease
Isolate protein
Find drug
Preclinical testing
GENOMICS, PROTEOMICS & BIOPHARM.
HIGH THROUGHPUT SCREENING
MOLECULAR MODELING
VIRTUAL SCREENING
COMBINATORIAL CHEMISTRY
IN VITRO & IN SILICO ADME MODELS
Potentially producing many more targets
and “personalized” targets
Screening up to 100,000 compounds a
day for activity against a target protein
Using a computer to
predict activity
Rapidly producing vast numbers
of compounds
Computer graphics & models help improve activity
Tissue and computer models begin to replace animal testing
5. MODERN DRUG DISCOVERY PROCESS
Target
identification
Target
validation
Lead
identification
Lead
optimization
Preclinical
phase
Drug
discovery
2-5 years
• Drug discovery is an expensive process involving high R & D cost
and extensive clinical testing. A typical development time is estimated
to be 10-15 years.
6-9 years
6. Target
Selection
• Cellular
and
Genetic
Targets
• Genomics
• Proteomics
• Bioinformat
ics
Lead
Discovery
• Synthesis
and
Isolation
• Combinator
ial
Chemistry
• Assay
developme
nt
• High-
Throughput
Screening
Medicinal
Chemistry
• Library
Developme
nt
• SAR
Studies
• In Silico
Screening
• Chemical
Synthesis
In Vitro
Studies
• Drug
Affinity and
Selectivity
• Cell
Disease
Models
• MOA
• Lead
Candidate
Refinement
In Vivo
Studies
• Animal
models of
Disease
States
• Behavioura
l Studies
• Functional
Imaging
• Ex-Vivo
Studies
Clinical
Trials and
Therapeutic
s
7. TARGET SELECTION
• Target selection in drug discovery is defined as the decision
to focus on finding an agent with a particular biological
action that is anticipated to have therapeutic utility
• Target identification: to identify molecular targets that are
involved in disease progression.
• Target validation: to prove that manipulating the
molecular target can provide therapeutic benefit for
patients.
8. TARGET SELECTION
Biochemical Classes of Drug Targets
G-protein coupled receptors - 45%
enzymes - 28%
hormones and factors - 11%
ion channels - 5%
nuclear receptors - 2%
12. MOLECULAR MODELING
Model construction
Molecular mechanics
Conformational searches
Molecular dynamics
QSAR/3D QSAR
Structure-based drug design
Rational drug design
NMR and X-ray
structure determination
Combinatorial chemistry
Chemical similarity
Chemical diversity
Homology modeling
Bioinformatics
Chemoinformatics
QM, MM methods
13. COMPUTATIONAL TOOLS
Quantum Mechanics (QM)
• Considers electronic effect & electronic structure of
the molecule
• Calculates charge distribution and orbital energies
• Can simulate bond breaking and formation
• Upper atom limit of about 100-120 atoms
14. Molecular Mechanics (MM)
Totally empirical technique applicable to both small and
macromolecular systems
A molecule is described as a series of charged points
(atoms) linked by springs (bonds)
The potential energy of molecule is described by a
mathematical function called a FORCE FIELD
15. Receptor Structure
Unknown Known
Unknown
Generate 3D structures,
Similarity/dissimilarity
Homology modelling
HTS, Comb. Chemistry
(Build the lock, then find the key)
Active Site Search
Receptor Based DD
de NOVO design,
3D searching
(Build or find the key that fits the lock)Ligand
Structure
Known
Indirect DD
Ligand-Based DD
Analogs Design
2D/3D QSAR &
Pharmacophore
Rational Drug Design
(Structure-based DD)
Molecular Docking
(Drug-Receptor
interaction)
BASIC MODELING STRATEGIES
17. Drugs or ligand binds with receptor and
mediate their Pharmacological action
Drug
Receptor
Pharmacological
action
11/29/2019Stucture-activity relationship 19
18. Docking attempts to find the “best” matching
between two molecules
Docking
MOLECULAR DOCKING
19. MOLECULAR DOCKING
It is a method which predicts the preferred orientation of one
ligand when bound in an active site to form a stable complex.
Docking is used for finding binding modes of protein with
ligands or inhibitors. They are able to generate a large number
of possible structures.
In molecular docking, we attempt to predict the structure of
the intermolecular complex formed between two or more
molecules.
