Computer aided drug design

By Dr Sameh Ahmad M- Abdelghany

Basic Drug designingSection 01

Drug Design SLIDE 3

is the inventive process of finding new medications based on the knowledge of a biological target.

It involves the design of molecules that are complementary in shape and charge to the biomolecular target with which they interact and therefore will bind to it.

Life Cylce of Drug Design SLIDE 4

Synthetic or Natural

Compounds

Preclinical Trails Clinical Trails

1st step 2nd step Drug3rd step

Traditional Life Cycle

Modern Drug Design SLIDE 5

Target selection Identification of Target

Verification of target

Target Selection

Lead Identificati

on Screen Development

High throughput screening

Secondary assay

Lead Optimizati

on Lead explosion Potency in

disease Pharmacokinetics

Final

1st step 2nd step Drug3rd step

Drug Designing… SLIDE 6 Selected/designed molecule

should be: Organic small molecule. Complementary in shape to the

target. Oppositely charge to the

biomolecular target .

Drug Designing… SLIDE 7This molecule will: interact with target bind to the target activates or inhibits the function

of a biomolecule such as a protein

Drug Designing… SLIDE 8 Drug design frequently but not

necessarily relies on computer modeling techniques.

This type of modeling is sometimes referred to as computer-aided drug design.

Mechanism based drug design SLIDE 9

When the disease process is understood at the molecular level and the target molecule(s) are defined, drugs can be designed specifically to interact with the target molecule in such a way as to disrupt the disease.

Computer-aided drug design(CADD) SLIDE 10

CADD represents computational methods and resources that are used to facilitate the design and discovery of new therapeutic solutions.

Introduction to CADD SLIDE 11 Drug design with the help of computers may be used at any of the following

stages of drug discovery: hit identification using virtual screening (structure- or ligand-based design) hit-to-lead optimization of affinity and selectivity (structure-based

design, QSAR, etc.) lead optimization: optimization of other pharmaceutical properties while

maintaining affinity.

Objective of CADD SLIDE 12 To change from:

Random screening against disease assays Natural products, synthetic chemicals

To: Rational drug design and testing Speed-up screening process Efficient screening (focused, target directed) De novo design (target directed) Integration of testing into design process Fail drugs fast (remove hopeless ones as early as possible)

Types of drug design SLIDE 131) Ligand based drug design 2)Structure based drug design

Ligand-based drug design SLIDE 14 relies on knowledge of other

molecules that bind to the biological target of interest.

used to derive a pharmacophore model that defines the minimum necessary structural characteristics a molecule must possess in order to bind to the target.

Ligand-based drug design SLIDE 15 a model of the biological target may be built based on the knowledge of what

binds to it, and this model in turn may be used to design new molecular entities that interact with the target.

Alternatively, a quantitative structure-activity relationship (QSAR), in which a correlation between calculated properties of molecules and their experimentally determined biological activity, may be derived. These QSAR relationships in turn may be used to predict the activity of new analogs.

Structure-based drug design: SLIDE 16 relies on knowledge of the three

dimensional structure of the biological target obtained through :

1. x-ray crystallography 2. NuclearMagnetic Resonance

(NMR) spectroscopy.

NMR spectroscopy

X-ray crystallography

Structure-based drug design SLIDE 17

If an experimental structure of a target is not available, it may be possible to create a homology model of the target based on the experimental structure of a related protein.

Homology modeling, also known as comparative modeling of protein, refers to constructing an atomic-resolution model of the "target" and an experimental three-dimensional structure of a related homologous protein (the "template").

Structure-based drug design SLIDE 18

Using the structure of the biological target, candidate drugs that are predicted to bind with high affinity and selectivity to the target may be designed using:

interactive graphics Intelligence of a medicinal chemist. various automated computational procedures may be

used to suggest new drug candidates.

Methods SLIDE 191) Virtual screening : The first method is identification of new ligands for a given receptor by searching large

databases of 3D structures of small molecules to find those fitting the binding pocket of the receptor using fast approximate docking programs.

2) de novo design of new ligands: In this method, ligand molecules are built up within the constraints of the binding pocket

by assembling small pieces in a stepwise manner. These pieces can be either individual atoms or molecular fragments. The key advantage of such a method is that novel structures can be suggested.

