Computational Chemistry in Drug Discovery 1

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  • Created by: LBCW0502
  • Created on: 16-10-19 14:30
What is a hit compound?
A chemical compound with biological activity in micromolar range
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What is a lead compound?
A chemical compound with biological activity in nanomolar range
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What is lead generation (hit to lead)? (1)
Stage in early drug discovery where a small molecule hits from a HTS are evaluated and undergo limited optimisation to identify promising lead compounds
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What is lead generation (hit to lead)? (2)
Lead compounds undergo more extensive optimisation in a subsequent step of drug discovery called lead optimisation
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What does in silico mean?
Applying computational algorithms and performing molecular modelling. For recognition, optimisation or designing of novel bioactive molecules (promising approaches to goal)
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State the steps in the drug discovery process
Target validation, assay development, HTS, hit to lead, lead optimisation, pre-clinical drug development, clinical drug development
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Describe features of biological targets (1)
Biomacromolecules - proteins, nucleic acids, carbohydrates, lipids. Proteins/nucleic acids are common targets for drug discovery. Proteins - primary/secondary/tertiary/quaternary structures. Important to have correct target structure
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Describe features of biological targets (2)
Nucleic acids have same level of folding, packaging, occupied space in living cells, sequence of nucleic acid used in computational chemistry
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Describe features of biological targets (3)
3D structure of targets - x,y,z co-ordinates for atoms in molecules, visualise structures
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Describe features of finding known structures
Search with protein and species names. Select and click on PDB ID code. Check the resolution and download the related paper. Download the PDB 3D structure
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Describe features of generating known structures (1)
Search with protein and species names then select and click Uniprot code. Check the structure section. Download FASTA sequence file. Homology modelling (PDB 3D file) - form 3D structure model from amino acid sequence, use algorithm. SWISS, TASSER
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Describe features of generating known structures (2)
If sequence identity between target and template is <20% - not acceptable
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Which softwares are used to generate ligand structures?
ChemDraw and Chem3D
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Describe the 1st generation rational approach in drug design
Consider molecules as topological entities in 2D with associated chemical properties. Quantitative Structure Activity/Property Relationship (QSAR/QPSR), implemented in computers
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Describe the 2nd generation rational approach in drug design
Acceptance of medicinal chemists of molecular modelling favoured by the fact that QSAR was supplemented by 3D visualisation
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Describe features of rational design/lead optimisation - case study (histamine) (1)
Rational drug design - validated target (known target for specific compound, interaction, shows biological effect). Start with validated biological target, end up with drug that optimally interacts with target, triggers desired biological action
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Describe features of rational design/lead optimisation - case study (histamine) (2)
Histamine triggers release of stomach acid. Need histamine antagonist to prevent stomach acid release by histamine. Histamine analogs were synthesised with systemically varied structures (chemical modification) and screened
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Describe features of rational design/lead optimisation - case study (histamine) (3)
N-guanyl-histamine showed some antagonist properties, lead compound (modified structure)
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State features of computer aided molecular/drug design
Target discovery (identification/validation). Lead discovery (hit identification, lead optimisation), saving time and money. Computational methods - obtain information which cannot be obtained from lab
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Describe features of screening
Experimentally (in vitro, HTS) or computationally (in silico, VS) - both lead to validated structure and selective ligands
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Describe features of virtual screening (1)
A computational technique used in drug discovery to search libraries (databases) of small molecules in order to identify those structures which are most likely to bind to drug target/hits
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Describe features of virtual screening (2)
Valuable tool in hit to lead optimisation. Hits are the output of VS, indicate activity may function as lead for consequent lead optimisation
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What are the two types of virtual screening?
Structure-based VS and ligand-based VS (pharmacophore modelling)
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What is ligand based pharmacophore modelling? (1)
Consider ligands with high selectivity to protein target. Create conformational space for each ligand to represent conformational flexibility. Alignment of ligands determine essential common chemical features to construct models
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What is ligand based pharmacophore modelling? (2)
Pharmacophore generation (predictive pharmacophore model). Pharmacophore features - portion of pharmacophore model which represents collection of certain type of functional groups
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What is ligand based pharmacophore modelling? (3)
Similar ligands, different functional groups (e.g. OH, NH), H bond donor (type of functional group, chemical feature), hydrophobic group, H bond acceptor. Binding site of target protein, ligand to bind to binding site
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What is ligand based pharmacophore modelling? (4)
Active site in enzyme, catalytic reaction e.g. folding of protein for particular function. Conformational space of ligand (ligand occupies binding site in particular orientations).
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What is ligand based pharmacophore modelling? (5)
More flexible ligand creates bigger conformational space in binding site (more favoured). Specific chemical features. Precise distance between groups – obtained from binding site, high affinity for binding site
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Describe features of ligand-based virtual screening (1)
Pharmacophore hypothesis taken as template using pharmacophore modelling. Pharmacophore - molecular framework which carries essential features which are responsible for drug's biological activity
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Describe features of ligand-based virtual screening (2)
Investigate whether a compound available in 3D database can adopt conformation consistent with pharmacophore or not
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Describe features of structure-based virtual screening (1)
Computational approach used in drug discovery to search a chemical compound library for novel bioactive molecules against a certain drug target by considering the microenvironment of binding site/active site and key residues
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Describe features of structure-based virtual screening (2)
Used when there are no ligands available, need to find hits for target, screen library, determine which compounds bind to target and form stable complex
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What are the materials needed for performing VS?
A database of compounds. Query. Mathematical model. Pharmacophore model. Active compound. Receptor (docking approach)
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Describe features of molecular docking
Key tool in structural molecular biology and computer assisted drug design. Predict predominant binding modes of ligand with protein of known 3D structure. Binding mode - orientation/conformation or pose of ligand within receptor
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Describe features of prediction by molecular docking (1)
Predict binding affinity/pose. Sampling - identify correct pose of molecule. Explore conformational space of small molecule. Increase flexibility of ligand/receptor, increase complexity of sampling exponentially. Systemic/stochastic search
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Describe features of prediction by molecular docking (2)
Scoring - correctly rank/select correct pose. Accurately represents/predicts ligand protein interactions, binding affinity prediction, pose prediction, guide sampling, rank sampled poses, discrimination of binders/non-binders
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What is scoring function? (1)
A mathematical predictive model that produces a score that represents energy and stability of docked complex. (Equation). Optimise pose prediction and affinity prediction. Steric terms, H bond term, hydrophobic term, torsion count factor
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What is scoring function? (2)
More negative energy, more favourable complex (system with lower energy). Negative value for affinity, higher score
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Describe features of fracture based drug design
An approach to identify hits. Identify very small molecules (half the size of typical drugs). Fragments expanded or linked together to generate drug leads. Search for collections of smaller molecules and grow fragment or combine 2+
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What qualifies compounds to be considered as fragments? (1)
Small organic molecules. Low affinity (100 microns - 10 mM) for binding to target. Diversity of physicochemical properties, molecular diversity. Good ligand efficiency (free energy of binding). Aqueous solubility
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What qualifies compounds to be considered as fragments? (2)
Rule of three - Mwt <300 Da, H bond donors <3, H bond acceptors <3
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What is the rule of 5 for hit or lead molecules?
Mwt <300 Da, H bond donors and H bond acceptors <3, calculated log P <3, number of rotatable bonds <3, polar surface area <60 A^2 (measure of permeability through cell membrane)
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What are the steps in fragment-based computational drug design?
Design of good fragment library. Computational docking, ranking, screening of fragments within library. Growing, linking or combining of fragments to yield lead compounds (e.g. ring system from drug, heterocyclic system, side chains)
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