21
20. TYPES OF DOCKING :-
• There are to types of docking that are :-
1. Rigid docking : In rigid docking the molecules are rigid, in 3D
space of one of the molecule which brings it to an optimal fit
with other molecule in terms of scoring function. Also the
internal geometry of both the receptor and ligand are rigid.
2. Flexible docking : In this type of docking the molecules are
flexible, conformations of the receptor and ligand molecules as
they appear in complex. 22
22. TYPES OF DOCKING STUDIES :-
1. Protein-Protein docking : These interactions occur between
two proteins that are similar in size. Conformational
changes are limited by steric constraints and thus are said
to be rigid.
24
23. 2. Protein Receptor - Ligand docking : protein receptor -ligand
docking is used to check the structure, position and
orientation of a protein when it interacts with small
molecules like ligands. Protein receptor-ligand motifs fit
together tightly, and are often referred to as a lock and
key mechanism.
25
24. • Protein - Ligand Protein - Protein
•
•
•
• Protein - Nucleotide
•
26
25. TYPES OF INTERACTIONS :-
•Interactions between particles can be defined as a consequence of forces between the
molecules contained by the particles. These forces are divided into four categories :-
1. Electrostatic forces - Forces with electrostatic origin due to the charges residing in
the matter. The most common interactions are charge-charge, charge dipole and
dipole-dipole.
2. Electrodynamics forces - The most widely known is the Van der Waals
interactions.
3. Steric forces - Steric forces are generated when atoms in different molecules come
into very close contact with one another and start affecting the reactivity of each
other. The resulting forces can affect chemical reactions and the free energy of a
system.
4. Solvent-related forces - These are forces generated due to chemical reactions
between the solvent and the protein or ligand. Examples are Hydrogen bonds
(hydrophilic interactions) and hydrophobic interactions.
27
26. FACTORS AFFECTING DOCKING :-
The factors affecting docking are of two different forces that are as follows :-
1. Intra-molecular forces :-
a. Bond length
b. Bond angle
c. Dihedral angle
2. Inter-molecular forces :-
a. Electrostatic
b. Dipolar
c. H-bonding
d. Hydrophobicity
e. Van der Waal’s forces
28
27. STAGES OF DOCKING :-
1.Target / Receptor selection and preparation
2.Ligand selection and preparation
3.Docking
4. Evaluating docking results
29
28. Target structure
A target 3D structure is required!
The PDB (protein databank)
➔ Xray diffraction
● No size limit
● More accurate
● Unique structure (of the crystal)
● Crystallization problems
● Hydrogen are missed
➔ NMR
● Lowest accuracy
● Solution structure
● Size limit around residues (for a
protein)
➔ Homology modelling
● Free and quick
● No experimental
● Low precision of sidechains
● Sequence similarity or
identity?
30. 3.2. Target structure treatment
Experimental structures are far from being perfect!
You can find in them:
o Ions
o Water
o Soap
o Glycosyl
o Antibody
o Chaperon proteins
o Missing atoms…
You must clean the pdb file
31. Where is the interacting site on the protein?
Three major methods:
Experimental complex
Safer method
We need an identical mechanism for ligands
Analysis of structural properties
Cavity detection is complex
More an art than a definite method
Molecular docking of the whole protein
Time consuming and boring
Needs a lot of docking poses (~ 1000) to do
statistics
Generally we have “surprising” results
Interacting site:
33. Common Software's Used for Docking Purpose :-
Sr. No. Docking
Program
Year
Published
Docking Approach
1. DOCK 1988 Shape fitting
(sphere sets)
2. Auto Dock 1990 Genetic
Algorithm, Simulated
Annealing
3. Flex X 2001 Incremental
construction
4. FRED 2003 Shape fitting
5. VLifeMDS Protein-ligand based design
6. FLOG 1994 Rigid body docking program
7. HADDOCK 2003 Protein-Protein docking, Protein-
Ligand docking
35
34. STEPS INVOLVED IN DOCKING PROGRAM :-
1. Get the complex from protein data bank
2. Clean the complex
3. Add the missing hydrogen / side chain atoms and minimize the complex
4. Clean the minimized complex
5. Separate the minimized complex in macromolecule (lock) and ligand (key)
6. Prepare the docking suitable files for lock and key
7. Prepare all the needing files for docking
8. Run the docking
9. Analyze the docking results
36
35. Compounds + biological activity
New compounds with
improved biological activity
QSAR
Correlate chemical structure with activity using statistical approach
QSAR and Drug Design
36. QSAR?
A QSAR is a mathematical relationship between a
biological activity of a molecular system and its
geometric and chemical characteristics.