3) optimization of known ligands by evaluating proposed analogs within the binding cavity.

Binding site identification SLIDE 20 It is the first step in structure based design. relies on identification of concave surfaces

on the protein that can accommodate drug sized molecules that also possess appropriate "hot spots" (hydrophobic surfaces, hydrogen bonding sites, etc.) that drive ligand binding.

Docking & Scoring SLIDE 21 Docking attempts to find the “best”

matching between two molecules It includes finding the Right Key for the

Lock To place a ligand (small molecule) into the

binding site of a receptor in the manners appropriate for optimal interactions with a receptor.

To evaluate the ligand-receptor interactions in a way that may discriminate the experimentally observed mode from others and estimate the binding affinity.

Components of Docking SLIDE 22I- pre- and/or during docking: Representation of receptor binding site and ligand II- during docking: Sampling of configuration space of the ligand-

receptor complexIII- during docking and scoring: Evaluation of ligand-receptor interactions

Advantages of CADD SLIDE 23 Time Cost Accuracy information about the disease screening is reduced Database screening less manpower is required

Success stories of CADD SLIDE 24 K+ ion channel blocker structural based discovery

Ca2+ antagonist / T-channel blocker chemical descriptor based discovery

Success stories of CADD SLIDE 25Glyceraldehyde-phosphate DH inhibitors (anti-trypanosomatid drugs) combinatorial docking

Thrombin inhibitor docking, de-novo design

Computational Tools For Drug DesigningSection 02

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Categories of software SLIDE 27Databases & Draw Tools

Molecular Modeling & Homology Modeling

Binding site prediction & Docking

Ligand design Screening -QSAR

Binding free energy estimation

ADME Toxicity

Databases SLIDE 28

ZincDatabase, Zinc15Database ChEMBL JChemforExcel ProteinDataBank(PDB) BindingMOAD(MotherOfAllDatabase) PDBbind STITCH,SMPDB

Databases SLIDE 29

Databases SLIDE 30

Draw Tools SLIDE 31 ChemDraw MarvinSketch ACD/ChemSketch Marvin molecule editor and viewer ChemWriter UCSFChimera Pymol

Medchem SLIDE 32

SLIDE 33UCSF Chimera

SLIDE 34Chem Office

Molecular Modeling SLIDE 35 CHARMM GROMACS Amber SwissParam CHARMM-GUI CHARMMing.org SwissSideChain

Hyperchem SLIDE 36

Homology Modeling SLIDE 37 Modeller I-TASSER LOMETS SWISS-MODEL SWISS-MODELRepository Robetta

LOMETS SLIDE 38

Binding site prediction SLIDE 39 MED-SuMo CAVER FINDSITE sc-PDB Pocketome PocketAnnotatedatabase 3DLigandSite, metaPocket PocketAnnotate

CAVER SLIDE 40

Docking SLIDE 41 Autodock DOCK GOLD SwissDock DockingServer 1-ClickDocking iGemdock

iGemdock SLIDE 42

Screening SLIDE 43 Pharmer Catalyst PharmaGist SwissSimilarity Blaster AnchorQuery ligandscout Discovery Studio

Discovery Studio SLIDE 44

Target prediction SLIDE 45 MolScore-Antivirals MolScore-Antibiotics Swiss Target Prediction SEA ChemProt

Ligand design SLIDE 46 GANDI LUDI AutoT&T2 SwissBioisostere VAMMPIRE sc-PDB-Frag e-LEA3D eDesign iScreen

Binding free energy estimation SLIDE 47 Hyde, X-score NNScore DSXONLINE BAPPLserver BAPPL-Zserver,

QSAR SLIDE 48 cQSAR clogP ClogP/CMR MOLEdb ChemDB/Datasets OCHEM E-Dragon PatternMatchCounter avogadro

Avogadro SLIDE 49

ADME Toxicity SLIDE 50 VolSurf GastroPlus MedChemStudio ALOGPS OSIRISPropertyExplorer SwissADME Metrabase PACT-F, TOXNET

GastroPlus SLIDE 51

That’s all. Thank you very much! Any Questions?