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.
37. The number of compounds required for synthesis
in order to place 10 different groups in 4 positions
of benzene ring is 104
Solution: Synthesize a small number of compounds
and from their data derive rules to predict the
biological activity of other compounds.
Why QSAR ?
39. Molecular Structure ACTIVITIES
Representation Feature Selection & Mapping
Descriptors
Quantitative structure-activity relationships correlate, within congeneric
series of compounds, their chemical or biological activities, either with
certain structural features or with atomic, group or molecular
descriptors.
Quantitative Structure Activity Relationship (QSAR)
Katiritzky, A. R. ; Lovanov, V. S.; Karelson, M. Chem. Soc. Rev. 1995, 24, 279-287
40. Rationale for QSAR studies
• In drug design, in-vitro potency addresses only part of the
need; a successful drug must also be able to reach its target in
the body while still in its active form.
• The in-vivo activity of a substance is a composite of many
factors, including the intrinsic reactivity of the drug, its
solubility in water, its ability to pass the blood-brain barrier,
its non- reactivity with non-target molecules that it encounters
on its way to the target, and others.
• A quantitative structure-activity relationship (QSAR) correlates
measurable or calculable physical or molecular properties to
some specific biological activity in terms of an equation.
• Once a valid QSAR has been determined, it should be possible
to predict the biological activity of related drug candidates
before they are put through expensive and time-consuming
biological testing. In some cases, only computed values need
to be known to make an assessment.
41. Advantages of QSAR:
• Quantifying the relationship between structure and activity
provides an understanding of the effect of structure on
activity, which may not be straightforward when large
amounts of data are generated.
• There is also the potential to make predictions leading to the
synthesis of novel analogues. Interpolation is readily justified,
but great care must be taken not to use extrapolation outside
the range of the data set.
• The results can be used to help understand interactions
between functional groups in the molecules of greatest
activity, with those of their target
42. DATA FOR QSAR
• All analogs belong to congeneric series.
• All analogs have the same mechanism of action.
• All analogs bind in a similar fashion.
• The effect of isosteric replacement can be predicted.
• Binding affinity is correlated with interaction energy (e.g.,
ionic effects are approx. const.)
• Biological activity is correlated with binding affinity (e.g.,
not with transport properties).
44
43. WHY DO WE NEED DESCRIPTORS?
• Relate structure to activity (QSAR).
• Descriptors act as independent variable.
• Describe different aspects of molecules.
• Compare different molecular structures.
• Compare different conformation of same molecule.
45
45. TYPES OF QSAR
• 1D-QSAR correlating activity with global molecular properties like pKa,
log P, etc.
• 2D-QSAR correlating activity with structural patterns like connectivity
indices, 2D-pharmacophores, without taking into account the 3D-
representation of these properties.
• 3D-QSAR correlating activity with non-covalent interaction fields
surrounding the molecules.
• 4D-QSAR additionally including ensemble of ligand configurations in
3D-QSAR.
• 5D-QSAR explicitly representing different induced-fit models in 4D-
QSAR.
• 6D-QSAR further incorporating different solvation models in 5D-QSAR.
• GQSAR further incorporating different fragments of molecules
47
46. 2D QSAR
• Correlation of physicochemical descriptors with biological
activity.
• Typical QSAR methodology.
• Alignment independent
• Can not predict the interaction potential of molecules
under study.
•Example of 2DQSAR
• pIC50 = 0.0215+ 0.1743(±0.0911) SaasCcount
• -0.0084(±0.0002) XAHydrophilicArea
• + 0.0590(±0.0269) SsOHE-index
• -0.1742(±0.1000) SaaNE-index
48
47. METHODS:
• Quantitative regression techniques
• Qualitative pattern recognition techniques
• Hammet relationships as linear free energy relationship (LFER).
• Statistical parameters: Craig plot
• Simple linear regression
• Multiple Linear Regression(MLR), also termed as Ordinary Least
Squares (OLS)
• PLS- Partial Least Square fitting
• Adaptive Least Squares (ALS)
• PCA- Principal Component Analysis
49
BA = S Iij Fij + k
48. 3D QSAR
• 3D-QSAR refers to the application of force field calculations requiring three-dimensional
structures, e.g. based on protein crystallography or molecule superimposition.
• It examines the steric fields (shape of the molecule), the hydrophobic regions (water-
soluble surfaces), and the electrostatic fields.
• Alignment dependent.
• Can predict the interaction potential of molecules under study.
• pIC50 = 4.1638+ 0.0324 S_989 + 0.3716 S_141 + 0.2655 E_902 +0.1045 E_709 50
49. DESCRIPTORS FOR 3D QSAR
• Descriptors are calculated as hydrophilic, steric and electrostatic
interaction energies at the lattice points of the grid using a
methyl probe of charge +1.
• This field provides a description of how each molecule will tend
to bind in the active site.
• Field descriptors typically consist of a sum of one or more spatial
properties, such as steric factors or the electrostatic potential.
51
O
N
O
N
50. G QSAR
• GQSAR is a breakthrough patent pending methodology that significantly enhances the
use of QSAR as an approach for new molecule design. As a predictive tool for activity,
this method is significantly superior to conventional 3D and 2D QSAR.
• In this method, every molecule of the data set is considered as a set of fragments, the
fragmentation scheme being either template based or user defined.
• The descriptors are evaluated for each fragment and a relationship between these
fragment descriptors is formed with the activity of the whole molecule.
• Unlike conventional QSAR, with the GQSAR, researchers get critically important site
specific clues within a molecule where a particular descriptor needs to be modified.
• GQSAR approach builds upon the basic focus of QSAR by applying the knowledge
gained in the field over the past four decades in terms of molecular descriptors,
statistical modeling etc.
52
52. VALIDATION OF QSAR MODELS
• Statistical quality
• Fitting R2
• Predictability Q2
• Outliers
• Prediction reliability for external set
54
53. ADVANTAGES OF QSAR:
• Quantifying the relationship between structure and activity provides an
understanding of the effect of structure on activity, which may not be
straightforward when large amounts of data are generated.
• There is also the potential to make predictions leading to the synthesis of
novel analogues. Interpolation is readily justified, but great care must be
taken not to use extrapolation outside the range of the data set.
• The results can be used to help understand interactions between
functional groups in the molecules of greatest activity, with those of their
target. To do this it is important to interpret any derived QSAR in terms of
the fundamental chemistry of the set of analogues, including any outliers.55
54. DISADVANTAGES OF QSAR:
• False correlations may arise through too heavy a reliance being
placed on biological data, which, by its nature, is subject to
considerable experimental error.
• Frequently, experiments upon which QSAR analyses depend, lack
design in the strict sense of experimental design. Therefore the data
collected may not reflect the complete property space. Consequently,
many QSAR results cannot be used to confidently predict the most
likely compounds of best activity.
• Various physicochemical parameters are known to be cross-
correlated. Therefore only variables or their combinations that have
little covariance should be used in a QSAR analysis; similar
considerations apply when correlations are sought for different sets
of biological data
56
55. Molecular descriptors used in QSAR
Type Descriptors
Hydrophobic Parameters Partition coefficient ; log P
Hansch’s substitution constant; π
Hydrophobic fragmental constant; f, f’
Distribution coefficient; log D
Apparent log P
Capacity factor in HPLC; log k’ , log k’W
Solubility parameter; log S
56. Molecular descriptors used in QSAR
Type Descriptors
Electronic Parameters Hammett constant; σ, σ +, σ -
Taft’s inductive (polar) constant; σ*
Swain and Lupton field parameter
Ionization constant; pKa , ΔpKa
Chemical shifts: IR, NMR
57. Molecular descriptors used in QSAR
Type Descriptors
Steric Parameters Taft’s steric parameter; Es
Molar volume; MV
Van der waals radius
Van der waals volume
Molar refractivity; MR
Parachor
Sterimol
58. Molecular descriptors used in QSAR
Type Descriptors
Quantum chemical descriptors Atomic net charge; Qσ, Qπ
Superdelocalizability
Energy of highest occupied molecular orbital;
EHOMO
Energy of lowest unoccupied molecular orbital;
ELUMO
59. Molecular descriptors used in QSAR
Type Descriptors
Spatial Descriptor Jurs descriptors,
Shadow indices,
Radius of Gyration
Principle moment of inertia
60. Classification of Descriptors Based on the
Dimensionality of their Molecular Representation
Molecular
representation
Descriptor Example
0D Atom count, bond
counts, molecular
weight, sum of
atomic properties
Molecular weight, average
molecular weight number of:
atoms, hydrogen atoms carbon
atoms, hetero-atoms, non-
hydrogen atoms, double bonds,
triple bonds, aromatic bonds,
rotatable bonds, rings, 3-
membered ring, 4- membered
ring, 5-membered ring, 6-
membered ring
61. Classification of Descriptors Based on the
Dimensionality of their Molecular Representation
Molecular
representation
Descriptor Example
1D Fragments
counts
Number of: primary C, secondary C,
tertiary C, quaternary C, secondary
carbon in ring, tertiary carbon in
ring, quaternary carbon in ring,
unsubstituted aromatic carbon,
substituted carbon, number of H-
bond donar atoms, number of H-
bond acceptor atoms, unsaturation
index,
hydrophilic factor, molecular
refractivity.
62. Classification of Descriptors Based on the
Dimensionality of their Molecular Representation
Molecular
representation
Descriptor Example
2D Topological
descriptors
Zagreb index, Wiener index, Balaban
J index, connectivity indices chi (χ),
kappa (К) shape indices
3D Geometrical
descriptors
Radius of gyration, E-state
topological
parameters, 3D Wiener index, 3D
Balaban index
63. Pharmacophore
• A pharmacophore that indicates the key features of a series of
active molecules
• In drug design, the term 'pharmacophore‘ refers to a set of
features that is common to a series of active molecules
• Hydrogen-bond donors and acceptors, positively and
negatively charged groups, and hydrophobic regions are
typical features
• We will refer to such features as 'pharmacophoric groups'
64.
65. 3. PHARMACOPHORE
• Defines the important groups involved in binding
• Defines the relative positions of the binding groups
• Need to know Active Conformation
• Important to Drug Design
• Important to Drug Discovery
66. 3D-Pharmacophores
• A three-dimensional pharmacophore specifies the spatial relation-
ships between the groups
• Expressed as distance ranges, angles and planes
• A commonly used 3D pharmacophore for antihistamines contains
two aromatic rings and a tertiary nitrogen
73. 3D Pharmacophore
• Defines relative positions in space of important
binding groups
Example
N
HO
HO
N
x
x
74. • Defines relative positions in space of the binding
interactions which are required for activity / binding
Generalised Bonding Type Pharmacophore
Ar
Ar
x
x
y
Base
HBA
HBD
HBA Base
HBA
HBD
HBA
y
75. Pharmacophores from Target Binding Sites
H-bond
donor or
acceptor
aromatic
center
basic or
positive
center
H-bond
donor or
acceptor
aromatic
center
basic or
positive
center
Pharmacophore
O
H
CO2
ASP
SER
PHE
Binding
site
78. INHOUSE DEVELOPED LEADS
80
Factor IXA Inhibitor
(Med Chem Res ,2013, 22:976–985)
Calcium Channel Blocker
(J.Kor. Chem. Soc 57,2013)
NN
S N
H
CH3
O N N
N
N
CN
O
S
H2NHN
O
79. DRUG DESIGN SUCCESSES
While we are still waiting for a drug totally designed from
scratch, many drugs have been developed with major
contributions from computational methods
N
F
O
N
HN
CO2H
Et
norfloxacin (1983)
antibiotic
first of the 6-fluoroquinolones
QSAR studies
dorzolamide [Trusopt] (1994)
glaucoma treatment
carbonic anhydrase inhibitor
SBLD and ab initio calcs
S
O2
S
SO2NH2
NH
MeO
MeO
O
N
donepezil (1996)
Alzheimer's treatment
acetylcholinesterase inhibitor
shape analysis and docking studies
N
NHN
N
N
N
HO
Cl
Bu
losartan [Cozaar] (1995)
angiotensin II antagonist
anti-hypertensive
Modeling Angiotensin II octapeptide
H
N
NMe 2
NH
O
H
O
zolmatriptan [Zomig] 1995
5-HT1D agonist
migraine treatment
Molecular modeling
80. SUMMARY
Drug Discovery is a multidisciplinary, complex,
costly and intellect intensive process.
Modern drug design techniques can make drug
discovery process more fruitful & rational.
Knowledge management and technique specific
expertise can save time & cost, which is a
paramount need of the hour.